Cancer symptom deterioration notification system, cancer symptom deterioration notification method, and cancer symptom deterioration notification program

Abstract

Provided is a cancer symptom deterioration notification system that notifies a cancer patient of the deterioration of the cancer symptoms thereof.　The system comprises a user information terminal that is used by a user and a server device that can communicate with the user information terminal. The server device comprises a reception unit that receives user input of information that indicates the extent of various symptoms, a symptom score aggregation unit that aggregates the received information, a symptom degree of change determination unit that determines the degree of change in the symptoms on the basis of the aggregated information, a symptom deterioration situation determination unit that determines the deterioration of the symptoms on the basis of the determined degree of change in the symptoms, and a notification unit that notifies the user of the determined deterioration of the symptoms.

Description

Cancer symptom worsening degree notification system, cancer symptom worsening degree notification method, and cancer symptom worsening degree notification program

[0001] The present invention relates to a cancer symptom worsening notification system, a cancer symptom worsening notification method, and a cancer symptom worsening notification program, which are suitable for notifying cancer patients of information regarding the progression of the disease and presenting information encouraging them to consult or be examined at a medical institution.

[0002] Generally, if you are not feeling well or have concerns about your physical or mental condition, you will consult a medical institution such as a hospital or clinic. Methods of consultation include contacting the above medical institution by phone, email, or text message via an inquiry form on the medical institution's website, or receiving a medical examination.

[0003] This type of consultation is also applicable to malignant tumors (hereafter referred to as "cancer"), and the appropriate treatment is selected based on the judgment of a specialist, for example, on the stage of the cancer, the treatment the patient is receiving or has received in the past (not limited to cancer), medical history, complications, and the patient's wishes.

[0004] Even if cancer patients experience changes in their physical condition, they may decide that the condition is not serious enough to warrant a medical consultation, or that going to a medical institution would be a burden to the doctor, and so they may not consult a medical institution. In such cases, the cancer may progress more than expected by the time of the next consultation, which could result in delayed treatment of symptoms caused by the cancer itself or cancer treatment, or the development of other complications.

[0005] The following Patent Document 1 discloses a system technology that presents medical professionals with information that is useful for determining whether or not a patient needs to be examined, based on the amount of change in data related to past examinations. This allows medical professionals to consider whether or not to examine the patient.

[0006] JP 2018-41418 A

[0007] However, the technology disclosed in the above-mentioned Patent Document 1 still has the problem that the patient cannot fully grasp his or her own condition and cannot determine whether or not the patient is in a condition that requires immediate medical attention.

[0008] In order to solve the above problem, one aspect of the present invention provides a cancer symptom worsening notification system that includes a user information terminal used by a user and a server device that is capable of communicating with the user information terminal, and the server device includes a reception unit that receives user input of information indicating the degree of symptom severity for various symptoms, a symptom score aggregation unit that aggregates the received information, a symptom fluctuation degree determination unit that determines the degree of symptom fluctuation based on the aggregated information, a symptom worsening degree determination unit that determines the degree of symptom worsening based on the determined degree of symptom fluctuation, and a notification unit that notifies the user of the determined degree of symptom worsening.

[0009] In order to solve the above problem, in one embodiment of the cancer symptom worsening notification system of the present invention, the notification unit may present recommendation information to the user recommending at least one of medical treatment and self-care based on the determined degree of symptom worsening.

[0010] In order to solve the above problem, in one embodiment of the present invention, a cancer symptom worsening notification system may include a server device that includes a progression prediction unit that predicts the progression of cancer based on the determined degree of symptom worsening, and a notification unit that may present recommendation information to the user that recommends at least one of visiting a doctor or self-care based on the predicted progression of cancer.

[0011] In order to solve the above problem, in one embodiment of the cancer symptom worsening notification system of the present invention, the progression prediction unit may predict the progression of a cancer selected from a group of solid cancers including colorectal cancer, gastric cancer, lung cancer, and breast cancer.

[0012] In order to solve the above problem, in a cancer symptom worsening notification system according to one embodiment of the present invention, the user's information terminal may be equipped with an application execution unit that executes an application received from a server device, and the application that is executed may display the cancer progression and recommendation information.

[0013] In order to solve the above problem, a cancer symptom worsening notification system according to one embodiment of the present invention further includes a syndrome prediction unit that predicts a corresponding syndrome based on the determined symptom worsening, and the progression prediction unit may predict the progression of the cancer based on the predicted syndrome.

[0014] In order to solve the above problem, in a cancer symptom worsening notification system according to one embodiment of the present invention, the symptom fluctuation degree determination unit may further determine the degree of symptom fluctuation based on information regarding the user's cancer history and history of use of anticancer drugs.

[0015] In order to solve the above problem, in a cancer symptom worsening notification system according to one aspect of the present invention, the progression prediction unit may predict the progression of cancer based on the results of image diagnosis.

[0016] In order to solve the above problem, in a cancer symptom worsening notification system according to one aspect of the present invention, the progression prediction unit may predict the possibility of cancer metastasis.

[0017] In order to solve the above problem, a cancer symptom worsening notification system according to one embodiment of the present invention may further include an information terminal of a medical professional at the medical institution where the user receives medical services, which is capable of communicating with the server device, and the notification unit may present information about the user on the medical professional's information terminal based on the worsening of the symptoms.

[0018] In order to solve the above problem, a cancer symptom worsening notification system according to one embodiment of the present invention may further include an information terminal of a medical professional at the medical institution where the user receives medical services, which is capable of communicating with the server device, and the notification unit may present information about the user on the medical professional's information terminal based on the progression of the cancer.

[0019] In order to solve the above problem, in one embodiment of the present invention, a cancer symptom worsening notification system includes a receiving unit that receives input of feedback information from a user, and a notification unit that modifies recommendation information based on the received feedback information and presents the modified recommendation information to the user.

[0020] In order to solve the above problem, in one embodiment of the cancer symptom worsening notification system of the present invention, the notification unit may modify recommendation information based on information on the user's accompanying symptoms, drug classification, combination, dosage, administration time, administration method, administration order, and administration date, and present the modified recommendation information to the user.

[0021] In order to solve the above problem, one aspect of the present invention provides a method for notifying the degree of worsening of cancer symptoms, the method including a computer executing a reception step in which a user inputs information indicating the degree of symptom severity for various symptoms, a symptom score aggregation step in which the received information is aggregated, a symptom fluctuation degree determination step in which the degree of symptom fluctuation is determined based on the aggregated information, a symptom worsening degree determination step in which the degree of symptom worsening is determined based on the degree of symptom fluctuation, and a notification step in which the determined degree of symptom worsening is notified to the user.

[0022] In order to solve the above problem, a method for notifying the degree of worsening of cancer symptoms according to one embodiment of the present invention may include a progression prediction step in which a computer predicts the degree of cancer progression based on the determined degree of symptom worsening, and a notification step in which a computer notifies a user of recommendation information recommending at least one of medical treatment and self-care based on the predicted degree of cancer progression.

[0023] In order to solve the above problem, a cancer symptom worsening notification program according to one embodiment of the present invention provides a computer with the following functions: a reception function for receiving user input of information indicating the degree of symptom severity for various symptoms; a symptom score aggregation function for aggregating the received information; a symptom fluctuation degree determination function for determining the degree of symptom fluctuation based on the aggregated information; a symptom worsening degree determination function for determining the degree of symptom worsening based on the degree of symptom fluctuation; and a notification function for notifying the user of the determined degree of symptom worsening.

[0024] In order to solve the above problem, a cancer symptom worsening notification program according to one embodiment of the present invention may be implemented in a computer with a progression prediction function that predicts the progression of cancer based on the determined degree of symptom worsening, and a notification function that notifies the user of recommendation information recommending at least one of medical examination and self-care based on the predicted progression of cancer.

[0025] The present invention provides a system that notifies cancer patients of the degree of worsening of their cancer symptoms, allowing them to understand the degree of worsening of their cancer symptoms and making it easier for them to determine whether they are in a condition that requires immediate medical treatment.

[0026] 1 is a block diagram showing the functional configuration of a cancer progression stage-related information notification system.

[0023] FIG. 1 is a diagram showing a main flow relating to the processing of the cancer progression stage-related information notification system.

[0024] FIG. 2 is a diagram showing a sub-flow relating to the processing of the cancer progression stage-related information notification system.

[0025] FIG. 3 is a sequence diagram showing data exchange between a server, a user terminal, and a medical professional terminal.

[0026] FIG. 4 is a diagram showing an example of a questionnaire for scoring the degree of a user's symptoms.

[0027] FIG. 5 is a diagram showing the form of symptom fluctuation for determining the degree of symptom fluctuation based on aggregated information.

[0028] FIG. 6 is a diagram showing a syndrome matrix for predicting a relevant syndrome.

[0029] FIG. 7(a) is a diagram showing a matrix for predicting cancer and the cancer progression stage based on the relevant syndrome and the degree of deterioration.

[0029] FIG. 7(b) is a diagram showing a matrix for determining the recommendation strength of recommendation information to be presented to a user based on the relevant syndrome and the degree of deterioration.

[0029] FIG. 8(b) is a diagram showing a data structure correlating consultation recommendation strength with consultation recommendation information to be presented to a user.

[0029] FIG. 9(c) is a diagram showing a data structure correlating self-care recommendation strength with self-care recommendation information to be presented to a user. 1A is a diagram showing an example of a screen on a user terminal on which recommendation information is presented. 1B is a diagram showing an example of a screen on a medical staff terminal on which information on the probability of hospitalization is presented. 1C is a diagram showing a data structure in which the date and time when user input of user name and symptom information was accepted, user ID, and management ID are associated. 1D is a diagram showing an example of an evaluation table of feedback from the system presented to the user after recommendation information is presented. 1D is a diagram showing a matrix for correcting the next recommendation strength based on feedback from the user.

[0027] A cancer progression-related information notification system 100 (cancer symptom worsening notification system) according to an embodiment of the present invention will be described below with reference to the drawings. The cancer progression-related information includes progression information indicating the type of cancer and the stage of cancer (sometimes simply referred to as "cancer progression" or "cancer progression level" in this specification), recommendation information recommending at least one of medical examination and self-care based on the stage of cancer, etc. Details of this information will be described later.

[0028] 1 shows the main configuration of a server device 101, a user's information terminal 102, and a medical professional's information terminal 103 in an embodiment of a cancer progression-related information notification system 100. The server device 101 in the cancer progression-related information notification system 100 includes a reception unit 110, a communication unit 120, a control unit 130, a notification unit 140, and a storage unit 150. The server device 101 is computer hardware including a processor and a memory.

[0029] The cancer progression-related information notification system 100 acquires information about a cancer patient's symptoms, determines the degree of symptom fluctuation and symptom worsening, predicts the stage of cancer, notifies the cancer patient of information about the predicted stage of cancer, and presents recommendation information based on the stage of cancer. The cancer progression-related information notification system 100 may also be a system that presents information about the user to a medical professional, such as the probability that the user will come for a medical examination, the probability of hospitalization, recommendation information, the stage of symptom worsening, and the predicted cancer and stage of cancer. The cancer progression-related information notification system 100 connects a server device 101, a user's information terminal 102, and a medical professional's information terminal 103 via a communication network. In the cancer progression-related information notification system 100, each functional unit is connected via a communication line. Here, the connection may be wired or wireless.

[0030] The user may be, for example, a cancer patient. More specifically, the user may be a patient currently undergoing cancer treatment. Progression information refers to information related to the progression of cancer. For example, the progression information may include information indicating the stage of cancer and other text information indicating whether the cancer has progressed. Progression information may be text information such as "Stage I" or text information such as "Your cancer may be more advanced than last time." However, the progression information is not limited to these and may be any information that allows cancer patients to understand the progression of their cancer. Recommendation information includes at least one of information recommending a medical examination (medical examination recommendation information) and information recommending self-care (self-care recommendation information). Recommending a medical examination includes recommending the user to consult a medical institution. More specifically, recommending the user to call a nearby medical institution or their family doctor or to receive a medical examination regarding the user's condition. Recommending self-care includes recommending self-care to the user. More specifically, the system recommends appropriate self-care to the user, selected from pre-registered self-care methods based on the user's own condition. Furthermore, symptom fluctuations include the system objectively recognizing some kind of change in the user's symptoms. More specifically, for example, if the user experiences abdominal pain, the system recognizes that the abdominal pain is getting worse over time. Here, symptom fluctuations also include symptom relief. Furthermore, the degree of worsening refers to the degree of worsening of the user's symptoms. More specifically, in the case of abdominal pain, the degree of pain increase based on the information processing of the cancer progression stage related information notification system 100 is included. In this case, the degree of worsening can be expressed by a preset evaluation item, as described below. Furthermore, the cancer progression stage refers to the progression of the cancer the patient is suffering from, and more specifically, includes the cancer stage. Furthermore, the cancer progression stage includes information regarding the progression of the cancer.More specifically, for example, the information includes the stage of cancer determined based on the TNM classification, which is an international standard, and information regarding the degree and rate of progression of cancer determined based on standards independently set by the cancer progression related information notification system 100. In this specification, the patient, the medical professional, or both may be simply referred to as the "user."

[0031] The communication unit 120 is a communication interface that communicates with external devices via a network. The communication unit 120 communicates with, for example, the user's information terminal 102 and the medical professional's information terminal 103. More specifically, the communication unit 120 transmits an application of the cancer progression-related information notification system 100 to the user's information terminal 102 and the medical professional's information terminal 103, transmits progression information and recommendation information to the user's information terminal 102, and transmits information about the user, such as the user's probability of visiting a hospital or being hospitalized, recommendation information, the degree of symptom worsening, predicted cancer and cancer progression, to the medical professional's information terminal 103.

[0032] The reception unit 110 receives user input of information indicating the severity of symptoms transmitted from the user's information terminal 102. For example, the reception unit 110 receives information indicating scores for the severity of various symptoms experienced by the user. A method for scoring the severity of various symptoms experienced by the user includes the user subjectively assigning a score to the severity of the symptoms using a medical questionnaire, as described below. Furthermore, the information input by the user and received by the reception unit 110 is not limited to the above and may include, for example, numerical information indicating the user's body temperature, weight, bowel movement frequency, etc., or information indicating tests performed inside or outside a medical institution. The user input is performed, for example, from the user's information terminal 102 or a PC. For example, the input method may be input by touching the screen in the case of the user's information terminal 102, or input by clicking a mouse in the case of a PC. For example, the input format may be a medical questionnaire commonly used in medical institutions. However, the format is not limited to this. For example, the format may be a table in which scores correspond to the severity of symptoms. The information received by the receiving unit 110 is transmitted to the symptom score tallying unit 130a of the control unit 130. The symptom score refers to information that indicates the severity of the various symptoms of the user described above, converted into a score. In addition to the information described above, the information received by the receiving unit 110 includes, for example, the user name and the date and time when the input of information was received. Note that the information indicating the severity of the user's symptoms may include, for example, text information, audio information, image information, video information, etc., in addition to the scored information described above.

