Information Processing System

Abstract

To provide an information processing system for supporting a selection of a patient to be a candidate of a trial target.SOLUTION: An information processing system for supporting a selection of a patient to be a candidate of a trial target includes: a structure processing unit for structuring text information input to a predetermined screen; and a candidate processing unit for determining a patient to be a candidate of a trial target by using the information obtained by structuring the input text information and information of the reference of the trial.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention is an information processing system that supports the selection of patients who are candidates for clinical trial subjects. [Background technology]

[0002] When developing a new drug, clinical trials are conducted to confirm the efficacy of the new drug and whether or not there are any side effects. In clinical trials, the doctor in charge of the clinical trial (clinical trial investigator) selects patients who meet the criteria for the clinical trial as candidate subjects based on criteria set in advance by the pharmaceutical company. The clinical trial is then explained to the candidate patients, and after their consent is obtained, an examination is conducted by an ethics committee within the medical institution, and if approved, the clinical trial is conducted with the patient as a clinical trial subject.

[0003] Therefore, when a clinical trial is conducted on a new drug, the investigator will select candidate patients for the clinical trial by comparing the information recorded in the patient's medical record (e.g., electronic medical record) with the information in the guidelines that contain the criteria for clinical trials.

[0004] Conventionally, the selection of patients who are candidates for clinical trial subjects has been done manually by the clinical trial doctor, but because this work is burdensome, attempts have been made to systematize it. One example of a system for selecting clinical trial subjects is the following Patent Document 1. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] JP 2014-194595 A Summary of the Invention [Problem to be solved by the invention]

[0006] The standards in the guidelines published by pharmaceutical companies when conducting clinical trials generally include "clinical diagnosis," "age," "presence or absence of comorbid diseases (medical history)," "presence or absence of regularly taken medications," "ADL (activity level) before the onset of the disease," "significance of the disease (scoring)," and "imaging findings." As most of this information is recorded in the patient's medical records, when the clinical trial physician selects patients who are candidates for clinical trial subjects, he or she refers to the information in the patient's medical records. Note that the clinical trial physician is often not the patient's doctor.

[0007] One of the most important criteria for clinical trials is the "clinical diagnosis." However, in many cases, the clinical diagnosis (clinical disease name) is not recorded in the medical record. Even if it is recorded in the medical record, it is often not immediately clear where the clinical diagnosis is recorded.

[0008] This is because there is no obligation to record the name of a clinical diagnosis in a medical record. Medical records are broadly divided into a section for displaying and recording standard information such as the patient's attributes, a free entry section for displaying and recording information obtained by the doctor when examining the patient and the doctor's own findings, and an ordering section for displaying and recording information on instructions for the patient, such as treatment, examinations, and prescribed medications.

[0009] The free-entry section of the medical record is a display and recording section in which the doctor can freely and unrestrictedly enter information and findings he or she has heard from the patient in free text format, and so the doctor may or may not enter the name of a clinical diagnosis there. Even if the doctor enters a clinical diagnosis, there is no rule as to where in the free-entry section the doctor enters it, and it is at the discretion of each doctor. In this way, since the free-entry section is, so to speak, a memorandum for the doctor, the doctor often writes in a way that the doctor can understand, and the clinical diagnosis is not necessarily expressed accurately, and may be entered as an abbreviation, etc., as appropriate.

[0010] Therefore, while a doctor in charge of a patient can immediately identify whether or not a patient has a clinical diagnosis based on the criteria of a clinical trial, when the doctor in charge of the clinical trial is not the doctor in charge of the patient, if the doctor in charge of the clinical trial views the patient's medical record, he or she must search for the clinical diagnosis written in the free-form entry field of the medical record or infer the clinical diagnosis by reading and interpreting the information written in the free-form entry field of the medical record. However, such work is a heavy burden for busy doctors in charge of clinical trials.

[0011] In addition, since many medical institutions have introduced electronic medical records as medical records, it is possible to search for the target disease name by matching the electronic medical record with the clinical trial criteria in the pharmaceutical company's guidelines. However, conventional electronic medical records simply store text information entered in the free-text field of the electronic medical record, so even if you try to search for and match the corresponding disease name in the electronic medical record using the clinical diagnosis name in the clinical trial criteria as a search key, it is not possible to distinguish whether it is the patient's current clinical diagnosis name or a clinical diagnosis name from the patient's medical history.

[0012] Therefore, even in this case, the clinical trial physician will still need to interpret the information in the electronic medical records.