[0033] The control unit 130 is an internal CPU of the server device 101 in the cancer progression-related information notification system 100, and controls the processing of each functional unit. As shown in Fig. 1 , the control unit 130 includes a symptom score tallying unit 130a, a symptom fluctuation degree determining unit 130b, a symptom worsening state determining unit 130c, a syndrome prediction unit 130d, and a progression prediction unit 130e. The control unit 130 can realize various functions to be realized using various programs and data stored in the memory unit 150 of the cancer progression-related information notification system 100.

[0034] The symptom score tallying unit 130a tallys the information received by the receiving unit 110. Here, tallying the information refers to calculating a total value based on the scored information. For example, the tallying information can be stored in a storage unit together with the user name, the date and time when the input of the information was received, and the like. The information tallying the symptom score tallying unit 130a is transmitted to the symptom fluctuation degree determining unit 130b.

[0035] The symptom fluctuation degree determining unit 130b determines the degree of symptom fluctuation of the user based on the information compiled by the symptom score collecting unit 130a. One example of a method for determining the degree of symptom fluctuation is, for example, creating a graph plotting information on symptoms converted into scores for each day. The degree of symptom fluctuation may be determined based on the amount of increase or decrease in the numerical values ​​in the graph over a predetermined period, but this is not limited to this. For example, the symptom fluctuation degree determining unit 130b may calculate the amount of increase or decrease without using a graph to determine the degree of symptom fluctuation. However, the method for determining the degree of symptom fluctuation is not limited to this. For example, the degree of symptom fluctuation may be determined using a graph plotting information converted into scores for each week. Information on the degree of symptom fluctuation determined by the symptom fluctuation degree determining unit 130b is transmitted to the symptom worsening degree determining unit 130c.

[0036] Furthermore, for example, after calculating the total score representing the symptom severity for each day, the degree of symptom fluctuation can be determined based on the difference between the total scores for any two days. More specifically, assume that the total symptom severity based on the user's subjective assessment of various symptoms on one day is 60, and the total score for the following day is 70. Here, it is assumed that the system can determine that there is symptom fluctuation if the difference exceeds 8. In this case, the difference between the total scores for the two days is 10, and it is determined that there is symptom fluctuation. However, the method for determining the degree of symptom fluctuation is not limited to this.

[0037] The symptom worsening degree determining unit 130c determines the degree of symptom worsening based on the information on the degree of symptom fluctuation determined by the symptom fluctuation degree determining unit 130b. For example, among the information determined to have a symptom fluctuating, the symptom worsening degree determining unit 130c may use the above-mentioned graph to calculate a slope value based on the symptom type and total value over a certain period based on the information on the symptom type and total value, and determine the degree of symptom worsening based on whether the slope value exceeds a predetermined threshold. For example, when determining the degree of symptom worsening, different predetermined threshold values ​​may be used for abdominal pain and headache. The information on the degree of symptom worsening determined by the symptom worsening degree determining unit 130c is transmitted to the progression prediction unit 130e.

[0038] The progression prediction unit 130e predicts the progression of cancer based on the information on the symptom worsening level determined by the symptom worsening level determination unit 130c. Here, the progression prediction unit 130e may predict the type of cancer the user is thought to be suffering from and the progression of that cancer, in addition to the input cancer. Furthermore, the progression prediction unit 130e can predict the possibility of cancer metastasis based on symptom information from the user. Here, if a cancer patient inputs information on the cancer they have been diagnosed with, the progression prediction unit 130e predicts the possibility of cancer metastasis based on the input cancer information. The progression prediction unit 130e transmits the cancer progression information, information on the possibility of cancer metastasis, and other information predicted by the progression prediction unit 130e to the notification unit 140. Based on the predicted cancer progression and the information on the possibility of metastasis, the notification unit 140 can extract recommendation information from the storage unit 150.

[0039] The progression prediction unit 130e may also predict the progression of cancer based on the results of imaging diagnosis (imaging diagnosis results) selected from X-ray examinations, CT examinations, MRI examinations, ultrasound examinations, PET examinations, nuclear medicine examinations, and even PET-CT and SPECT-CT. For example, the progression prediction unit 130e may divide the results of imaging diagnosis into several scored evaluations in advance. Using the divided evaluations, the progression prediction unit 130e may predict the progression of cancer using a matrix that associates the degree of symptom worsening with scored information regarding the results of imaging diagnosis. The progression prediction unit 130e may also predict the progression of cancer based on information regarding the user's accompanying symptoms, regimen, and age. A regimen refers to a plan for actually administering anticancer drugs to a cancer patient. More specifically, a regimen includes information such as drug classification, combination, dosage, administration time, administration method, administration order, and administration date. Furthermore, if the user is already undergoing cancer treatment, the progression prediction unit 130e may predict the progression of cancer based on information regarding the type of anticancer drug being used or the combination of anticancer drugs if multiple anticancer drugs are being used in combination. The information regarding the accompanying symptoms, regimen, user's age, and anticancer drugs may be input together with the user's input of a score indicating the severity of the symptoms via the input unit 102c (described below), or may be input by a medical professional via the input unit 103c (described below). This allows the progression prediction unit 130e to dynamically change the predicted progression of cancer. This also allows the notification unit 140 to dynamically change the recommendation strength of the recommendation information presented to the user based on the user's accompanying symptoms, regimen, and age. Here, the information used in the decision process by the control unit 130 may necessarily include information regarding essential symptoms shown in the matrix 700 (described below). Furthermore, when dynamically changing the stage of cancer or the recommendation strength of recommendation information, the stage prediction unit 130e and the notification unit 140 may use the Bayes method.

[0040] Here, the server device 101 can connect to the information terminal 103 of a medical professional via a network from the communication unit 120, for example, and receive information related to the past imaging diagnosis of a cancer patient. The received information related to the imaging diagnosis is accepted by the accepting unit 110. The accepted information is transmitted to the control unit 130.

[0041] One method for predicting the stage of cancer is to use a matrix that predicts the stage of cancer based on the type of symptoms the user is experiencing and the degree of worsening. More specifically, if the user has abdominal pain and the degree of worsening is determined to be slight, a matrix table can be used that classifies abdominal pain and slight worsening as stage I stomach cancer. Information about the stage of cancer predicted by the stage prediction unit 130e is transmitted to the notification unit 140.

[0042] The control unit 130 may further include a syndrome prediction unit 130d that predicts a corresponding syndrome. For example, the syndrome that is thought to be the cause of the user's symptoms is predicted based on the degree of symptom worsening determined by the symptom worsening degree determination unit 130c. At this time, information on the syndrome predicted by the syndrome prediction unit 130d is transmitted to the progression prediction unit 130e. The progression prediction unit 130e can predict the degree of cancer progression based on the information on the syndrome predicted by the syndrome prediction unit 130d. Here, a syndrome refers to, for example, a series of symptoms that occur simultaneously. Specifically, these include stroke, cardiovascular events, acute abdominal pain, severe skin disease, spinal cord compression, mucocutaneous disorders, peripheral neuropathy, renal failure, neuromuscular disease, jaundice, decreased PS, central nervous system disorders, metabolic disturbances of consciousness, inability to drink orally, severe diarrhea, cardiac failure, respiratory failure, severe allergies, bloodstream infections, acute organ infections, gastrointestinal bleeding, hemoptysis, difficulty in hemostasis, venous thrombosis, cardiotoxicity, electrolyte abnormalities, drug-induced lung disorders, acute visual impairment, and acute hearing impairment.

[0043] The server device 101 of the cancer progression-related information notification system 100 may be provided with a selection unit for selecting which of the above-mentioned processes related to symptom score aggregation, symptom fluctuation determination, symptom worsening determination, and cancer progression prediction to execute, or may be able to select and discard each process. For example, the storage unit 150 has a function for storing various programs, data, parameters, etc. required for the operation of the cancer progression-related information notification system 100. Specifically, the storage unit 150 is composed of, for example, a main storage device composed of ROM and RAM, an auxiliary storage device composed of non-volatile memory, etc., and various recording media such as an HDD (Hard Disc Drive), an SSD (Solid State Drive), and a flash memory. The storage unit 150 may store information compiled by the symptom score compilation unit 130a, information about the predicted progression of the user's cancer, matrix information described later, recommendation information to be presented to the user by the notification unit 140, recommendation information previously presented to the user and information about the date and time when the information was presented to the user, the user ID of the user who presented the information, a management ID for managing the user ID, information about feedback from the user, etc. Details of the notification unit 140 will be described later.

[0044] The notification unit 140 notifies the user's information terminal 102 of progression information, recommendation information, etc. The notification unit 140 extracts recommendation information from the storage unit 150 of the server device 101 based on progression information indicating the predicted progression of cancer transmitted from the progression prediction unit 130e. The notification unit 140 can also modify the recommendation strength of the recommendation information based on feedback information (described below) received from the user's information terminal 102, and notify the user's information terminal 102 of the modified recommendation information again. This allows the system to notify more appropriate recommendation information taking into account the user's physical condition, etc. Note that the progression information and recommendation information may be notified not only to the user's information terminal 102, but also to the medical professional's information terminal 103 (described below).

[0045] 1 , the user's information terminal 102 includes a communication unit 102a, a reception unit 102b, an input unit 102c, a control unit 102d, and a storage unit 102f. The control unit 102d includes an application execution unit 102e. The communication unit 120 of the server device 101 transmits an application related to the cancer progression-related information notification system 100 to the communication unit 102a of the user's information terminal 102. When the user's information terminal 102 receives the application, the application execution unit 102e executes the application.

[0046] The reception unit 102b receives an application transmitted from the server device 101. The reception unit 102b may also receive input from the input unit 102c and transmit the input to the control unit 102d. The reception unit 102b also receives progress information and recommendation information transmitted from the server device 101. The information received by the reception unit 102b is transmitted to the control unit 102d.

[0047] The input unit 102c is an input interface that has the function of accepting input from the user of the user's information terminal 102 and transmitting it to the control unit 102d. More specifically, the input unit 102c is an input interface for inputting information indicating the severity of symptoms as a score in an application related to the cancer progression-related information notification system 100. The input unit 102c may be implemented by soft keys such as a touch panel or hard keys. Alternatively, the input unit 102c may be a microphone for accepting voice input. The input unit 102c transmits the input content accepted by the user to the control unit 102d. The input unit 102c may, for example, accept instructions from the user for using the application related to the cancer progression-related information notification system 100 and transmit the instructions to the control unit 102d. The input unit 102c may also, for example, accept input of responses to a medical interview conducted by the application related to the cancer progression-related information notification system 100 and transmit the instructions to the control unit 102d. The input unit 102c may also accept input of information requesting the server device 101 to transmit an application related to the cancer progression stage-related information notification system 100. As a result, information requesting the application is transmitted to the server device 101.

[0048] The control unit 102d is a processor having a function of controlling each unit of the user's information terminal 102. The control unit 102d may be realized by a single core or a multi-core. The control unit 102d transmits request information for using an application related to the cancer progression stage related information notification system 100 to the server device 101 via the communication unit 102a. The control unit 102d also includes an application execution unit 102e as a function realized by the control unit 102d.

[0049] The application execution unit 102e executes an application related to the cancer progression-related information notification system 100 downloaded from the server device 101. The application execution unit 102e executes the application related to the cancer progression-related information notification system 100, registers various information in the application related to the cancer progression-related information notification system 100 according to input from the user, and acquires and displays progression information and recommendation information from the server device 101. The application execution unit 102e may also have the functions of the symptom score aggregation unit 130a, symptom fluctuation degree determination unit 130b, symptom worsening state determination unit 130c, syndrome prediction unit 130d, progression prediction unit 130e, and notification unit 140 as described above. That is, the user's information terminal 102 may accept input of information indicating the degree of symptoms by the user, and display progression information indicating the degree of cancer progression and recommendation information on the screen according to the processing described above and the processing flow described below. In this case, the application execution unit 102e may further include information necessary for predicting the stage of cancer (correspondence information between the stage of cancer and representative symptoms, syndromes, and progression, etc., as shown in a matrix described below) and information necessary for notifying the recommendation information (correspondence information between the type of cancer, the predicted stage of cancer, and recommendation information, as shown in a matrix described below). This allows the user's information terminal 102 to perform the above-described series of processes, thereby enabling the stage of cancer information indicating the stage of cancer and recommendation information to be displayed on the screen. Furthermore, the application execution unit 102e may be capable of notifying the user of recommendation information whose strength of recommendation to be notified has been modified based on feedback information from the user. In this case, the application execution unit 102e may further include correspondence information between the feedback information from the user, as described below, and information regarding modification of recommendation strength, thereby enabling the application execution unit 102e to notify the user of recommendation information whose strength of recommendation to be notified has been modified based on feedback information from the user.

[0050] The storage unit 102f has a function of storing various programs and data required for the operation of the user's information terminal 102. The storage unit 102f can be realized, for example, by a hard disk drive (HDD), a solid state drive (SSD), or a flash memory. The storage unit 102f stores applications received from the server device 101. After receiving the application, the application execution unit 102e executes the application stored in the storage unit 102f. The storage unit 102f also stores an ID used to log in to the cancer progression-related information notification system 100 when the application is executed, as well as progression information and recommendation information presented by the server device 101.

[0051] Information input from the input unit 102c of the user's information terminal 102 is transmitted to the reception unit 110 of the server device 101. The reception unit 110 of the server device 101 receives information from the user's information terminal 102. The received information is transmitted to the control unit 130. The control unit 130 executes a processing flow, which will be described later, based on the content of the received information, and transmits the information to the notification unit 140. The notification unit 140 extracts recommendation information corresponding to the predicted cancer progression level from the storage unit 150 through the processing flow, and transmits the extracted recommendation information to the user's information terminal 102. This allows the user to obtain recommendation information by transmitting information in which the severity of symptoms is scored.

[0052] 1 , the medical worker's information terminal 103 includes a communication unit 103a, a reception unit 103b, an input unit 103c, a control unit 103d, and a storage unit 103f. The control unit 103d includes an application execution unit 103e. The communication unit 120 of the server device 101 transmits an application related to the cancer progression stage related information notification system 100 from the communication unit 120 of the server device 101 to the communication unit 103a of the medical worker's information terminal 103. When the medical worker's information terminal 103 receives the application, the application execution unit 103e executes the application.

[0053] The reception unit 103b receives applications transmitted from the server device 101. The reception unit 103b may also receive input from the input unit 103c and transmit the input to the control unit 103d. The reception unit 103b also receives information transmitted from the server device 101, such as the user's probability of visiting the hospital or being hospitalized, recommendation information, the degree of symptom worsening, predicted cancer and cancer progression, etc. The information received by the reception unit 103b is transmitted to the control unit 103d.