[0013] Assuming that matching will be performed, asking doctors to enter patients' clinical diagnoses, symptoms, and commonly used medications in standardized language into electronic medical records places a heavy burden on doctors. This burden is particularly heavy for doctors in emergency rooms where every minute counts. Furthermore, doctors want to be able to fill out electronic medical records in their own style, so asking them to enter information in standardized language and sentences places a psychological burden on them.

[0014] In addition, the ordering section of the electronic medical record contains information on the receipt for insurance claims, and the receipt lists the diagnosis given to the patient. However, the diagnosis listed on the receipt is a diagnosis (insurance diagnosis, insurance disease) given for the convenience of insurance claims, and does not necessarily match the clinical diagnosis determined by the doctor to identify the patient's actual illness. Therefore, the patient's actual illness (clinical diagnosis) cannot necessarily be determined by simply referring to the receipt.

[0015] Furthermore, when selecting potential clinical trial subjects based on a clinical diagnosis, patients cannot be selected as potential clinical trial subjects until they have a confirmed clinical diagnosis, and therefore cannot be applied to the selection of potential clinical trial subjects for new drugs for acute illnesses.

[0016] For example, when a patient complaining of symptoms such as paralysis, numbness, or limb weakness is transported by ambulance to an emergency room, a definitive clinical diagnosis, such as cerebral infarction, is not determined for that patient until several hours after transport to the medical institution. Until then, the patient has not been given a definitive clinical diagnosis. On the other hand, when selecting candidates for clinical trials of a new drug for acute diseases such as cerebral infarction, it is necessary to select whether the patient can be a candidate for a clinical trial immediately after transport, but conventional systems cannot handle this.

[0017] Therefore, when selecting candidates for clinical trials of new drugs for acute illnesses, emergency room doctors can voluntarily contact the clinical trial physician, so that the clinical trial physician can become aware of the existence of candidate patients, but this places a heavy burden on the emergency room doctor. [Means for solving the problem]

[0018] In view of the above problems, the present inventors have invented an information processing system that supports the identification of patients who are candidates for clinical trial subjects.

[0019] A first invention is an information processing system that supports the selection of patients who are candidates for clinical trial subjects, the information processing system having a structuring processing unit that structures text information input to a predetermined screen, and a candidate processing unit that identifies patients who are candidates for clinical trial subjects using information obtained by structuring the input text information and information on clinical trial criteria, The candidate processing unit identifies patients who are candidates for the clinical trial using a part of the information on the clinical trial criteria, excluding at least a clinical diagnosis name from the information on the clinical trial criteria, and identifies patients who are candidates for the clinical trial by excluding from the candidates patients whose clinical diagnosis name includes a clinical diagnosis name other than the clinical diagnosis name in the information on the clinical trial criteria. It is an information processing system.

[0020] Since the free-text field of the electronic medical record is an input field in which the doctor freely inputs text, there is variation in the notation of clinical diagnosis names, symptoms, etc. Therefore, simply matching the information input in the free-text field of the electronic medical record with the information of the clinical trial criteria does not allow appropriate identification of patients who are candidates for clinical trial subjects. Therefore, as in the present invention, by structuring the text information input in the free-text field of the electronic medical record or the input screen of the input support processing unit, it is possible to appropriately determine differences in the attributes of the disease, such as whether it is a clinical diagnosis name or a pre-existing condition, and identify patients who are candidates for clinical trial subjects. In addition, since the input method is not changed when inputting, the freedom of the doctor to input text information is not impaired. When selecting patients who are candidates for clinical trials, they are usually selected from patients who have been assigned a confirmed clinical diagnosis to which the new drug is applicable. Therefore, patients who are candidates for clinical trials are selected using a confirmed clinical diagnosis, and patients who have not been assigned a confirmed clinical diagnosis are not selected. However, in the case of acute illnesses, it may be desirable to administer a new drug as part of a clinical trial before a confirmed clinical diagnosis is assigned. Therefore, by using these inventions, it is possible to appropriately select patients who are candidates for clinical trials before a confirmed clinical diagnosis is assigned.

[0021] In the above-mentioned invention, the information processing system can be configured as an information processing system having a notification processing unit that outputs, on a screen displaying information about a patient identified by the candidate processing unit, an indication that the patient is a candidate for a clinical trial.

[0022] A third invention is an information processing system that supports the selection of patients who are candidates for clinical trial subjects, the information processing system having a candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered on a specified screen and information on clinical trial criteria, and a notification processing unit that outputs, on a screen displaying information about the identified patient, an indication that the patient is a candidate for the clinical trial; The candidate processing unit identifies patients who are candidates for the clinical trial using a part of the information on the clinical trial criteria, excluding at least a clinical diagnosis name from the information on the clinical trial criteria, and identifies patients who are candidates for the clinical trial by excluding from the candidates patients whose clinical diagnosis name includes a clinical diagnosis name other than the clinical diagnosis name in the information on the clinical trial criteria. It is an information processing system.

[0023] As in these inventions, when a patient who is a candidate for a clinical trial is identified, it is preferable to output an indication that the patient is a candidate for the clinical trial when displaying the patient's information on a specified screen. This allows the doctor in charge of the examination to recognize that the patient is a clinical trial subject. When selecting patients who are candidates for clinical trials, they are usually selected from patients who have been assigned a confirmed clinical diagnosis to which the new drug is applicable. Therefore, patients who are candidates for clinical trials are selected using a confirmed clinical diagnosis, and patients who have not been assigned a confirmed clinical diagnosis are not selected. However, in the case of acute illnesses, it may be desirable to administer a new drug as part of a clinical trial before a confirmed clinical diagnosis is assigned. Therefore, by using these inventions, it is possible to appropriately select patients who are candidates for clinical trials before a confirmed clinical diagnosis is assigned.

[0024] In the above-mentioned invention, the notification processing unit can be configured as an information processing system that displays information related to the clinical trial on a screen that displays information about the patient identified by the candidate processing unit.

[0025] The notification processing unit may display information related to the clinical trial, so that, for example, a doctor who examined the patient can immediately contact the clinical trial coordinator or the clinical trial physician if he or she has any questions about the clinical trial.

[0026] In the above-mentioned invention, the notification processing unit can be configured as an information processing system that displays a screen showing information about patients identified by the candidate processing unit, including the criteria for the clinical trial and an input field for indicating whether the criteria are met, and notifies a designated person of the information entered into the input field and information about patients who are candidates for clinical trial subjects.

[0027] When patients who are candidates for clinical trial subjects are automatically selected, there is a possibility that the selection will be incorrect. Therefore, as in the present invention, by displaying an input field to check whether the criteria for the clinical trial are met, a doctor can check and the accuracy can be improved. In addition, by notifying a designated person such as the clinical trial doctor after inputting the information into the input field, the accuracy of patient selection can be improved.

[0028] In the above-mentioned invention, the notification processing unit can be configured as an information processing system that produces different outputs: an output indicating that the person is a candidate for a clinical trial subject based on their identification by the candidate processing unit, and an output indicating that the person is a candidate for a clinical trial subject after entry has been made into the input field.

[0029] According to the present invention, it is possible to easily distinguish whether a patient is a candidate for a clinical trial who has been automatically selected or a candidate for a clinical trial whose intention has been confirmed.

[0032] In the above-mentioned invention, the candidate processing unit can be configured as an information processing system that determines whether or not the patient has been given a definite clinical diagnosis, and if the patient has been given a definite clinical diagnosis, identifies patients who are candidate clinical trial subjects using the clinical diagnosis in the clinical trial criteria information as well, and if the patient has not been given a definite clinical diagnosis, identifies patients who are candidate clinical trial subjects using a part of the clinical trial criteria information excluding at least the clinical diagnosis in the clinical trial criteria information, and identifies patients who are candidate clinical trial subjects by excluding from the candidates, among the identified patients, patients whose clinical diagnosis includes a clinical diagnosis other than the clinical diagnosis in the clinical trial criteria information.

[0033] When selecting patients who are candidates for clinical trials, they are usually selected from patients who have been assigned a confirmed clinical diagnosis to which the new drug is applicable. Therefore, patients who are candidates for clinical trials are selected using a confirmed clinical diagnosis, and patients who have not been assigned a confirmed clinical diagnosis are not selected. However, in the case of acute illnesses, it may be desirable to administer a new drug as part of a clinical trial before a confirmed clinical diagnosis is assigned. Therefore, by using these inventions, it is possible to appropriately select patients who are candidates for clinical trials before a confirmed clinical diagnosis is assigned.