[0054] The input unit 103c is an input interface that receives input from the medical professional via the medical professional's information terminal 103 and transmits the input to the control unit 103d. More specifically, the input unit 103c is an input interface for inputting information in an application related to the cancer progression-related information notification system 100. For example, the input information includes information regarding the results of the user's past imaging diagnosis. As described above, imaging diagnosis includes imaging diagnoses selected from the group consisting of X-ray examinations, CT examinations, MRI examinations, ultrasound examinations, PET examinations, and nuclear medicine examinations. The input unit 103c may be implemented by soft keys such as a touch panel or hard keys. Alternatively, the input unit 103c may be a microphone for receiving voice input. The input unit 103c transmits the input received from the medical professional to the control unit 103d. The input unit 103c may, for example, receive an instruction from a medical professional to use an application related to the cancer progression stage-related information notification system 100 and transmit the instruction to the control unit 103d. The input unit 102c may also receive input of information requesting the server device 101 to transmit an application related to the cancer progression stage-related information notification system 100. As a result, information requesting the application is transmitted to the server device 101.

[0055] The control unit 103d is a processor having a function of controlling each unit of the medical professional's information terminal 103. The control unit 103d may be realized by a single core or a multi-core. The control unit 103d transmits request information for using an application related to the cancer progression stage related information notification system 100 to the server device 101 via the communication unit 103a.

[0056] The application execution unit 103e executes an application related to the cancer progression stage related information notification system 100 downloaded from the server device 101. The application execution unit 103e executes the application related to the cancer progression stage related information notification system 100, and acquires and displays information about the user, such as the calculated probability of the user visiting the hospital or the probability of being hospitalized, the recommendation information, the degree of symptom worsening, the predicted cancer and the cancer progression stage, based on the progression stage information and recommendation information received from the server device 101.

[0057] The storage unit 103f has the function of storing various programs and data required for the operation of the medical professional's information terminal 103. The storage unit 103f can be realized, for example, by a hard disk drive (HDD), a solid state drive (SSD), or a flash memory. The storage unit 103f stores received applications, and after reception, the application execution unit 103e executes the applications stored in the storage unit 103f. The storage unit 103f also stores user information such as an ID for logging into the cancer progression-related information notification system 100 when the application is executed, the probability of hospital visit and hospitalization presented by the server device 101, recommendation information, symptom deterioration, predicted cancer and cancer progression, etc. When the server device 101 transmits recommendation information to the user's information terminal 102, the server device 101 transmits information such as the user's probability of hospital visit and hospitalization calculated based on the recommendation information to the medical professional's information terminal 103. This allows medical professionals to obtain information about the user, such as the probability of the user visiting the hospital or being admitted, as well as recommendation information, the degree of symptom worsening, predicted cancer and cancer progression.

[0058] It should be noted that the server device 101 is not limited to distributing applications to the user's information terminal 102 or the medical professional's information terminal 103. For example, a separate device for distributing applications may be provided. In this case, the control unit 130 of the server device 101 may determine whether or not the application may be distributed to the distribution server device 104 by the user's information terminal 102 or the medical professional's information terminal 103, and may control the distribution function of the distribution server device 104. For example, the distribution server device 104 stores applications related to the cancer progression-related information notification system 100 specified by the server device 101, and functions as a download server that distributes applications in response to requests from the user's information terminal 102 or the medical professional's information terminal 103.

[0059] 1 is a block diagram showing the functions and configuration of a distribution server device 104. As shown in FIG. 1, the distribution server device 104 includes a communication unit 104a, an input unit 104b, a control unit 104c, a storage unit 104d, and a distribution unit 104e.

[0060] The communication unit 104a is a communication interface having a function for communicating with other devices. The communication unit 104a may communicate with other devices using any communication protocol, and may communicate via either wired or wireless means, as long as the communication unit 104a is capable of communicating with other devices. The communication unit 104a communicates with the server device 101 and the user's information terminal 102 in accordance with instructions from the control unit 104c.

[0061] The input unit 104b is an input interface that receives input from an operator of the distribution server device 104 and transmits it to the control unit 104c. The input unit 104b may be implemented by soft keys such as a touch panel, or by hard keys. Alternatively, the input unit 104b may be a microphone for receiving voice input.

[0062] The control unit 104c is a processor having a function of controlling each unit of the delivery server device 104. The control unit 104c may be realized by a single core or by multiple cores.

[0063] The control unit 104c may include a distribution unit 104e as a function realized by the control unit 104c.

[0064] The distribution unit 104e identifies the installer of the application indicated in the request information transmitted from the communication unit 104a, reads it from the storage unit 104d, and transmits the read installer of the application via the communication unit 104a to the information terminal 102 of the user who sent the request information and the information terminal 103 of the medical worker.

[0065] The storage unit 104d has a function of storing various programs and data required for the operation of the distribution server device 104. The storage unit 104d can be realized, for example, by a hard disk drive (HDD), a solid state drive (SSD), flash memory, etc. The storage unit 104d stores all applications (installers) requested by the server device 101.

[0066] The distribution unit 104e has a function of distributing the designated application to the information terminal 102 of the user who transmitted the request information and the information terminal 103 of the medical worker in accordance with instructions from the control unit 104c.

[0067] The above is a description of an example of the configuration of the distribution server device 104.

[0068] The server device 101 may store patient information in advance.

[0069] Patient information includes, for example, prescription information at the time of the patient's visit. For example, prescription information includes information on the prescribed medication and the generic name code that must be written on the prescription. For example, prescription information may indicate a numeric code, such as a drug price list code, an individual drug code, or a prescription computer processing system code. Furthermore, patient information is not limited to this, and may include, for example, the patient's age, gender, cancer stage and subtype at a given time, medical history, complications, and other information related to the patient's social situation, such as living environment. Other patient information may be any information necessary to understand the patient's condition. For example, the patient's doctor may input the patient information into the application as information about the patient when using the application. When the server device 101 receives patient information from the medical professional's information terminal 103, it transmits the patient information to a memory unit and stores it. For example, when the server device 101 distributes an application to a user's information terminal 102 and a medical worker's information terminal 103, the server device 101 can later grant the user's information terminal 102 and the medical worker's information terminal 103 permission to use the distributed application.

[0070] For example, when a user uses an application, the user registers account information in the application. When registering an account, the user inputs information written on a prescription from a doctor. The information written on the prescription may include a generic name code, as described above. The user transmits the account information to the server device 101. If the information written on the prescription included in the account information matches the prescription information included in the patient information input by the doctor, the server device 101 may issue the user an ID and password required to log in to the application.

[0071] Furthermore, when a medical professional uses an application, by entering information such as the possession of a medical license, nursing license, or pharmacist license, as well as the registration number of such license, when registering account information, the server device 101 may determine that the user is a medical professional and issue an application ID, password, etc. to the medical professional's information terminal 103 without requiring the input of information such as that described on a prescription. Note that, for example, the server device 101 may generate a patient ID if the user is a patient (if the above-mentioned license information has not been entered), and may generate a medical professional ID if the user is a medical professional (if the above-mentioned license information has been entered), and manage these IDs accordingly.

[0072] This allows the server device 101 to avoid, as much as possible, sending and receiving erroneous information that may occur when exchanging information with a user or a doctor. Furthermore, the authorization to use such an application may be granted not only by the server device 101 but also by the distribution server device 104.

[0073] Note that the information processing performed by the symptom score tallying unit 130a, the symptom fluctuation degree determining unit 130b, the symptom worsening state determining unit 130c, the syndrome prediction unit 130d, and the progression prediction unit 130e is not limited to being performed by the server device 101. For example, the information processing may be performed by the information terminal 102 of a user or the information terminal 103 of a medical professional that executes an application related to the cancer progression-related information notification system 100. In this case, the reception unit 110 of the server device 101 receives the cancer progression information predicted by the user's information terminal 102 or the medical professional's information terminal 103. Based on the received cancer progression information, recommendation information extracted from the memory unit 150 may be presented to the user's information terminal 102, or the probability of hospital visit or hospitalization calculated based on the cancer progression information, recommendation information, the symptom worsening state, the predicted cancer and cancer progression, etc. may be presented to the medical professional's information terminal 103. The above is an explanation of the configurations of the server device 101, the user's information terminal 102, and the medical worker's information terminal 103 related to the cancer progression stage related information notification system 100.

[0074] FIG. 2 is a flowchart illustrating an example of the operation of the server device 101 of the cancer progression-related information notification system 100. The flowchart in FIG. 2 illustrates a main flow of processing by the server device 101 of the cancer progression-related information notification system 100. As illustrated in FIG. 2, in the server device 101 of the cancer progression-related information notification system 100, the reception unit 110 receives information indicating the degree of symptoms for various symptoms input by the user through the user's information terminal 102 (step S210). For example, the reception unit 110 receives information in which the user subjectively scores the degree of symptoms. The received information is transmitted to the symptom score tallying unit 130a. The symptom score tallying unit 130a tally the received information (step S220). The tallying information is transmitted to the symptom fluctuation degree determining unit 130b. The symptom fluctuation degree determining unit 130b determines the degree of symptom fluctuation based on the tallying information (step S230). The information on the determined degree of symptom fluctuation is transmitted to the symptom worsening degree determining unit 130c. The symptom worsening degree determining unit 130c determines the degree of symptom worsening based on the information on the determined degree of symptom fluctuation (step S240). The determined information on the degree of symptom worsening is transmitted to the progression prediction unit 130e. Here, for example, as shown in FIG. 2 , the syndrome prediction unit 130d may predict a syndrome that may be causing the user's symptoms based on the determined information on the degree of symptom worsening (step S250). In this case, the information on the degree of symptom worsening determined by the symptom worsening degree determining unit 130c is transmitted to the syndrome prediction unit 130d. Thereafter, the information on the syndrome predicted by the syndrome prediction unit 130d is transmitted to the progression prediction unit 130e, which predicts the degree of cancer progression.

[0075] Based on the determined degree of symptom worsening, the progression prediction unit 130e predicts the progression of cancer (step S260). Information on the predicted progression of cancer is transmitted to the notification unit 140. As described above, the progression prediction unit 130e can predict the possibility of cancer metastasis in addition to the progression of cancer. The progression prediction unit 130e may also predict the progression of cancer based on the results of imaging diagnosis selected from the group consisting of X-ray examination, CT examination, MRI examination, ultrasound examination, PET examination, and nuclear medicine examination. Here, for example, the communication unit 120 can connect to the information terminal 103 of a medical professional via a network and receive information related to the cancer patient's past imaging diagnosis. The received information on the imaging diagnosis is received by the reception unit 110. The received information is transmitted to the control unit 130.

[0076] The notification unit 140 presents to the user or a medical professional progression information indicating the predicted cancer progression and information recommending medical examination based on the predicted cancer progression (step S270), and ends the process. More specifically, the notification unit 140 transmits progression information indicating the cancer progression predicted by the progression prediction unit 130e to the user's information terminal 102. The notification unit 140 also extracts recommendation information corresponding to the cancer progression information from the storage unit 150 and transmits it to the user's information terminal 102. The storage unit 150 stores the recommendation information to be presented to the user, the date and time when the information from the user was received, the user ID, a management ID issued to the user and corresponding to the user ID, etc.

[0077] For example, the notification unit 140 may extract recommendation information to be presented to the user based on a matrix that associates the cancer stage with information about the recommended medical examination. More specifically, if the cancer stage is I, it is not deemed that an immediate medical examination is required, and the notification unit 140 may extract the lowest level of recommendation strength and notify the user's information terminal 102 of the recommendation information together with the cancer stage information indicating that the cancer stage is I.

[0078] An example of a notification method by the notification unit 140 is to transmit the progression information and recommendation information from the communication unit 120 to the user's information terminal 102 via a network and display them on the screen of the user's information terminal 102. When the recommendation information is presented to the user, the cancer progression related information notification system 100 ends its processing. This allows the cancer patient to understand their own condition and obtain an opportunity to consult with a medical institution before the cancer progresses further.

[0079] The processes from step S210 to step S270 may be executed, for example, by an application related to the cancer progression-related information notification system 100 executed on the user's information terminal 102 or the healthcare professional's information terminal 103. Information on the cancer progression predicted by the application related to the cancer progression-related information notification system 100 executed on the user's information terminal 102 or the healthcare professional's information terminal 103 is transmitted from the communication units 102a, 103a to the server device 101 via the network. In this case, the reception unit 110 related to the server device 101 receives the cancer progression information. Based on the received cancer progression information, the notification unit 140 extracts recommendation information and presents it to the user.

[0080] FIG. 3 is a diagram showing a subflow related to the processing of the cancer progression-related information notification system 100. More specifically, FIG. 3 is a diagram showing a subflow related to the processing after recommendation information is presented to the user. As shown in FIG. 3 , based on the cancer progression predicted by the progression prediction unit 130e, the notification unit 140 presents to the user progression information indicating the cancer progression and recommendation information recommending at least one of medical examination and self-care (step S310). For example, the presentation method may include transmitting the progression information and recommendation information from the communication unit 120 to the user's information terminal 102 via a network, and displaying the progression information and recommendation information on the screen of the information terminal 102.

[0081] Thereafter, the control unit 130 calculates, based on the stage of cancer, the user's probability of visiting a hospital, probability of being hospitalized, recommendation information, the degree of symptom worsening, the predicted cancer and stage of cancer, etc. This process may be performed by the stage of cancer prediction unit 130e, or may be performed by a hospital visit probability / hospitalization probability calculation unit further provided in the cancer stage of cancer related information notification system 100.

[0082] Here, when the progression prediction unit 130e calculates the probability of hospital visit or hospitalization, etc., it can calculate the probability of hospital visit or hospitalization when predicting the progression of cancer based on the degree of worsening of the user's symptoms. In this case, the probability of hospital visit or hospitalization may be calculated based on the progression of cancer or the degree of worsening of the symptoms. For example, the probability of hospital visit or hospitalization may be calculated from the progression of cancer using a predetermined algorithm. More specifically, when the predicted progression of cancer is I, the probability of hospital visit or hospitalization may be calculated from the progression of cancer using a matrix that associates the probability of the user visiting the hospital with 50% and the probability of hospitalization with 10%.

[0083] Furthermore, for example, a method for calculating a user's probability of hospitalization or hospitalization based on the stage of cancer may involve quantifying the stage of cancer and inputting the numerical value as an input variable into a function established based on empirical rules to calculate the probability of hospitalization or hospitalization. In this case, the empirical rule may be, for example, provided in the cancer stage-related information notification system 100 with a machine learning unit that learns information correlating the stage of cancer of past users or other different users with their hospital visit history or hospitalization history as training data, and the machine learning unit sets a function based on the training data. Information about the function set by the machine learning unit is transmitted to a hospital visit probability / hospitalization probability calculation unit, etc. The hospital visit probability / hospitalization probability calculation unit calculates the probability of hospitalization or hospitalization using the transmitted function.