[0034] An eighth invention is an information processing system for supporting the selection of patients who are candidates for clinical trial subjects, the information processing system having a structuring processing unit that structures text information inputted to a predetermined screen, and a candidate processing unit that identifies patients who are candidates for clinical trial subjects using information obtained by structuring the input text information and information on clinical trial criteria, The candidate processing unit is an information processing system that, if the patient has not been assigned a confirmed clinical diagnosis, identifies patients who are candidates for clinical trial subjects using at least the patient's symptoms and disease-related symptoms corresponding to the clinical diagnosis. The ninth invention is, An information processing system for supporting the selection of patients who are candidates for clinical trial subjects, the information processing system comprising: a candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered on a specified screen and information on the criteria for the clinical trial; and a notification processing unit that outputs, on a screen displaying information about the identified patients, an indication that the patients are candidates for the clinical trial;The candidate processing unit is an information processing system that, if the patient has not been assigned a confirmed clinical diagnosis, identifies patients who are candidates for clinical trial subjects using at least the patient's symptoms and disease-related symptoms corresponding to the clinical diagnosis.

[0035] In the above-mentioned invention, the candidate processing unit can be configured as an information processing system that, when the patient has not been assigned a definitive clinical diagnosis, estimates the accuracy of the patient's clinical diagnosis using at least the patient's symptoms and the disease-related symptoms corresponding to the clinical diagnosis, and, when this satisfies specified conditions, determines that the patient meets the clinical diagnosis in the clinical trial criteria.

[0036] An eleventh invention is an information processing system for supporting the selection of patients who are candidates for clinical trial subjects, the information processing system having a structuring processing unit that structures text information inputted to a predetermined screen, and a candidate processing unit that identifies patients who are candidates for clinical trial subjects using information obtained by structuring the input text information and information on clinical trial criteria, The candidate processing unit is an information processing system that, when a definitive clinical diagnosis has not been given to the patient, outputs the accuracy of the clinical diagnosis of the patient by inputting at least the patient's symptoms as input values ​​into a predetermined deep learning or machine learning learning model, and, when the accuracy of the outputted clinical diagnosis satisfies predetermined conditions, determines that the patient satisfies the clinical diagnosis in the clinical trial criteria. A twelfth invention is an information processing system that supports the selection of patients who are candidates for clinical trial subjects, the information processing system having a candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered into a specified screen and information about clinical trial criteria, and a notification processing unit that outputs, on a screen displaying information about the identified patient, an indication that the patient is a candidate for the clinical trial; The candidate processing unit is an information processing system that, when a definitive clinical diagnosis has not been given to the patient, outputs the accuracy of the clinical diagnosis of the patient by inputting at least the patient's symptoms as input values ​​into a predetermined deep learning or machine learning learning model, and, when the accuracy of the outputted clinical diagnosis satisfies predetermined conditions, determines that the patient satisfies the clinical diagnosis in the clinical trial criteria.

[0037] In the eighth and eleventh inventions, the free-text field of the electronic medical record is an input field in which the doctor freely inputs text, and therefore there is variation in the notation of clinical diagnosis names, symptoms, etc. Therefore, simply matching the information input in the free-text field of the electronic medical record with the information of the clinical trial criteria does not allow appropriate identification of patients who are candidates for clinical trial subjects. Therefore, as in the present invention, by structuring the text information input in the free-text field of the electronic medical record or the input screen of the input support processing unit, it is possible to appropriately determine differences in the attributes of the disease, such as whether it is a clinical diagnosis name or a pre-existing condition, and identify patients who are candidates for clinical trial subjects. In addition, since the input method is not changed when inputting, the freedom of inputting text information by the doctor is not impaired. In the ninth and twelfth inventions, when a patient who is a candidate for a clinical trial is identified, it is preferable to output an indication that the patient is a candidate for a clinical trial when displaying the patient's information on a predetermined screen. This allows the doctor in charge of the examination to recognize that the patient is a clinical trial subject. In each of the above-mentioned inventions, These inventions can be used to identify patients who are candidates for clinical trials before they have a definitive clinical diagnosis.

[0038] The first invention can be realized by loading the program of the present invention into a computer and executing it. That is, the computer is made to function as a structuring processing unit that structures text information inputted to a predetermined screen, and a candidate processing unit that identifies patients who are candidates for clinical trial subjects using the structured information of the input text information and information on the criteria for clinical trials. An information processing program, wherein the candidate processing unit identifies patients who are candidates for clinical trial subjects using a portion of the information on the clinical trial criteria, excluding at least a clinical diagnosis name from the information on the clinical trial criteria, and identifies patients who are candidates for the clinical trial subjects by excluding from the candidates, among the identified patients, patients whose clinical diagnosis name includes a clinical diagnosis name other than the clinical diagnosis name in the information on the clinical trial criteria, It is an information processing program. The third invention can be realized by loading the program of the present invention into a computer and executing it. That is, the computer is made to function as a candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered on a specified screen and information on the criteria for the clinical trial, and a notification processing unit that outputs, on a screen displaying information on the identified patients, that they are candidates for the clinical trial. An information processing program, wherein the candidate processing unit identifies patients who are candidates for clinical trial subjects using a portion of the information on the clinical trial criteria, excluding at least a clinical diagnosis name from the information on the clinical trial criteria, and identifies patients who are candidates for the clinical trial subjects by excluding from the candidates, among the identified patients, patients whose clinical diagnosis name includes a clinical diagnosis name other than the clinical diagnosis name in the information on the clinical trial criteria, It is an information processing program.