[0084] When the hospital visit probability / hospitalization probability calculation unit calculates the probability of hospital visit or hospitalization, information on the cancer progression predicted by the progression prediction unit 130 e is transmitted to the hospital visit probability / hospitalization probability calculation unit. The hospital visit probability / hospitalization probability calculation unit calculates the user's probability of hospital visit or hospitalization based on the predicted cancer progression.

[0085] The notification unit 140 presents information on the user's hospital visit probability and hospitalization probability calculated by the progress prediction unit 130e or the hospital visit probability / hospitalization probability calculation unit to the medical worker's information terminal 103 (step S320). Here, the method of presenting the information on the hospital visit probability and hospitalization probability to the medical worker's information terminal 103 can be, for example, by transmitting the information on the user's hospital visit probability and hospitalization probability from the communication unit 120 of the server device 101 to the medical worker's information terminal 103 via a network and displaying it on the screen of the medical worker's information terminal 103.

[0086] At this time, the information presented on the medical worker's information terminal 103 can include, for example, the user ID, the user's name, and scored information regarding the severity of the symptoms entered by the user, in addition to the user's probability of visiting the hospital and the probability of being hospitalized. This allows the medical worker to comprehensively grasp in advance information regarding the symptoms felt by the user, the severity of the symptoms felt by the user, information regarding the progression of the cancer, and the like, and can take more appropriate measures during medical treatment.

[0087] After presenting the cancer progression-related information notification system 100 with the progression information and recommendation information to the user, the system accepts input of feedback information regarding the cancer progression-related information notification system 100 from the user (step S330). For example, an application related to the cancer progression-related information notification system 100 executed on the user's information terminal 102 may previously possess information on an evaluation form for feedback regarding the cancer progression-related information notification system 100. The user uses the feedback evaluation form to provide feedback regarding the cancer progression-related information notification system 100 from their own information terminal 102. Furthermore, the input feedback information accepted by the cancer progression-related information notification system 100 may be, for example, information more directly input by a medical professional or the user into the information terminal 103 or the information terminal 102 regarding the treatment (e.g., hospitalization) actually provided to the user. Here, for example, the feedback method may be a point-based evaluation, as described below.

[0088] More specifically, the user checks the score that they feel is appropriate in the feedback evaluation table. When the user has finished inputting the feedback, they transmit the feedback information to the server device 101. The server device 101 receives the feedback information. That is, when the receiving unit 110 receives the input of feedback information from the user, the received information is transmitted to the control unit 130. Further details of the feedback will be described later using FIG. 12.

[0089] The notification unit 140 modifies the recommendation strength of the recommendation information based on the received information (step S340). Here, the recommendation strength of the recommendation information refers to the rank of the recommendation information. More specifically, for example, it includes a rank when the recommendation strength of the recommendation information is divided into four levels, A to D, as described below. Note that, just as the recommendation strength of the recommendation information is expressed, the recommendation strength of the consultation recommendation information and the self-care recommendation strength of the self-care recommendation information are also expressed. "Modification" refers to changing the information. For example, it refers to adjusting the rank described above. More specifically, it includes, for example, changing the recommendation strength of the recommendation information from rank A to rank B, or from rank D to rank C. A method for correcting the recommendation intensity of the recommendation information may involve, for example, calculating a total value from the checked score information in the feedback evaluation sheet, and then correcting the recommendation intensity of the recommendation information, determining the degree of symptom fluctuation, determining the degree of symptom worsening, or predicting the progression of cancer based on a matrix that associates the total value with recommendation information of several pre-set levels of recommendation intensity.

[0090] More specifically, the cancer progression prediction unit 130e changes the cancer progression predicted before the server device 101 related to the cancer progression related information notification system 100 receives the feedback information based on the total value of the points included in the feedback information.

[0091] Furthermore, for example, the symptom fluctuation degree determining unit 130b and the symptom worsening degree determining unit 130c may change a threshold value used in information processing related to determining the degree of symptom fluctuation or the degree of symptom worsening, based on the total value of the scores included in the feedback information, and may re-determine the degree of symptom fluctuation or the degree of symptom worsening based on the changed threshold value. In this case, the progression degree predicting unit 130e may be able to re-predict the degree of cancer progression based on the re-determined degree of symptom worsening.

[0092] The notification unit 140 can extract new recommendation information based on the newly predicted cancer progression and the newly predicted cancer progression, and present the information to the user. This allows the user to receive progression information and recommendation information that have been appropriately revised in response to feedback. In this case, the notification unit 140 or the hospital visit probability / hospitalization probability calculation unit presents information on the user's hospital visit probability and hospitalization probability to the medical professional's information terminal 103 based on the newly predicted cancer progression. This allows medical professionals to share the hospital visit probability and hospitalization probability that have been appropriately revised in response to user feedback, leading to the provision of appropriate medical care to the user.

[0093] As a specific example of modifying the recommendation strength of recommendation information, for example, if the total value of the feedback points falls below a predetermined threshold, the notification unit 140 determines that the user is dissatisfied with the progression information and recommendation information presented by the cancer progression related information notification system 100, and based on the matrix described below, the recommendation strength of the recommendation information that has already been presented will be reduced by one level next time.

[0094] Furthermore, after correcting the recommendation intensity of the recommendation information based on the feedback information, the notification unit 140 can further correct the recommendation intensity of the recommendation information based on information such as the user's accompanying symptoms, drug classification, combination, dosage, administration time, administration method, administration order, and administration date (step S350). For example, when the user inputs information that scores the severity of symptoms, they also input information such as accompanying symptoms, drug dosage, administration time, administration method, administration order, and administration date, and the receiving unit 110 receives the input of each piece of information. Drugs include anticancer drugs. Drug classification may be based on the pharmacological classification of prescription drugs.

[0095] Based on the received information, the notification unit 140 modifies the recommendation strength of the recommendation information. The modified recommendation information is presented to the user (step S360), and the processing ends. Although not shown in FIG. 3 , the probability of hospital visit, probability of hospitalization, recommendation information, symptom worsening, predicted cancer and cancer progression, etc. may be recalculated based on the modified recommendation information. In this case, information regarding the recalculated probability of hospital visit, probability of hospitalization, etc. is presented again to the medical professional's information terminal 103. This allows the notification unit 140 to present to the user recommendation information that is more appropriate for the user's current condition. Furthermore, the notification unit 140 can present more appropriate user information to the medical professional and provide more appropriate treatment to the user. Furthermore, the notification unit 140 may present to the user, together with the modified recommendation information, progression information indicating the new cancer progression predicted by the progression prediction unit 130e based on feedback information from the user.

[0096] Of the processes shown in FIG. 3, the process of step S310 may be executed, for example, in an application related to the cancer progression-related information notification system 100 executed on the user's information terminal 102 or the medical professional's information terminal 103.

[0097] 4 is a sequence diagram showing data exchange between a server device 101 that provides the cancer progression-related information notification system 100, a user's information terminal 102, and a medical professional's information terminal 103. As shown in FIG. 4, the user's information terminal 102 requests the server device 101 to provide the application for the cancer progression-related information notification system 100 (step S410). Here, the server device that provides the application for the cancer progression-related information notification system 100 to the user and the server device 101 that determines whether or not to provide the application related to the cancer progression-related information notification system 100 to the user may be separate devices.

[0098] Upon receiving the request, the server device 101 transmits application information for the cancer progression-related information notification system 100 to the user's information terminal 102 (step S420). The user's information terminal 102 receives the application information for the cancer progression-related information notification system 100. This allows the user to use the cancer progression-related information notification system 100 from the information terminal 102. Furthermore, the medical professional's information terminal 103 requests the server device 101 to transmit the application information for the cancer progression-related information notification system 100 (step S430). Upon receiving the request, the server device 101 transmits application information for the cancer progression-related information notification system 100 to the medical professional's information terminal 103 (step S440). This allows the medical professional to use the cancer progression-related information notification system 100 from the information terminal 103.

[0099] Upon receiving the application information, the user inputs information indicating the severity of the user's symptoms for various symptoms on the application's input screen. For example, the user inputs information indicating the severity of the user's symptoms, converted into scores, on the application's input screen. At this time, a medical questionnaire, as described below, is displayed on the input screen, and the user selects and checks the types of symptoms that they feel apply to them and the scores representing the severity of the symptoms. After the user has finished inputting the information regarding the severity of the symptoms, the user transmits the input information to the server device 101 (step S450). Upon receiving the information from the user, the server device 101 predicts the stage of cancer based on the above-described processing flow. The server device 101 transmits to the user's information terminal 102 stage information indicating the predicted stage of cancer and information recommending a medical examination based on the predicted stage of cancer (step S460).

[0100] Next, the server device 101 determines the user's probability of visiting the hospital, probability of being hospitalized, recommendation information, the degree of symptom worsening, predicted cancer and cancer progression, etc. based on the predicted cancer progression. The server device 101 transmits information related to the calculated probability of visiting the hospital, probability of being hospitalized, etc. to the medical professional's information terminal 103 (step S470). After transmitting the recommendation information to the user's information terminal 102, the server device 101 requests input of feedback to the cancer progression-related information notification system 100 (step S480). Upon receiving the request from the server device 101, the user inputs feedback to the cancer progression-related information notification system 100. One method of inputting feedback is to input the feedback into a feedback input screen transmitted from the server device 101 to the user's information terminal 102.

[0101] For example, the feedback may be provided using an evaluation table that associates preset questions with scores representing answers to the questions. However, the feedback method is not limited to this. For example, the user may provide written feedback in response to preset questions. The user then transmits the feedback information that they have input to the server device 101 (step S490).

[0102] Upon receiving feedback information from the user, the server device 101 modifies the recommendation strength of the next recommendation information based on the received feedback information. The modified recommendation information is presented to the user (step S500), and the process ends. As described above, the server device 101 can present to the user, in addition to the modified recommendation information, stage information indicating the cancer stage newly predicted by the stage prediction unit 130e based on the feedback information from the user. This process flow can be executed repeatedly. As a result, the cancer stage-related information notification system 100 can provide cancer patients with more appropriate stage information indicating the cancer stage and recommendation information, and medical professionals can provide more appropriate treatment to cancer patients.

[0103] 5 is a diagram showing an example of an input screen 500 on which a user inputs information indicating the severity of various symptoms experienced by the user in the cancer progression stage related information notification system 100. As shown in FIG. 5, the input screen 500 can be created, for example, based on a medical questionnaire commonly used in medical institutions. However, the input screen 500 is not limited to the form shown in FIG. 5. For example, as described above, the input screen 500 may have a form in which symptoms are associated with scores indicating the severity of the symptoms.

[0104] The user subjectively scores their own symptoms on an input screen 500 such as that shown in Fig. 5. For example, if the user feels that the item "I'm not feeling well overall" shown in Fig. 5 somewhat applies to them, they check the box for score 3, which indicates a moderate degree. In this way, the user scores the degree of the symptoms they feel for each symptom item on the medical questionnaire. The information that has been entered is sent to the server device 101.

[0105] For example, a send button may be provided at the bottom of the input screen 500, and by clicking the button, the input information is automatically sent to the server device 101. As a result, the symptom score tallying unit 130a in the cancer progression related information notification system 100 tally and manages the input information from the user received by the receiving unit 110.

[0106] The questionnaire displayed on the input screen 500 shown in FIG. 5 may be a questionnaire created based on a matrix described below, or more specifically, a questionnaire created based on the Edmonton Rating System. Furthermore, the scores representing the severity of symptoms may be set based on, for example, the Numeric Rating Scale (NRS). More specifically, for example, with respect to the severity of pain, a value of 0 indicates "no pain" and a value of 10 indicates "extreme pain (the most severe pain ever experienced)." In this way, pain may be divided into 11 levels from 0 to 10, and the user may select the level of pain numerically. Furthermore, the control unit 130 may use the NRS to predict symptom fluctuations, symptom worsening, and the user's syndrome. Furthermore, the questionnaire displayed on the input screen 500 may be a questionnaire created based on the semantic differential method in addition to the NRS. Here, the control unit 130 may use the NRS and the Semantic Differential Method (SD) to predict the fluctuation of symptoms, the degree of worsening of symptoms, and the user's syndrome based on the set numerical values.

[0107] 6 is a diagram showing a graph 600 used when determining the degree of symptom fluctuation based on the collected information. As shown in Fig. 6, graph 600 shows the date and time when information input from the user was received on the horizontal axis and the total score of each symptom, which is information that subjectively scores the degree of symptom severity, on the vertical axis.

[0108] For example, the symptom fluctuation degree determining unit 130b calculates the difference between the total scores for any two consecutive days after the symptom score tallying unit 130a calculates the total score indicating the degree of symptom severity for each day based on information input by the user. If the difference exceeds a threshold, the symptom fluctuation degree determining unit 130b determines the degree of symptom fluctuation. However, the method for determining the degree of symptom fluctuation is not limited to this.

[0109] For example, the symptom fluctuation degree determining unit 130b may determine the degree of symptom fluctuation based on a weekly total value rather than a daily total value. Furthermore, instead of totaling the scores of various symptoms, the symptom score tallying unit 130a may tally and manage score information for each symptom and the date and time when the symptom information is input.

[0110] Furthermore, the symptom fluctuation degree determination unit 130b may associate the date and time of receiving input with score information for each symptom, and determine the degree of symptom fluctuation based on, for example, the difference in scores for each symptom over any two consecutive days. For example, as shown in Figure 6, the degree of symptom fluctuation may be determined based on information that the user input on January 1, 2022 that the symptom severity is 7 and information that the user input one week later on January 8, 2022 that the symptom severity is 10. In this case, the difference in scores indicating the symptom severity is 3.

[0111] Here, for example, if the threshold value is set to 2, the symptom fluctuation degree determining unit 130b determines the degree of fluctuation in the user's symptoms between January 1, 2022 and January 8, 2022, as shown in Fig. 6. Next, information on the determined degree of symptom fluctuation is transmitted to the symptom worsening degree determining unit 130c.

[0112] The symptom worsening degree determining unit 130c determines the degree of symptom worsening based on the information determining the degree of symptom fluctuation. For example, the symptom worsening degree determining unit 130c sets different thresholds depending on the type of symptom, and uses a graph 600 shown in FIG. 6 to calculate a slope value obtained from the difference between the total values ​​calculated by the symptom fluctuation degree determining unit 130b for an arbitrary period (preferably a period of three days or more) and the numerical information on the date and time when the input was received, and determines the degree of symptom worsening based on the calculated slope value. More specifically, if the calculated slope value is below a predetermined threshold, the symptom worsening degree determining unit 130c determines that symptom worsening is not observed, and if the calculated slope value is above the predetermined threshold, the symptom worsening degree determining unit 130c determines that symptom worsening is observed. When determining that symptom worsening is observed, the symptom worsening degree determining unit 130c may determine the degree of symptom worsening as one or more stages. For example, the symptom worsening degree determining unit 130c may determine the degree of worsening of the symptom in three stages: "slight worsening," "worsening," and "significant worsening." The symptom worsening degree determining unit 130c may determine one or more stages of the degree of worsening of the symptom based on the value of the calculated slope. More specifically, the symptom worsening degree determining unit 130c may determine one or more stages of the degree of worsening of the symptom based on whether the value of the calculated slope exceeds one or more predetermined thresholds corresponding to one or more stages of worsening of the symptom. However, the method of determining the degree of worsening of the symptom is not limited to this.