[0039] Eighth The present invention can be realized by loading the program of the present invention into a computer and executing it. That is, the computer is An information processing program that functions as a structuring processing unit that structures text information inputted to a predetermined screen, and a candidate processing unit that identifies patients who are candidates for clinical trial subjects using information obtained by structuring the input text information and information on clinical trial criteria, The candidate processing unit is an information processing program that, if the patient has not been assigned a confirmed clinical diagnosis, identifies patients who are candidates for clinical trial subjects using at least the patient's symptoms and the disease-related symptoms corresponding to the clinical diagnosis. The ninth aspect of the present invention can be realized by loading the program of the present invention into a computer and executing it. That is, Computer, An information processing program that functions as a candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered on a specified screen and information on the criteria for the clinical trial, and a notification processing unit that outputs, on a screen that displays information about the identified patients, an indication that the patients are candidates for the clinical trial, The candidate processing unit is an information processing program that, if the patient has not been assigned a confirmed clinical diagnosis, identifies patients who are candidates for clinical trial subjects using at least the patient's symptoms and the disease-related symptoms corresponding to the clinical diagnosis. The eleventh aspect of the present invention can be realized by loading the program of the present invention into a computer and executing it. That is, Computer, An information processing program that functions as a structuring processing unit that structures text information inputted to a predetermined screen, and a candidate processing unit that identifies patients who are candidates for clinical trial subjects using information obtained by structuring the input text information and information on clinical trial criteria,The candidate processing unit is an information processing program that, if the patient has not been assigned a definitive clinical diagnosis, outputs the accuracy of the patient's clinical diagnosis by inputting at least the patient's symptoms as input values ​​into a specified deep learning or machine learning learning model, and, if the accuracy of the outputted clinical diagnosis satisfies specified conditions, determines that the patient meets the clinical diagnosis in the clinical trial criteria. The twelfth aspect of the present invention can be realized by loading the program of the present invention into a computer and executing it. That is, Computer, An information processing program that functions as a candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered on a specified screen and information on the criteria for the clinical trial, and a notification processing unit that outputs, on a screen that displays information about the identified patients, an indication that the patients are candidates for the clinical trial, The candidate processing unit is an information processing program that, if the patient has not been assigned a definitive clinical diagnosis, outputs the accuracy of the patient's clinical diagnosis by inputting at least the patient's symptoms as input values ​​into a specified deep learning or machine learning learning model, and, if the accuracy of the outputted clinical diagnosis satisfies specified conditions, determines that the patient meets the clinical diagnosis in the clinical trial criteria. Effect of the Invention

[0040] In the information processing system of the present invention, information such as clinical diagnoses entered on a specified input screen is structured and stored, and variations in notation are absorbed, so that patients who are suitable candidates for clinical trials according to their current clinical diagnoses can be selected without interfering with the freedom of doctors to enter information into electronic medical records. In addition, even if a patient does not have a confirmed clinical diagnosis, they can be selected as a candidate for a clinical trial, which is particularly effective in clinical trials of new drugs for acute diseases. [Brief description of the drawings]