[0113] For example, the symptom worsening degree determining unit 130c may set a different coefficient for each symptom and determine the degree of symptom worsening using the product of the difference between the total values ​​calculated by the symptom fluctuation degree determining unit 130b and the coefficient. In this case, if the product exceeds a predetermined threshold, the symptom worsening degree determining unit 130c determines that the symptom has worsened. Information on the degree of symptom worsening determined by the symptom worsening degree determining unit 130c may be transmitted to the progression degree predicting unit 130e or the syndrome predicting unit 130d.

[0114] FIG. 7 is a diagram showing an example of a matrix 700 used by the syndrome prediction unit 130d when predicting a syndrome based on the degree of symptom worsening. As shown in FIG. 7, the matrix 700 is a diagnostic matrix commonly used in medical institutions. The matrix 700 is not limited to the one shown in FIG. 7. For example, any matrix may be used as long as it associates syndromes with the degree of symptom worsening. For example, the matrix 700 shown in FIG. 7 can be used to determine that a user is suspected of having acute abdominal pain if the user experiences abdominal pain for a certain period of time. The syndrome prediction unit 130d uses the matrix 700 shown in FIG. 7 and predicts a syndrome that is thought to be the cause of the user's symptoms based on the type of symptoms the user is experiencing and the degree of symptom worsening. Information about the syndrome predicted by the syndrome prediction unit 130d is transmitted to the progression prediction unit 130e.

[0115] It should be noted that the matrix 700 is not limited to the form shown in Fig. 7. The columns for representative symptoms shown in the matrix 700 include, in addition to pain, fatigue, impaired consciousness, nausea, loss of appetite, dyspnea, fever, bleeding, edema, movement disorder, palpitations, cough, visual impairment, hearing impairment, and the like.

[0116] Furthermore, as the syndromes corresponding to the representative symptoms shown in the matrix 700, for example, if the representative symptom is pain, the corresponding syndromes may be set as stroke, cardiovascular event, acute abdomen, severe skin disease, spinal cord compression, mucocutaneous disorder, and peripheral neuropathy. Also, if the representative symptom shown in the matrix 700 is fatigue, the corresponding syndromes may be set as renal failure, neuromuscular disease, jaundice, and PS decline. Also, if the representative symptom shown in the matrix 700 is consciousness disorder, the corresponding syndromes may be set as central nervous system disorder and metabolic consciousness disorder.

[0117] Furthermore, if the representative symptom shown in matrix 700 is nausea, the corresponding syndrome may be set as "inability to drink orally." If the representative symptom shown in matrix 700 is loss of appetite, the corresponding syndrome may be set as "inability to drink orally" and "severe diarrhea." If the representative symptom shown in matrix 700 is dyspnea, the corresponding syndrome may be set as heart failure, respiratory failure, or severe allergy. If the representative symptom shown in matrix 700 is fever, the corresponding syndrome may be set as bloodstream infection or acute organ infection.

[0118] Furthermore, if the representative symptom shown in matrix 700 is bleeding, the corresponding syndrome may be set as gastrointestinal bleeding, hemoptysis, or difficulty in hemostasis. If the representative symptom shown in matrix 700 is edema, the corresponding syndrome may be set as venous thrombosis or heart failure. If the representative symptom shown in matrix 700 is movement disorder, the corresponding syndrome may be set as neuromuscular disease. If the representative symptom shown in matrix 700 is palpitations, the corresponding syndrome may be set as cardiotoxicity or electrolyte abnormality. If the representative symptom shown in matrix 700 is visual impairment, the corresponding syndrome may be set as acute visual impairment. If the representative symptom shown in matrix 700 is hearing impairment, the corresponding syndrome may be acute hearing impairment.

[0119] In addition, the syndrome name column of matrix 700 may include, for example, stroke, cardiovascular event, acute abdominal pain, as well as severe skin disease, spinal cord compression, mucocutaneous disorders, peripheral neuropathy, renal failure, neuromuscular disease, jaundice, decreased PS, central nervous system disorders, metabolic disturbance, inability to drink water orally, severe diarrhea, cardiac failure, respiratory failure, severe allergy, bloodstream infection, acute organ infection, gastrointestinal bleeding, hemoptysis, difficulty in hemostasis, venous thrombosis, cardiotoxicity, electrolyte abnormalities, drug-induced lung disorders, acute visual impairment, acute hearing impairment, etc.

[0120] The symptom columns shown in matrix 700 may include, for example, pain, fatigue, drowsiness, nausea, loss of appetite, shortness of breath, fever, bleeding, edema, movement disorder, palpitations, cough, visual impairment, hearing impairment, numbness, incontinence, decreased urinary output, yellowing of the skin, severe dry mouth, wheezing, dizziness, diarrhea, etc. Matrix 700 may also be a table based on the Edmonton Symptom Assessment System. The medical questionnaire shown in FIG. 5 may also be a medical questionnaire created based on, for example, information on the symptoms and syndromes shown in matrix 700 shown in FIG. 7.

[0121] Furthermore, with regard to the column for the syndrome summary shown in matrix 700, the summary of stroke may be, for example, a headache that worsens over time, and sometimes nausea, vomiting, and impaired consciousness. Furthermore, the summary of cardiovascular events shown in matrix 700 may be chest pain or back pain that worsens over time, and sometimes dyspnea, nausea, and vomiting. Furthermore, the summary of acute abdominal pain shown in matrix 700 may be abdominal pain that worsens over time, and sometimes fever, loss of appetite, nausea, and vomiting.

[0122] The outline of a severe skin disease shown in matrix 700 may be body surface pain (always accompanied by widespread skin damage) that worsens over a daily period. The outline of spinal cord compression shown in matrix 700 may be back pain (always accompanied by motor impairment, sensory impairment, or bladder-rectum disorder) that worsens over a daily to weekly period. The outline of a mucosal skin disorder shown in matrix 700 may be skin damage or stomatitis (sometimes accompanied by pain) that worsens over a weekly period. The terms "weekly," "daily," and "hourly" may include units of time less than the respective units. That is, "weekly" may include units of time less than the respective units, such as "dayly" and "hourly," and "daily" may include units of time less than the respective units, such as "hourly." Thus, for example, worsening over a weekly period may include worsening over a daily period and worsening over an hourly period, and worsening over a daily period may include worsening over an hourly period.

[0123] Here, the symptom deterioration determining unit 130c may determine that a symptom is worsening on a daily basis if the score indicating the evaluation by the NRS described above for each symptom is 2 or more and exceeds the average + 2SD calculated from the past 7 days. Alternatively, it may determine that a symptom is worsening on a weekly basis if the average score indicating the evaluation by the NRS for each symptom over the past 7 days is 2 or more higher than the previous week. Here, 2SD refers to a value twice the value of the standard deviation. Note that, although 7 days was used as the period used to calculate the average and standard deviation (SD) above, this is not limiting.

[0124] Furthermore, when the score indicating the NRS evaluation for pain or dyspnea on a certain day is 7 or more, the symptom score aggregation unit 130a may transmit the score information to the notification unit 140 without determining the degree of symptom fluctuation, the degree of symptom worsening, syndrome prediction, or cancer progression prediction, and the notification unit 140 may present information recommending at least one of medical examination and self-care to the user. When the user inputs essential symptom information on the input screen 500 shown in FIG. 5 when predicting the syndrome shown in the matrix 700 shown in FIG. 7, for example, when the user inputs information indicating that a doctor has instructed the user to take medication for the essential symptom experienced by the user, when the user inputs information indicating that a doctor has instructed the user to see a doctor for the symptom, or when the user inputs information indicating that a doctor has instructed the user to ignore the symptom, the syndrome prediction unit 130d may predict the user's syndrome while taking into account the input case information. Furthermore, for example, in the cancer progression related information notification system 100, the expressions may be changed as appropriate, such as hematemesis and bloody stool shown in the matrix 700 meaning vomiting or coughing up blood, or red or black muddy stools; edema meaning swelling of part or all of the body, leaving marks when pressed with the fingers; motor disorder meaning inability to move the hands or feet, difficulty speaking, or difficulty swallowing; skin disorder meaning skin rash, peeling skin, or mouth ulcers; numbness meaning feeling a tingling or prickling sensation on the skin that should not be there; and decreased urine volume meaning urinating significantly less frequently than usual.

[0125] 5 is a questionnaire created based on the Edmonton Rating System, and the matrix for predicting a syndrome is also created based on the Edmonton Rating System, different priorities may be set for the criteria for predicting a syndrome depending on the symptoms. For example, the matrix 700 shown in FIG. 7 may have headache as an essential symptom of stroke, and if the headache worsens over time, the syndrome prediction unit 130d may predict that the user's syndrome is stroke.

[0126] Furthermore, in a case where the representative symptom is pain and the corresponding syndrome name is a cardiovascular event, and chest or back pain is an essential symptom and the chest or back pain is worsening over time, the syndrome prediction unit 130d may predict that the user's syndrome is a cardiovascular event. In a case where the representative symptom is acute abdominal pain and abdominal pain is an essential symptom and the abdominal pain is worsening over time, the syndrome prediction unit 130d may predict that the user's syndrome is acute abdominal pain. In a case where the representative symptom is a severe skin disease and pain on the body surface and a widespread skin disorder are essential symptoms and the body surface pain is worsening over time and the skin disorder is worsening over time, the syndrome prediction unit 130d may predict that the user's syndrome is a severe skin disease. Furthermore, for spinal cord compression, the representative symptom of which is pain, back pain and any one of the symptoms of movement disorder, numbness, and incontinence are essential symptoms, and the back pain is worsening on a weekly basis, and any one of the symptoms of movement disorder, numbness, and incontinence is worsening on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is spinal cord compression. For mucocutaneous disorders, the representative symptom of which is pain is a skin disorder, and the skin disorder is worsening on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is mucocutaneous disorders. For mucocutaneous disorders, if pain is present on the body surface or bleeding is observed in the mouth, hands, or feet, the syndrome prediction unit 130d may exclude mucocutaneous disorders from the predicted syndromes in order to select more severe syndromes. For peripheral neuropathy, the representative symptom of which is pain is a peripheral neuropathy if numbness is worsening on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is peripheral neuropathy. In addition, in the case of peripheral neuropathy, if pain is observed in the hands and feet, the syndrome prediction unit 130d may exclude peripheral neuropathy from the syndromes to be predicted in order to select syndromes with a higher degree of severity.

[0127] Furthermore, in the case of renal failure whose representative symptom is fatigue, if the decrease in urine volume worsens on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is renal failure. In the case of renal failure, if edema is observed throughout the body, the syndrome prediction unit 130d may exclude renal failure from the syndromes to be predicted in order to select a more severe syndrome. In the case of neuromuscular disease whose representative symptom is fatigue, if movement disorder worsens on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is neuromuscular disease. In the case of neuromuscular disease, if pain is observed in the head or limbs, the syndrome prediction unit 130d may exclude neuromuscular disease from the syndromes to be predicted in order to select a more severe syndrome. In the case of jaundice whose representative symptom is fatigue, if yellowing of the skin worsens on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is jaundice. In addition, in the case of PS decline, the representative symptom of which is fatigue, if any of the symptoms of lethargy, drowsiness, nausea, and loss of appetite worsens on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is PS decline.

[0128] Furthermore, in the case of a central nervous system disorder whose representative symptom is a disturbance of consciousness, if headache worsens daily, drowsiness worsens daily, or nausea worsens daily, the syndrome prediction unit 130d may predict that the user's syndrome is a central nervous system disorder. Furthermore, in the case of a metabolic disorder of consciousness whose representative symptom is a disturbance of consciousness, if drowsiness worsens weekly, or nausea worsens weekly, the syndrome prediction unit 130d may predict that the user's syndrome is a metabolic disorder of consciousness. Furthermore, in the case of a metabolic disorder of consciousness, if abdominal pain is observed, the syndrome prediction unit 130d may exclude metabolic disorder of consciousness from the syndromes to be predicted in order to select a more severe syndrome.

[0129] In the case of the inability to drink oral water whose representative symptom is nausea, if either the nausea worsens day by day or the loss of appetite worsens day by day, the syndrome prediction unit 130d may predict that the user's syndrome is the inability to drink oral water. Furthermore, if abdominal pain is observed in the case of the inability to drink oral water, the syndrome prediction unit 130d may exclude the inability to drink oral water from the predicted syndromes in order to select a more serious syndrome.

[0130] In the case of the inability to drink oral water whose representative symptom is loss of appetite, if either nausea is getting worse day by day or loss of appetite is getting worse day by day, the syndrome prediction unit 130d may predict that the user's syndrome is inability to drink oral water. Furthermore, in the case of the inability to drink oral water whose representative symptom is loss of appetite, if abdominal pain is observed, the syndrome prediction unit 130d may exclude the inability to drink oral water from the predicted syndromes in order to select a more severe syndrome. Furthermore, in the case of severe diarrhea whose representative symptom is loss of appetite, if abdominal pain is observed, the syndrome prediction unit 130d may exclude the inability to drink oral water from the predicted syndromes in order to select a more severe syndrome.

[0131] Furthermore, in the case of cardiac failure whose representative symptom is dyspnea, if either shortness of breath worsens over a week or edema is worsening throughout the body over a week, the syndrome prediction unit 130d may predict that the user's syndrome is cardiac failure. Furthermore, in the case of cardiac failure, if either chest pain or hemoptysis is present, the syndrome prediction unit 130d may exclude cardiac failure from the syndromes to be predicted in order to select a more severe syndrome. Furthermore, in the case of respiratory failure whose representative symptom is dyspnea, if shortness of breath worsens over a week, the syndrome prediction unit 130d may predict that the user's syndrome is respiratory failure. Furthermore, in the case of respiratory failure, if chest pain is present or edema is present on the face, the syndrome prediction unit 130d may exclude respiratory failure from the syndromes to be predicted in order to select a more severe syndrome. Furthermore, in the case of severe allergy whose representative symptom is dyspnea, if shortness of breath worsens over an hour, the syndrome prediction unit 130d may predict that the user's syndrome is severe allergy. In addition, in the case of severe allergies, if abdominal pain is observed, or if edema is observed on the face or throughout the body, the syndrome prediction unit 130d may exclude severe allergies from the syndromes to be predicted in order to select a syndrome with a higher degree of severity.