[0041] [Figure 1] 1 is a block diagram illustrating an example of the overall processing function of an information processing system according to the present invention. [Diagram 2] FIG. 2 is a block diagram illustrating an example of a hardware configuration of a computer on which the information processing system of the present invention functions. [Diagram 3] 4 is a flowchart showing an example of an overall process of the information processing system of the present invention. [Figure 4]1 is a diagram showing an example of text information entered in a free text field of an electronic medical record and structured information obtained as a result of structuring processing on that information. [Diagram 5] FIG. 13 is a diagram showing an example of a spelling variation dictionary for disease names. [Figure 6] FIG. 13 is a diagram showing an example of a notation variation dictionary for drug names. [Figure 7] FIG. 11 is a diagram showing an example of structured information. [Figure 8] FIG. 13 is a diagram showing an example of an electronic medical record screen. [Figure 9] FIG. 13 is a diagram showing an example of an electronic medical record screen in which information about a clinical trial is extracted from a clinical trial-related information storage unit and displayed as a pop-up window. [Figure 10] FIG. 13 is a diagram showing another example of an electronic medical record screen in which information about a clinical trial is extracted from a clinical trial-related information storage unit and displayed as a pop-up window. [Figure 11] FIG. 11 is a block diagram illustrating an example of the overall processing function of an information processing system according to a second embodiment. [Figure 12] 13 is a flowchart illustrating an example of an overall process of an information processing system according to a second embodiment. [Figure 13] 13 is a flowchart illustrating an example of a processing process of a candidate processing unit in the third embodiment. [Figure 14] FIG. 13 is a diagram showing an example of a screen of an electronic medical record in the third embodiment. [Figure 15] FIG. 13 is a diagram showing another example of the screen of the electronic medical record in the third embodiment. [Figure 16] FIG. 13 is a diagram showing another example of the screen of the electronic medical record in the third embodiment. [Figure 17] FIG. 13 is a diagram showing another example of the screen of the electronic medical record in the third embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0042] An example of the overall processing function of the information processing system 1 of the present invention is shown in the block diagram of Fig. 1. Also, an example of the hardware configuration of a computer that realizes the information processing system 1 of the present invention is shown in Fig. 2. Note that, in the following explanation, an example is given in which the medical professional is a doctor, but the same can be realized in the case of other medical professionals such as dentists.

[0043] The information processing system 1 of the present invention is a computer (including portable communication terminals such as smartphones and tablet computers) that executes the processing of the present invention, and is preferably a computer system that is used primarily in medical institutions and research institutions (universities, research institutes, etc.), but is not limited to this.

[0044] The computer used in the information processing system 1 has a calculation device 70 such as a CPU that executes the calculation processing of a program, a storage device 71 such as a RAM or a hard disk that stores information, a display device 72 such as a display (screen), an input device 73 such as a keyboard or a pointing device (such as a mouse or a numeric keypad), and a communication device 74 that transmits and receives the processing results of the calculation device 70 and the information stored in the storage device 71 via a network such as the Internet or a LAN. Each function (each means) realized on the computer is executed by loading the means (such as a program or module) that executes the process into the calculation device 70. When each function uses information stored in the storage device 71 in its processing, it reads the corresponding information from the storage device 71 and uses the read information appropriately for the processing in the calculation device 70. In addition, although the information processing system 1 in FIG. 1 is shown as being realized by one computer, the functions may be distributed and arranged in multiple computers. The computer includes various information processing devices such as a server, a personal computer, and a workstation. It may also be in a so-called cloud format.

[0045] If the computer is equipped with a touch panel display, the display device 72 and the input device 73 may be configured as one unit. Touch panel displays are often used in portable communication terminals such as tablet computers and smartphones, but are not limited to these. A touch panel display is a device that integrates the functions of the display device 72 and the input device 73 in that input can be made directly on the display using a specified input device (such as a touch panel pen) or a finger.

[0046] The functions of the various means in the present invention are only logically distinct, and may be physically or practically the same area. The processing described in the present invention is merely an example, and the processing process can be changed as appropriate.

[0047] Furthermore, the information processing system 1 of the present invention may be installed in other computer systems used in medical institutions, research institutes, etc., such as electronic medical record systems, and may be realized as part of the functions thereof.

[0048] The information processing system 1 has a target information storage unit 20, a structuring processing unit 21, a candidate processing unit 22, and a notification processing unit .

[0049] The subject information storage unit 20 stores information used for processing in the candidate processing unit 22 described later. The subject information storage unit 20 may include, for example, an electronic medical record information storage unit 201, a test information storage unit 202, and a clinical trial-related information storage unit 203, but is not limited to these.

[0050] The electronic medical record information storage unit 201 stores information on the electronic medical record of a patient. In addition to the attributes of the patient (personal information on the patient such as name, age, and sex), various information such as information on vital signs, information on the free entry field of the electronic medical record, and severity of the disease (severity score information) are preferably stored. Examples of information on vital signs include, but are not limited to, diastolic blood pressure, systolic blood pressure, body temperature, pulse rate, respiratory rate, and SpO2 (percutaneous arterial oxygen saturation). Examples of patient attributes include name, age, and sex. Furthermore, the electronic medical record information may further include, for example, JTAS (Japan Triage and Acuity Scale: urgency).

[0051] In the free-form comment section of the electronic medical record, medical professionals can freely enter information they have obtained from the patient and information from the examination, for example as text information.