[0132] For a bloodstream infection whose representative symptom is a fever, if the fever worsens on a daily basis, the syndrome prediction unit 130d may predict that the user's syndrome is a bloodstream infection.For an acute organ infection whose representative symptom is a fever, if the fever worsens on a daily basis, the syndrome prediction unit 130d may predict that the user's syndrome is an acute organ infection.

[0133] In the case of gastrointestinal bleeding, the representative symptom of which is bleeding, if vomiting blood and bloody stool worsen over time, the syndrome prediction unit 130d may predict that the user's syndrome is gastrointestinal bleeding. Furthermore, in the case of gastrointestinal bleeding, if abdominal pain is also present, the syndrome prediction unit 130d may exclude gastrointestinal bleeding from the syndromes to be predicted in order to select a more serious syndrome. Furthermore, in the case of hemoptysis, the representative symptom of which is bleeding, if hemoptysis worsens over time, the syndrome prediction unit 130d may predict that the user's syndrome is hemoptysis. Furthermore, in the case of difficult hemostasis, the representative symptom of which is bleeding, if bleeding does not stop over time, the syndrome prediction unit 130d may predict that the user's syndrome is difficult hemostasis.

[0134] Furthermore, in the case of venous thrombosis, the representative symptom of which is edema, if the edema in the face and limbs worsens over time, the syndrome prediction unit 130d may predict that the user's syndrome is venous thrombosis. In the case of venous thrombosis, if pain in the limbs is observed, the syndrome prediction unit 130d may exclude venous thrombosis from the syndromes to be predicted in order to select a more severe syndrome. In the case of heart failure, the representative symptom of which is edema, if shortness of breath worsens over time or if edema throughout the body worsens over time, the syndrome prediction unit 130d may predict that the user's syndrome is heart failure. In the case of heart failure, if chest pain and hemoptysis are observed, the syndrome prediction unit 130d may exclude heart failure from the syndromes to be predicted in order to select a more severe syndrome.

[0135] In addition, in the case of a neuromuscular disease whose representative symptom is a movement disorder, if the movement disorder worsens on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is a neuromuscular disease. In addition, in the case of a neuromuscular disease, if headache or pain in the limbs is observed, the syndrome prediction unit 130d may exclude the neuromuscular disease from the syndromes to be predicted in order to select a syndrome with a higher severity.

[0136] Furthermore, in the case of cardiotoxicity whose representative symptom is palpitations, if the palpitations worsen on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is cardiotoxicity. Furthermore, in the case of cardiotoxicity, if edema is observed throughout the body, the syndrome prediction unit 130d may exclude cardiotoxicity from the syndromes to be predicted in order to select a more serious syndrome. Furthermore, in the case of electrolyte abnormality whose representative symptom is palpitations, if the palpitations worsen on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is electrolyte abnormality.

[0137] In the case of drug-induced pulmonary injury where the representative symptom is cough, if the cough worsens on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is drug-induced pulmonary injury. In the case of acute visual impairment where the representative symptom is visual impairment, if the visual impairment worsens on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is acute visual impairment. In the case of acute hearing impairment where the representative symptom is hearing impairment, if the hearing impairment worsens on a weekly basis, the syndrome prediction unit 130d may predict that the user's syndrome is acute hearing impairment.

[0138] The outline of peripheral neuropathy shown in matrix 700 may be numbness (sometimes accompanied by pain) that worsens over a period of days. The outline of renal failure shown in matrix 700 may be decreased urine output that worsens over a period of days to weeks, and sometimes fatigue, loss of appetite, nausea, and vomiting. The outline of neuromuscular disease shown in matrix 700 may be movement disorder that worsens over a period of days to weeks, and sometimes fatigue and difficulty breathing.

[0139] The outline of jaundice shown in matrix 700 may be yellowing of the skin and eyes, which worsen over days to weeks, and sometimes fatigue and impaired consciousness. The outline of PS decline shown in matrix 700 may be fatigue, drowsiness, nausea or loss of appetite, which worsen over days, accompanied by a significant increase in time spent in bed. The outline of central nervous system disorders shown in matrix 700 may be two or more of headache, drowsiness, and nausea, which worsen over days.

[0140] The outline of metabolic disturbance of consciousness shown in matrix 700 may be fatigue or drowsiness that worsens over days to weeks, and inability to perform daily activities such as going to the toilet or eating. The outline of inability to drink water orally shown in matrix 700 may be nausea that worsens over days, inability to orally ingest the minimum amount of water needed, or loss of appetite that worsens over days, and inability to orally ingest the minimum amount of water needed.

[0141] Furthermore, the outline of severe diarrhea shown in matrix 700 may be diarrhea lasting for two or more days that exceeds the baseline number of bowel movements by four or more times (equivalent to CTCAE Grade 2). Here, the baseline number of bowel movements refers to the number of bowel movements over a predetermined number of days up to the previous day, for example, the number of bowel movements over a seven-day period up to the previous day. Here, CTCAE refers to the Common Terminology Criteria for Adverse Events. More specifically, CTCAE refers to a set of criteria for standardizing the classification of side effects of drugs used in cancer treatment. Here, the CTCAE Grade of diarrhea in the outline of severe diarrhea may be a Grade other than Grade 2. In this case, various judgment criteria shown in matrix 700 may be appropriately changed depending on the changed Grade. Furthermore, the outline of heart failure shown in matrix 700 may be dyspnea or generalized edema that worsens over a period of days to weeks, and sometimes fatigue and wheezing.

[0142] The outline of respiratory failure shown in matrix 700 may be dyspnea that worsens over days to weeks, and sometimes fever, fatigue, and wheezing. The outline of severe allergy shown in matrix 700 may be dyspnea that worsens over hours, and sometimes rash, abdominal pain, and loss of consciousness. The outline of bloodstream infection shown in matrix 700 may be a fever of 37.5°C or higher during a specific regimen, clearly above baseline, and sometimes loss of consciousness.

[0143] The outline of acute organ infection shown in matrix 700 may be a temperature of 37.5°C or higher, a clear fever from baseline, and occasionally abdominal pain, back pain, and coughing. The outline of gastrointestinal bleeding shown in matrix 700 may be hematemesis, black stool, or a large amount of fresh blood in the stool. The outline of hemoptysis shown in matrix 700 may be coughing up blood itself. The outline of difficult hemostasis shown in matrix 700 may be bleeding from a site where bleeding does not stop even with pressure or where hemostasis is impossible.

[0144] The outline of venous thrombosis shown in matrix 700 may be swelling in a specific area that worsens over days. The outline of heart failure shown in matrix 700 may be swelling that worsens over days to weeks, and sometimes dyspnea, fatigue, and wheezing. The outline of neuromuscular disease shown in matrix 700 may be movement disorder that worsens over days to weeks, and sometimes fatigue and dyspnea.

[0145] The outline of cardiac toxicity shown in matrix 700 may be a previously unnoticed irregular heartbeat during a specific regimen or tachycardia induced by slight body movement. The outline of drug-induced lung injury shown in matrix 700 may be a cough that worsens over days to weeks, and frequent fever and dyspnea during a specific regimen. The outline of acute visual impairment shown in matrix 700 may be visual impairment that worsens over days to weeks. The outline of acute hearing impairment shown in matrix 700 may be hearing impairment that worsens over weeks.

[0146] Furthermore, the column for the main pathology to be detected shown in the matrix 700 may be set to cerebral hemorrhage and subarachnoid hemorrhage, for example, if the representative symptom is pain and the syndrome name is stroke. Furthermore, the column for the main pathology to be detected shown in matrix 700 may be set to, for example, angina pectoris, myocardial infarction, or aortic dissection when the representative symptom is pain and the corresponding syndrome is a cardiovascular event; gastrointestinal perforation, cholelithiasis, ischemic enterocolitis, hemorrhagic cystitis, pancreatitis, or fulminant type 1 diabetes when the representative symptom is pain and the corresponding syndrome is acute abdominal pain; toxic epidermal necrolysis, Stevens-Johnson syndrome, bullous pemphigoid, or tumor tissue infection when the representative symptom is pain and the corresponding syndrome is a severe skin disease; spinal cord compression when the representative symptom is pain and the corresponding syndrome is spinal cord compression; hand-foot syndrome, stomatitis, or urticaria when the representative symptom is pain and the corresponding syndrome is a mucocutaneous disorder; or peripheral sensory neuropathy when the representative symptom is pain and the corresponding syndrome is a peripheral neuropathy.

[0147] Furthermore, the column for the main pathological condition to be detected shown in matrix 700 may be set to, for example, tumor lysis syndrome, thrombotic microangiopathy, rhabdomyolysis, acute renal failure when the representative symptom is fatigue and the corresponding syndrome name is renal failure; cerebral infarction, Guillain-Barré syndrome, myasthenia gravis, leukoencephalopathy, hypokalemia when the representative symptom is fatigue and the corresponding syndrome name is neuromuscular disease; acute hepatitis, cholestasis, jaundice when the representative symptom is fatigue and the corresponding syndrome name is jaundice; and cachexia when the representative symptom is fatigue and the corresponding syndrome name is decreased PS.

[0148] In addition, the column for the main pathological condition to be detected shown in matrix 700 may be set to, for example, encephalitis, meningitis, or increased intracranial pressure if the representative symptom is consciousness disorder and the corresponding syndrome name is central nervous system disorder, or may be set to hypoadrenalism, hyperthyroidism, hypercalcemia, or delirium if the representative symptom is consciousness disorder and the corresponding syndrome name is metabolic consciousness disorder.

[0149] Furthermore, the column for the main pathological condition to be detected shown in matrix 700 may be set to, for example, intestinal obstruction, infectious enteritis, paralytic ileus, or duodenal stenosis if the representative symptom is nausea and the corresponding syndrome name is inability to drink water orally.

[0150] Furthermore, the column for the main pathological condition to be detected shown in matrix 700 may be set to, for example, intestinal obstruction, infectious enteritis, paralytic ileus, or duodenal stenosis if the representative symptom is loss of appetite and the corresponding syndrome name is inability to drink water orally, or drug-induced diarrhea if the representative symptom is loss of appetite and the corresponding syndrome name is severe diarrhea.

[0151] Furthermore, the column for the main pathological condition to be detected shown in matrix 700 may be set to, for example, pulmonary thromboembolism, cardiac failure, pericardial effusion, myocarditis, and myocardial damage when the representative symptom is dyspnea and the corresponding syndrome name is cardiac failure; pneumothorax, pulmonary edema, ARDS, airway obstruction, lymphangitis carcinomatosis, and superior vena cava syndrome when the representative symptom is dyspnea and the corresponding syndrome name is respiratory failure; and anaphylaxis, angioedema, infusion reaction, and urticaria when the representative symptom is dyspnea and the corresponding syndrome name is severe allergy.

[0152] Furthermore, the column for the main pathological condition to be detected shown in matrix 700 may be set to, for example, sepsis and febrile neutropenia when the representative symptom is fever and the corresponding syndrome name is bloodstream infection, or cholangitis, respiratory tract infection, pneumonia, urinary tract infection, and cellulitis when the representative symptom is fever and the corresponding syndrome name is acute organ infection.

[0153] Furthermore, the column for the main pathological condition to be detected shown in matrix 700 may be set to, for example, gastrointestinal ulcer or colitis if the representative symptom is bleeding and the corresponding syndrome name is gastrointestinal bleeding, airway bleeding if the representative symptom is bleeding and the corresponding syndrome name is hemoptysis, or DIC or tumor bleeding if the representative symptom is bleeding and the corresponding syndrome name is difficulty in stopping bleeding.

[0154] Furthermore, the column for the main pathological condition to be detected shown in matrix 700 may be set to, for example, lower limb venous thrombosis if the representative symptom is edema and the corresponding syndrome name is venous thrombosis, and pulmonary embolism, cardiac failure, pericardial effusion, myocarditis, and myocardial damage if the representative symptom is edema and the corresponding syndrome name is heart failure.

[0155] Furthermore, the column for the main pathology to be detected shown in matrix 700 may be set to, for example, cerebral infarction, Guillain-Barré syndrome, myasthenia gravis, leukoencephalopathy, or hypokalemia when the representative symptom is movement disorder and the corresponding syndrome name is neuromuscular disease. Furthermore, the column for the main pathology to be detected shown in matrix 700 may be set to, for example, arrhythmia when the representative symptom is palpitations and the corresponding syndrome name is cardiotoxicity, or hyperkalemia when the representative symptom is palpitations and the corresponding syndrome name is electrolyte abnormality.

[0156] Furthermore, the column for the main pathology to be detected shown in matrix 700 may be set to, for example, drug-induced lung injury, interstitial pneumonia, if the representative symptom is cough and the corresponding syndrome name is drug-induced lung injury.

[0157] Furthermore, the column of the main pathology to be detected shown in the matrix 700 may be set to "fundus hemorrhage" if the representative symptom is visual impairment and the corresponding syndrome name is acute visual impairment.

[0158] Furthermore, the column for the main pathology to be detected shown in matrix 700 may be set to drug-induced hearing loss if, for example, the representative symptom is hearing impairment and the corresponding syndrome name is acute hearing impairment.

[0159] In addition, the response column shown in the matrix 700 shown in Fig. 7 is set to a response selected from three levels of responses: Emergency, Vulnerable, and Prevention, depending on the urgency of the response for each syndrome. For example, as shown in Fig. 7, in the matrix 700, if the representative symptom is pain and the corresponding syndrome names are stroke, cardiovascular event, and acute abdomen, the response for each of these syndromes is Emergency.

[0160] For example, if the representative symptom shown in matrix 700 is fatigue and the corresponding syndrome name is neuromuscular disease, or if the representative symptom is impaired consciousness and the corresponding syndrome name is central nervous system disorder, or if the representative symptom is nausea and the corresponding syndrome name is inability to drink water orally, or if the representative symptom is loss of appetite and the corresponding syndrome name is inability to drink water orally, or if the representative symptom is dyspnea and the corresponding syndrome name is heart failure, respiratory failure, or severe allergy, or if the representative symptom is fever and the corresponding syndrome name is bloodstream infection, or if the representative symptom is bleeding and the corresponding syndrome name is gastrointestinal bleeding, hemoptysis, or difficulty in stopping bleeding, or if the representative symptom is edema and the corresponding syndrome name is heart failure, or if the representative symptom is movement disorder and the corresponding syndrome name is neuromuscular disease, the correspondence may be set to Emergency in each case.