[0052] Furthermore, information on nursing records recorded by nurses may be stored in the electronic medical record information storage unit 201. The electronic medical record information storage unit 201 may store information that has been structured from the free-description field of the doctor in the electronic medical record in the structuring processing unit 21 described later, in association with information on the electronic medical record of the patient, preferably information on the free-description field.

[0053] The examination information storage unit 202 stores information on various examinations of the patient. The examination information includes, for example, various examination results such as blood tests, electrocardiograms, ultrasound tests, catheter tests, and genetic tests. Image information of examination results from X-ray tests, CT scans, MRI scans, and the like may also be stored. Furthermore, sound information collected by a stethoscope, sound information of organs, and the like may also be stored. In addition, genetic information from genetic tests may also be stored.

[0054] The clinical trial related information storage unit 203 stores guideline information for selecting clinical trial subjects for clinical trials of new drugs. For example, criteria indicating what kind of patients should be selected are stored in association with the type of new drug, the type of clinical trial, and the like. The criteria include, for example, "clinical diagnosis name," "age," "comorbid disease (presence or absence of medical history," "presence or absence of regular medication," "ADL (activity) before the onset of disease," "severity of disease," and "image findings," but are not limited to these, and other criteria may be set. Furthermore, the clinical trial related information storage unit 203 may include, in addition to the criteria for the clinical trial, information such as contact information for related parties, as well as a checklist indicating whether the criteria for the clinical trial are met. Related parties include, but are not limited to, the clinical trial coordinator and the clinical trial physician.

[0055] The information stored in the target information storage unit 20 may include information on electronic medical records managed by the medical institution stored in the electronic medical record information storage unit 201, information on tests stored in the test information storage unit 202, information on clinical trials stored in the clinical trial-related information storage unit 203, and information acquired from outside the medical institution (such as service systems and application systems used by local governments, companies, public institutions, individuals, etc.). For example, electronic medical record information, test information, health check information, and billing (receipt) information of the patient may be acquired and stored from other medical institutions, local governments, companies, etc. in the region via a regional medical cooperation network. In addition, personal health, medical care, and nursing care information recorded for each medical institution or pharmacy may be acquired from other medical institutions or pharmacies, or from the patient's own computer or external application. Furthermore, vital information of a patient during emergency transport may be acquired and stored from a computer system used by an ambulance team. When acquiring information from an external computer, it is stored in the target information storage unit 20 via an information acquisition unit not shown.

[0056] The structuring processing unit 21 structures unstructured information among the information stored in the target information storage unit 20. In particular, the structuring processing unit 21 structures text information written in free entry fields of electronic medical records stored in the electronic medical record information storage unit 201.

[0057] The information recorded in the electronic medical record stored in the electronic medical record information storage unit 201 is roughly divided into information in the "free entry field" and information in the "ordering" field, in addition to the attribute information of the patient. The information in the "free entry field" is information when the doctor examines the patient, and generally includes information such as the main complaint, current medical history, past medical history, and oral medications obtained from the patient, as well as clinical disease (clinical diagnosis), findings, and progress. The main complaint is the patient's complaint of symptoms, and the current medical history is information indicating when and how the main complaint began and what progress it has made, and is information accompanying the main complaint. The past medical history is the patient's past medical history, and oral medication is information indicating the medications the patient regularly takes. The clinical disease (clinical diagnosis) is the name of the disease that the doctor recalls as being optimal for the patient's condition and recorded in the free entry field, or the name of the disease that accurately reflects the patient's disease. Findings are information indicating the opinion of the doctor who examined the patient, and progress is information indicating what procedures and prescriptions were given to the patient.

[0058] The "ordering" information recorded in electronic medical records refers to insured diseases (insured diagnosis name, insured disease name), instructions given by doctors to nurses, pharmacists, etc. regarding procedures and tests for the patient, and prescribed medications, and is information that has the same meaning as information on medical receipts. The procedures and tests given to the patient are the implemented procedures, and the information on prescribed medication is the implemented prescription. Insured diseases (insured diagnosis name, insured disease name) are diseases that are given to patients for the sake of convenience in order to provide insured medical treatment. Insured diseases (insured diagnosis name, insured disease name) do not necessarily match clinical diseases (clinical diagnosis names), and are often far removed. For this reason, even doctors often cannot accurately identify a patient's actual disease (clinical disease) from the insured disease alone.

[0059] Electronic medical records contain "free text" information, which includes clinically important information, and "ordering" information, which is important from the perspective of insurance claims, but the information in the "free text" field is recorded by doctors' free input, so the information is not structured. For example, free text is entered in a free form of expression by text input.