[0161] Furthermore, if the representative symptom shown in matrix 700 is pain and the corresponding syndrome name is acute abdomen, the symptom is afebrile and the pain is mild, if the representative symptom is pain and the corresponding syndrome name is severe skin disease or spinal cord compression, if the representative symptom is fatigue and the corresponding syndrome name is renal failure, if the representative symptom is fatigue and the corresponding syndrome name is neuromuscular disease and the condition worsens on a weekly basis, if the representative symptom is fatigue and the corresponding syndrome name is jaundice, if the representative symptom is impaired consciousness and the corresponding syndrome name is metabolic impaired consciousness, if the representative symptom is nausea and the corresponding syndrome name is inability to drink water orally, if the representative symptom is loss of appetite and the corresponding syndrome name is inability to drink water orally and there is no vomiting, In the case where the representative symptom is dyspnea and the corresponding syndrome is cardiac failure and worsens on a weekly basis, or where the representative symptom is dyspnea and respiratory failure and worsens on a weekly basis, or where the representative symptom is fever and the corresponding syndrome is acute organ infection, or where the representative symptom is edema and the corresponding syndrome is cardiac failure and worsens on a weekly basis, or where the representative symptom is movement disorder and the corresponding syndrome is neuromuscular disease and worsens on a weekly basis, or where the representative symptom is palpitations and the corresponding syndrome is cardiotoxicity, electrolyte abnormality, or where the representative symptom is cough and the corresponding syndrome is drug-induced pulmonary injury, or where the representative symptom is visual impairment and the corresponding syndrome is acute visual impairment, the correspondence may be set to Vulnerable in each case.

[0162] Furthermore, when the representative symptom shown in matrix 700 is pain and the corresponding syndrome name is mucocutaneous disorder or peripheral neuropathy, when the representative symptom is fatigue and the corresponding syndrome name is decreased PS, when the representative symptom is loss of appetite and the corresponding syndrome name is severe diarrhea, when the representative symptom is edema and the corresponding syndrome name is venous thrombosis, or when the representative symptom is hearing impairment and the corresponding syndrome name is acute hearing impairment, the correspondence may be set to Precaution in each case.

[0163] 7, information on the location where each symptom is observed and the duration of the symptom may be associated with each syndrome. More specifically, if the representative symptom shown in matrix 700 is pain and the corresponding syndrome name is stroke, this means that the pain is observed in the head and that the pain is getting worse over time. Furthermore, when the representative symptom shown in matrix 700 is pain and the corresponding syndrome name is cardiovascular event, it means that the pain is felt in the chest and back and that the pain is getting worse over time; when the representative symptom is pain and the corresponding syndrome name is acute abdomen, it means that the pain is felt in the abdomen and that the pain is getting worse over time; when the representative symptom is pain and the corresponding syndrome name is severe skin disease, it means that the pain is felt on the body surface and that the pain is getting worse over time, that skin damage is felt over a wide area and that the skin damage is getting worse over time; when the representative symptom is pain and the corresponding syndrome name is spinal cord compression, it means that the pain is felt in the back and that the pain is getting worse over a week, and that movement disorder, numbness, and incontinence are getting worse over a week; when the representative symptom is pain and the corresponding syndrome name is mucocutaneous disorder, it means that the pain is felt on the body surface, that bleeding is felt in the mouth and hands and feet, and that the skin damage is getting worse over a week; and when the representative symptom is pain and the corresponding syndrome name is peripheral neuropathy, it means that the pain is felt in the hands and feet, and that numbness is getting worse over a week.

[0164] If the representative symptom is fatigue and the corresponding syndrome is renal failure, edema is observed throughout the body and the decrease in urine output is worsening on a weekly basis; if the representative symptom is fatigue and the corresponding syndrome is neuromuscular disease, pain is observed in the head and limbs and movement disorders are worsening on a weekly basis; if the representative symptom is fatigue and the corresponding syndrome is jaundice, yellowing of the skin is worsening on a weekly basis; and if the representative symptom is fatigue and the corresponding syndrome is decreased PS, lethargy, drowsiness, nausea, and loss of appetite are worsening on a weekly basis.

[0165] When the representative symptom is a disturbance in consciousness and the corresponding syndrome name is a central nervous system disorder, the pain is felt in the head, the pain is felt daily, and the drowsiness and nausea worsen daily; when the representative symptom is a disturbance in consciousness and the corresponding syndrome name is a metabolic disturbance in consciousness, the pain is felt in the abdomen, and the fatigue and drowsiness worsen weekly.

[0166] If the main symptom is nausea and the corresponding syndrome name is inability to drink orally, it means that the pain is felt in the abdomen and the nausea and loss of appetite are getting worse day by day.If the main symptom is loss of appetite and the corresponding syndrome name is inability to drink orally, it means that the pain is felt in the abdomen and the nausea and loss of appetite are getting worse day by day; if the main symptom is loss of appetite and the corresponding syndrome name is severe diarrhea, it means that the pain is felt in the abdomen.

[0167] When the representative symptom is dyspnea and the corresponding syndrome is cardiac failure, the area where pain is felt is the chest, shortness of breath is felt over a period of weeks, hemoptysis is felt, edema is felt throughout the body, and the edema worsens over a period of weeks; when the representative symptom is dyspnea and the corresponding syndrome is respiratory failure, the area where pain is felt is the chest, shortness of breath is felt over a period of weeks, and edema is felt in the face; and when the representative symptom is dyspnea and the corresponding syndrome is severe allergy, the area where pain is felt is the abdomen, shortness of breath is felt over a period of hours, and edema is felt in the face and throughout the body.

[0168] If the main symptom is fever and the corresponding syndrome is bloodstream infection, it means that the fever is getting worse over the days; if the main symptom is fever and the corresponding syndrome is acute organ infection, it means that the pain is felt in the chest, abdomen, or limbs and the fever is getting worse over the days; if the main symptom is bleeding and the corresponding syndrome is gastrointestinal bleeding, it means that the pain is felt in the abdomen and vomiting blood and bloody stool are getting worse over the days; if the main symptom is bleeding and the corresponding syndrome is hemoptysis, it means that hemoptysis is getting worse over the days; if the main symptom is bleeding and the corresponding syndrome is difficult to stop bleeding, it means that the bleeding is not stopping over the days.

[0169] If the representative symptom is edema and the corresponding syndrome is venous thrombosis, the pain is felt in the limbs and the edema worsens daily in the face and limbs; if the representative symptom is edema and the corresponding syndrome is heart failure, the pain is felt in the chest, shortness of breath is felt weekly, hemoptysis is felt, and the edema worsens throughout the body weekly. If the representative symptom is movement disorder and the corresponding syndrome is neuromuscular disease, the pain is felt in the head and limbs and the movement disorder worsens weekly.

[0170] If the main symptom is palpitations and the corresponding syndrome is cardiotoxicity, edema is observed throughout the body and the palpitations are getting worse every week. If the main symptom is palpitations and the corresponding syndrome is electrolyte abnormalities, the palpitations are getting worse every week. If the main symptom is cough and the corresponding syndrome is drug-induced lung injury, the cough is getting worse every week.

[0171] If the main symptom is visual impairment and the corresponding syndrome name is acute visual impairment, it means that the visual impairment is getting worse every week. If the main symptom is hearing impairment and the corresponding syndrome name is acute hearing impairment, it means that the hearing impairment is getting worse every week.

[0172] 8(a) and 8(b) will be described. FIG. 8(a) shows an example of a matrix 800 for predicting cancer and the stage of cancer based on the predicted syndrome and one or more stages of symptom worsening determined by the symptom worsening determination unit 130c for a specific user, and FIG. 8(b) shows an example of a matrix 810 for determining the recommendation strength associated with the recommendation information presented to the user by the notification unit 140. Regarding the stage of symptom worsening, in the example of FIG. 8(a), referring to the horizontal axis of the matrix 800, the symptom worsening has three stages: "slight worsening," "worsening," and "significant worsening." On the other hand, in the example of FIG. 8(b), referring to the horizontal axis of the matrix 810, the symptom worsening has one stage: "worsening."

[0173] FIG. 8( a) shows an example of a matrix 800 that predicts the progression of cancer for a specific user based on the predicted syndrome and the worsening of symptoms. As shown in FIG. 8( a), the matrix 800 has information on the predicted syndrome on the vertical axis and information on the worsening of symptoms on the horizontal axis, thereby associating the syndrome type with the worsening of symptoms and predicting the progression of cancer. Since the matrix 800 shown in FIG. 8( a) predicts the progression of cancer for the specific user based on the predicted syndrome and the worsening of symptoms, the progression prediction unit 130e may not predict the progression of cancer for syndromes and the worsening of symptoms that the specific user was not predicted to have, and these columns are left blank.

[0174] By using the matrix 800 shown in FIG. 8( a), the progression prediction unit 130e can predict that if the predicted syndrome is acute abdominal syndrome and the degree of worsening is slight, the cancer is at stage I. Here, the progression prediction unit 130e may be able to predict the degree of cancer progression only from information on the degree of symptom worsening. In this case, the matrix 800 shows information on various pre-set symptoms on the vertical axis instead of syndromes.

[0175] The progression prediction unit 130e predicts cancer and its progression based on various symptoms and the degree of symptom worsening. The matrix 800 shown in FIG. 8( a) may predict only the progression of cancer. In this case, the notification unit 140 extracts recommendation information to be presented to the user based on the progression of cancer. The notification unit 140 can notify the user of the progression information indicating the progression of cancer and the recommendation information. Here, the recommendation information includes information recommending at least one of a medical consultation and self-care. In this specification, information recommending a medical consultation may be referred to as medical consultation recommendation information, and information recommending self-care may be referred to as self-care recommendation information.

[0176] As shown in FIG. 8A , for example, if the predicted syndrome is “acute abdominal syndrome” and the acute abdominal syndrome is judged to be “slightly worsening,” the notification unit 140 notifies the patient with information indicating the following: ____ cancer (where ____ indicates an organ such as the colon or stomach) and the progression level (where ____ indicates a stage such as I). For example, if the predicted syndrome is “acute abdominal syndrome” and the acute abdominal syndrome is judged to be “slightly worsening,” the notification unit 140 notifies the patient with “____ cancer” and the progression level “Stage II.” If the predicted syndrome is “acute abdominal syndrome” and the acute abdominal syndrome is judged to be “worsening,” the notification unit 140 notifies the patient with “____ cancer” and the progression level “Stage III.” If the predicted syndrome is “acute abdominal syndrome” and the acute abdominal syndrome is judged to be “significantly worsening,” the notification unit 140 notifies the patient with “____ cancer” and the progression level “Stage IV.” Note that “Stage II” may simply be expressed as “II.”

[0177] FIG. 8( b) illustrates an example of a matrix 810 used by the notification unit 140 to present recommendation information to a user based on a predicted syndrome and the severity of the symptoms for a specific user. As illustrated in FIG. 8( b), the matrix 810 associates the type of predicted syndrome with the severity of the symptoms. The notification unit 140 uses the matrix 810 illustrated in FIG. 8( b) to determine a recommendation strength and presents recommendation information associated with the determined recommendation strength to the user. The matrix 810 illustrated in FIG. 8( b) may determine only the recommendation strength. The notification unit 140 may also present the severity of the symptoms to the user. Since the matrix 810 illustrated in FIG. 8( b) determines the recommendation strength based on the predicted syndrome and the severity of the symptoms for the specific user, the notification unit 140 may not determine the recommendation strength for a syndrome and the severity of the symptoms that the specific user was not predicted to suffer from, and the corresponding square in the matrix 810 may be left blank.

[0178] As shown in FIG. 8( b), for example, when the syndrome predicted for the user is "acute abdominal syndrome," the notification unit 140 determines the rank corresponding to the recommendation strength as rank A, and extracts recommendation information associated with rank A from the storage unit 150. The notification unit 140 presents the recommendation information associated with the extracted rank A to the user. Note that the notification unit 140 can also present the degree of worsening of symptoms to the user.

[0179] 8( a) and 8(b) , the matrix 800, 810 may have a syndrome column that includes, for example, stroke, cardiovascular event, acute abdomen, severe skin disease, spinal cord compression, mucocutaneous injury, peripheral neuropathy, renal failure, neuromuscular disease, jaundice, decreased PS, central nervous system disorder, metabolic disturbance, inability to drink water orally, severe diarrhea, cardiac failure, respiratory failure, severe allergy, bloodstream infection, acute organ infection, gastrointestinal bleeding, hemoptysis, difficulty in hemostasis, venous thrombosis, cardiotoxicity, electrolyte abnormality, drug-induced lung injury, acute visual impairment, acute hearing impairment, and the like.

[0180] FIG. 9( a) is a diagram showing an example of a matrix 900 by which the notification unit 140 presents recommendation information to the user based on the predicted cancer and the cancer progression. As shown in FIG. 9, the matrix 900 is a matrix that associates the cancer progression with the cancer type. The notification unit 140 determines the recommendation strength using the matrix 900 shown in FIG. 9( a), and presents the recommendation information associated with the determined recommendation strength to the user. Note that in this case, the notification unit 140 can also notify the user of progression information indicating the cancer progression predicted by the progression prediction unit 130e.

[0181] For example, if the user's predicted or present cancer is colon cancer and the stage of progression is stage I, the notification unit 140 determines the rank corresponding to the recommendation strength to be rank B, and extracts recommendation information corresponding to rank B from the storage unit 150. In this case, the notification unit 140 notifies the user of information indicating that the stage is I as the progression information, and the recommendation information corresponding to rank B.

[0182] Therefore, as described above, the notification unit 140 may determine the recommendation strength from the matrix 900 shown in Fig. 9(a) based on the cancer and the stage of cancer predicted by the stage prediction unit 130e from the matrix 800 shown in Fig. 8(a), or the notification unit 140 may determine the recommendation strength from the matrix 810 shown in Fig. 8(b). The notification unit 140 extracts recommendation information associated with the determined recommendation strength from the storage unit 150 and presents the recommendation information associated with the extracted recommendation strength to the user.

[0183] 9(b) is a diagram showing a data configuration 910 in which consultation recommendation information among the recommendation information presented to the user is associated with a rank corresponding to the strength of the consultation recommendation. There are four ranks, A to D, and consultation recommendation information corresponding to each rank is stored in the storage unit 150. For example, as shown in FIG. 9(b), if the rank corresponding to the consultation recommendation information presented to the user is B, consultation recommendation information stating "We recommend that you call a hospital within one week" is presented to the user.

[0184] FIG. 9C shows a data structure 920 in which self-care recommendation information among the recommendation information presented to the user is associated with a rank corresponding to the strength of the self-care recommendation. There are four ranks, A to D, and self-care recommendation information corresponding to each rank is stored in the storage unit 150. For example, as shown in FIG. 9C, if the rank corresponding to the self-care recommendation information presented to the user is A, the self-care recommendation information presented to the user reads, "Try self-care of XX. If your symptoms do not improve, we recommend that you call a hospital." Here, the "Try self-care of XX" portion is a portion in which appropriate self-care information corresponding to the syndrome is presented to the user. Specifically, for example, if the syndrome is "impossible to drink water orally," the self-care recommendation information presented to the user reads, "Try dividing food into small portions." For example, if the syndrome is "severe diarrhea," the part "Try self-care of XX" will be presented to the user as self-care recommendation information such as "Avoid cold foods and drinks and spicy foods."

[0185] FIG. 10( a) is a diagram showing a screen example 1000 when consultation recommendation information among the recommendation information is presented on the user's information terminal 102. As shown in FIG. 10( a), the consultation recommendation information and hospital contact information are displayed on the user's information terminal 102. However, the screen example 1000 is not limited to the form shown in FIG. 10( a). For example, self-care recommendation information may be presented on the screen example 1000 instead of the consultation recommendation information of FIG. 10( a). Furthermore, for example, self-care recommendation information may be presented on the screen example 1000 in addition to the consultation recommendation information of FIG. 10( a).

[0186] The user's information terminal 102 may be presented with the stage information indicating the stage of cancer predicted by the stage prediction unit 130e, as well as the recommendation information extracted by the notification unit 140 from the storage unit 150. Furthermore, when the user inputs symptom information, the hospital may also input information about the user's primary care physician. Furthermore, when the user inputs symptom information, the notification unit 140 may also input information about the user's address, and present information about the hospital closest to the address.

[0187] 10(b) is a diagram showing an example screen 1010 when the notification unit 140 presents the medical professional with information regarding the probability of hospital visit and the probability of hospitalization calculated based on the stage of cancer after presenting recommendation information to the user. As shown in FIG. 10(b), the notification unit 140 presents information regarding the user's ID and name to the information terminal 103 of the medical professional, as well as information regarding the user's probability of hospital visit and the probability of hospitalization. Note that the example screen 1010 is not limited to the form shown in FIG. 10(b).

[0188] FIG. 11 is a diagram showing a data configuration 1100 in which a user's name, the date and time when the input of symptom information was accepted, a user ID set by the user, and a management ID issued to the user by the server device 101 are associated with each other. The user can set the user ID when the application execution unit 102e executes an application related to the cancer progression-related information notification system 100. For example, as shown in FIG. 11 , Mr. Z sets his user ID to ZZZZZZZZ. Also, as shown in FIG. 11 , at 5:05 PM on January 1, 2022, the cancer progression-related information notification system 100 accepts input of symptom information from user Z with user ID ZZZZZZZZZ, and the server device 101 issues a management ID of 00000001.

[0189] When the user's information terminal 102 transmits the symptom severity score information to the server device 101, the server device 101 issues a management ID corresponding to the user ID. This allows the information transmitted from the user's information terminal 102 to be managed in the server device 101. "Management" refers to the storage of information for each user in the storage unit 150 in association with each user.

[0190] Furthermore, when the receiving unit 110 receives information on the degree of symptoms from a user, the information on the date and time of reception is transmitted to the storage unit 150. The storage unit 150 stores the user ID, the management ID corresponding to the user ID, and the date and time of reception of the input of information on the degree of symptom, and can transmit the stored information when the symptom fluctuation degree determining unit 130b and the symptom worsening degree determining unit 130c execute a determination process.

[0191] In addition to the information shown in FIG. 11 , the storage unit 150 can also store cancer progression information indicating the cancer progression level and recommendation information previously presented to the user. This allows the cancer progression-related information notification system 100 to collectively manage information such as the date and time when information on the degree of symptoms was received from the user, and the date and time when the progression information and recommendation information were presented. Furthermore, the server device 101 can also transmit progression information and recommendation information to the information terminal 102 of the user with the user ID corresponding to the management ID from the next time onwards. This makes it possible to prevent, for example, progression information and recommendation information intended for one user from being mistakenly sent to another user.

[0192] For example, in the cancer progression-related information notification system 100, the user may input basic information such as the user's name, address, and telephone number, along with information about a history of cancer, current family doctor, and medication information, before inputting symptom information. In this case, the user may set the user ID themselves, and an ID that has already been set may not be set in the cancer progression-related information notification system 100. This allows the cancer progression-related information notification system 100 to manage information about the user and prevent problems such as erroneous transmission of information to other users.

[0193] Fig. 12(a) is a diagram showing an example of a feedback format 1200 for providing a user with feedback regarding the cancer progression-related information notification system 100 after the user has been presented with progression information and recommendation information. As shown in Fig. 12(a), questions are set in advance in the feedback format 1200, and the evaluation of each question is indicated by a score.

[0194] For example, if the user feels that the stage information and recommendation information presented by the cancer stage related information notification system 100 are completely inappropriate, the user may check the box marked 1, indicating that the information is not applicable. After the user has finished inputting the feedback information, the user transmits the feedback information to the server device 101.

[0195] For example, a send button may be provided at the bottom of the feedback format 1200, and clicking the button may automatically send the input content to the server device 101. Note that the feedback format 1200 is not limited to the form shown in FIG. 12(a).

[0196] For example, the feedback format 1200 may be in the form of a user responding in writing to a question preset for feedback. The receiving unit 110 receives input of feedback information from the user. The received information is transmitted to the notification unit 140. When the user responds in writing, the notification unit 140 may modify the recommendation strength of the recommendation information based on, for example, the number of characters or the number of times a specific keyword is used.

[0197] 12(b) is a diagram showing a matrix 1210 for correcting the next progress information and recommendation information based on the feedback information when the server device 101 receives input of feedback information from the user. As shown in FIG. 12(b), the vertical axis of the matrix 1210 indicates the rank of the recommendation information, and the horizontal axis indicates the total score of the feedback received from the user.

[0198] For example, if the total score calculated from the feedback information received from the user is 14 and the rank corresponding to the recommendation information presented to the user by the notification unit 140 is B, the rank of the recommendation information to be presented to the user next time will be raised by one rank. Here, the rank corresponding to the recommendation information to be presented to the user may be set to A as the lowest rank and D as the highest rank. In this case, raising by one rank means changing the rank from A to B. The notification unit 140 presents the recommendation information with the corrected rank to the user. Note that the rank is not limited to a four-level evaluation as shown in FIG. 12(b).

[0199] For example, the rank corresponding to the recommendation information presented to the user may be a five-point scale from A to E. Furthermore, if the recommendation information can be divided into several types of message information and different messages can be used depending on the stage of cancer, it is not necessary to use ranks.

[0200] Furthermore, the method of correcting the recommendation intensity of the recommendation information using the matrix 1210 shown in FIG. 12( b) is not limited to the above-described form. For example, if the total score of specific input items in the feedback information is below a predetermined threshold, the rank may be raised from B to C, and if the total score exceeds the predetermined threshold, the rank may be lowered from B to A. For example, if no new symptoms are added to the user's symptom information entered through the input screen 500 for one month, the recommendation intensity of the recommendation information may be lowered by one level. In this way, the recommendation intensity of the recommendation information may be corrected while setting a certain time limit, limited period, etc. Here, the symptom score tallying unit 130 a may, for example, calculate the total value from the feedback information, and the symptom score tallying unit 130 a may also calculate the total value of the points related to specific input items in the feedback information. In this case, the information on the total feedback value calculated by the symptom score tallying unit 130 a is transmitted to the notification unit 140, for example. Furthermore, the method for correcting the recommendation intensity of the recommendation information may be any method as long as it ultimately gives the user a certain level of satisfaction and understanding with the cancer progression stage related information notification system 100.

[0201] Note that this embodiment is not limited to the above-described aspects, and may be realized by appropriately combining the above-described units. Furthermore, the effects of each aspect are not limited to those described above. For example, the notification unit 140 is not limited to correcting the recommendation information when the reception unit 110 receives feedback information once, but may also be corrected when the feedback information is received a predetermined number of times and a predetermined criterion is satisfied.

[0202] For example, the symptom fluctuation degree determining unit 130b, the symptom worsening degree determining unit 130c, the syndrome predicting unit 130d, and the progression predicting unit 130e may each predict the symptom fluctuation, the symptom worsening degree, the relevant syndrome, the type of cancer, and the progression of cancer based on a learning model trained using teacher data including information about the past symptoms of cancer patients, the type of cancer, the progression of cancer, and information about whether or not the patient has been hospitalized. Furthermore, the control unit 130 may use values ​​determined using a predetermined formula when executing the prediction and determination processes.

[0203] In the above embodiment, each functional unit of the server device 101, the user's information terminal 102, and the medical professional's information terminal 103 related to the cancer progression-related information notification system 100 (cancer symptom worsening notification system) may be realized by a logic circuit (hardware) formed on an integrated circuit (IC (Integrated Circuit) chip, LSI (Large Scale Integration)), or a dedicated circuit, or may be realized by software using a CPU (Central Processing Unit) and memory. Furthermore, each functional unit may be realized by one or more integrated circuits, or the functions of multiple functional units may be realized by a single integrated circuit. LSIs may also be referred to as VLSIs, super LSIs, ultra LSIs, etc. depending on the degree of integration. Here, the term "circuit" may also refer to digital processing by a computer, i.e., functional processing by software. Furthermore, the circuit may be realized by a reconfigurable circuit (for example, FPGA: Field Programmable Gate Array).

[0204] When the functional units of the server device 101, user information terminal 102, and medical professional information terminal 103 of the cancer progression-related information notification system 100 (cancer symptom worsening notification system) are implemented by software, the server device 101 of the cancer progression-related information notification system 100 includes a CPU that executes instructions from an input program, which is software that implements each function; a ROM (read-only memory) or storage device (these are referred to as "recording media") in which the input program and various data are recorded in a computer-readable format; and a RAM (random access memory) in which the input program is expanded. The object of the present invention is achieved by the computer (or CPU) reading and executing the input program from the recording media. The recording media may be "non-transitory tangible media," such as tapes, disks, cards, semiconductor memories, programmable logic circuits, etc. The input program may also be supplied to the computer via any transmission medium capable of transmitting the input program (e.g., a communication network or broadcast waves). The present invention can also be realized in the form of a data signal embedded in a carrier wave in which the input program is embodied by electronic transmission.

[0205] The input program can be implemented using, for example, a scripting language such as ActionScript or JavaScript (registered trademark), an object-oriented programming language such as Objective-C or Java (registered trademark), or a markup language such as HTML5, but is not limited to these.

[0206] 100 Cancer progression level related information notification system (cancer symptom worsening degree notification system) 101 Server device 110 Reception unit 120 Communication unit 130 Control unit 130a Symptom score aggregation unit 130b Symptom fluctuation degree determination unit 130c Symptom worsening degree determination unit 130d Syndrome prediction unit 130e Progression prediction unit 140 Notification unit 150 Memory unit 102 User's information terminal 102a Communication unit 102b Reception unit 102c Input unit 102d Control unit 102e Application execution unit 102f Memory unit 103 Medical professional's information terminal 103a Communication unit 103b Reception unit 103c Input unit 103d Control unit 103e Application execution unit 103f Memory unit 104 Distribution server device 104a Communication unit 104b Input unit 104c Control unit 104d Storage unit 104e Distribution unit

Claims

1. A cancer symptom worsening notification system comprising: a user information terminal used by a user; and a server device capable of communicating with the user information terminal, the server device comprising: a reception unit that receives user input of information indicating the degree of symptom for various symptoms; a symptom score aggregation unit that aggregates the received information; a symptom fluctuation degree determination unit that determines the degree of symptom fluctuation based on the aggregated information; a symptom worsening degree determination unit that determines the degree of symptom worsening based on the determined degree of symptom fluctuation; and a notification unit that notifies the user of the determined degree of symptom worsening.

2. The cancer symptom worsening notification system described in claim 1, characterized in that the notification unit presents to the user recommendation information recommending at least one of medical examination and self-care based on the determined degree of symptom worsening.

3. The cancer symptom worsening notification system described in claim 1, characterized in that the server device is provided with a cancer progression prediction unit that predicts the cancer progression based on the determined degree of symptom worsening, and the notification unit presents the user with recommendation information recommending at least one of medical examination and self-care based on the predicted degree of cancer progression.

4. The cancer symptom worsening notification system according to claim 3, characterized in that the progression prediction unit predicts the progression of a cancer selected from a group of solid cancers including colorectal cancer, gastric cancer, lung cancer and breast cancer.

5. The system for notifying the worsening of cancer symptoms described in claim 3, characterized in that the user's information terminal is equipped with an application execution unit that executes an application received from the server device, and the stage of cancer and the recommendation information are displayed in the executed application.

6. A cancer symptom worsening notification system as described in claim 3, further comprising a syndrome prediction unit which predicts a corresponding syndrome based on the determined degree of worsening of the symptoms, wherein the progression prediction unit predicts the degree of progression of the cancer based on the predicted syndrome.

7. The cancer symptom worsening notification system described in claim 1, characterized in that the symptom fluctuation degree determination unit further determines the degree of symptom fluctuation based on information regarding the user's cancer history and history of anticancer drug use.

8. A cancer symptom worsening notification system as described in claim 3, characterized in that the progression prediction unit predicts the progression of the cancer based on image diagnosis results.

9. A cancer symptom worsening notification system as described in claim 3, characterized in that the progression prediction unit predicts the possibility of cancer metastasis.

10. A cancer symptom worsening notification system as described in claim 2, further comprising an information terminal of a medical professional at a medical institution where the user receives medical services, the information terminal being capable of communicating with the server device, and the notification unit presenting information about the user to the medical professional's information terminal based on the worsening degree of the symptoms.

11. A cancer symptom worsening notification system as described in claim 3, further comprising an information terminal of a medical professional at a medical institution where the user receives medical services, the information terminal being capable of communicating with the server device, and the notification unit presenting information about the user on the medical professional's information terminal based on the progression of the cancer.

12. The cancer symptom worsening notification system described in claim 2 or 3, characterized in that the reception unit receives input of feedback information from a user, and the notification unit modifies the recommendation information based on the received feedback information and presents the modified recommendation information to the user.

13. The cancer symptom worsening notification system described in claim 2 or 3, characterized in that the notification unit modifies the recommendation information based on information regarding the user's accompanying symptoms, drug classification, combination, dosage, administration time, administration method, administration order, and administration date, and presents the modified recommendation information to the user.

14. A method for notifying the worsening of cancer symptoms, in which a computer executes the following steps: a reception step for receiving user input of information indicating the severity of various symptoms; a symptom score aggregation step for aggregating the received information; a symptom fluctuation degree determination step for determining the degree of symptom fluctuation based on the aggregated information; a symptom worsening degree determination step for determining the degree of symptom worsening based on the degree of symptom fluctuation; and a notification step for notifying a user of the determined degree of symptom worsening.

15. A cancer symptom worsening notification program that implements in a computer the following functions: a reception function that receives user input of information indicating the severity of various symptoms; a symptom score aggregation function that aggregates the received information; a symptom fluctuation degree determination function that determines the degree of symptom fluctuation based on the aggregated information; a symptom worsening degree determination function that determines the degree of symptom worsening based on the degree of symptom fluctuation; and a notification function that notifies the user of the determined degree of symptom worsening.

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