[0060] The structuring processing unit 21 structures the freely input text information stored in the target information storage unit 20, for example, text information recorded in the "free text field" of the electronic medical record, by referring to natural language analysis processes such as dependency analysis and context analysis, dictionary information (not shown), etc., to generate structured information. The structured information may be stored in the electronic medical record information storage unit 201 in association with the free text field of the electronic medical record of the patient. Various technologies can be used for structuring the text information in the structuring processing unit 21, and there is no limitation thereon.

[0061] The structuring processing unit 21 may also refer to dictionary information to execute a spelling variation process, which is a process for standardizing a plurality of spellings for the same event.

[0062] As a computer system used for natural language analysis processing, for example, Microsoft Azure's LUIS (Language Understanding) provided by Microsoft Corporation can be used, but it is not limited to this.

[0063] Structuring text information means taking freely entered text information and standardizing its contents for each predetermined information type. For example, it is stored in a table format. One process for structuring text information is as follows:

[0064] Based on the text information recorded in the "free text column" of the electronic medical record in the electronic medical record information storage unit 201, tags assigned to predetermined units of text information such as sentences, clauses, and paragraphs are divided into text information with standardized tags (classification of text information by information type) using natural language analysis processing such as referencing dictionary information and context analysis. Since the target information to be extracted for each information type is determined in a medical term dictionary that stores a dictionary of medical terms, the medical term dictionary in the dictionary information is referenced in the text information for each information type to extract the predetermined target information to be extracted. Then, a search is made for "highly relevant information" (hereinafter referred to as "related information") within a predetermined range before and after the extracted target information, for example within 15 characters before and after, and if there is such information, it is extracted as positive / negative expressions or additional information (remarks column) for the symptoms and disease names described below, and stored in a standardized table in association with structured information.

[0065] For example, information types include "history of current illness", "past medical history", "oral medication", "physical findings", and "progress after hospital visit", and the corresponding target information includes "symptoms" for the information type "history of current illness", "disease name" as past medical history for the information type "past medical history", "drug name" for the information type "oral medication", and "disease name" as diagnosis for the information type "progress after hospital visit". For each target information of the information type, the type of related information to be extracted is stored in advance in correspondence with each other. Note that the related information can be set arbitrarily, and for example, the above-mentioned LUIS may be used to automatically extract related information for each target information of the information type. A table is then generated for each information type, and the target information and related information are stored in this table. For example, the table for the information type "history of current illness" stores "symptoms" in correspondence with the corresponding positive and negative expressions. It is possible to arbitrarily set what information types to set and how to set the target information and related information for each information type, but there is a set of information types that is generally unified to a certain extent for the records of general doctors and nurses.

[0066] By performing the above-mentioned processing in the structuring processing unit 21, it is possible to structure the text information inputted into the free-text field, etc. Note that the processing for structuring the free-text field is not limited to the above-mentioned processing.

[0067] When the structuring processing unit 21 detects an information type in the text information of the "free text field" of the electronic medical record in the electronic medical record information storage unit 201 by natural language analysis processing such as referring to medical terms in the medical term dictionary in the dictionary information, referring to tags indicating the information type stored in the dictionary of the tag pattern of the dictionary information, and context analysis of the text information entered in the "free text field", the structuring processing unit 21 judges whether or not a table corresponding to the information type, for example, a table of the information type "current illness history", a table of the information type "medical history", a table of the information type "oral medication", a table of the information type "physical findings", and a table of the information type "progress after visiting the hospital", has already been generated. Then, if a table corresponding to the detected information type has not been generated, the table is generated. Also, for each text information of the information type, the natural language analysis processing and the medical term dictionary in the dictionary information are referenced to extract target information, and related information is searched for and extracted based on the target information. Then, the target information and related information are sorted and stored in the generated information type table.

[0068] On the other hand, if a table corresponding to the detected information type already exists, the target information is extracted by referring to natural language analysis processing for each text information of the information type and the medical term dictionary in the dictionary information, and related information is searched for and extracted based on the target information.The extracted target information and related information are then sorted and stored in the table corresponding to the detected information type.The presence or absence of a table corresponding to the information type can be determined by setting a correspondence between the information type and a table in advance, and determining whether or not a table has been generated based on that correspondence.

[0069] For the detected target information and related information, dictionary information may be referenced to determine whether the target information or related information contains expressions indicating the name of a symptom, disease, or drug, and if it contains any of these, a matching string may be identified in the dictionary information, and a process may be performed to unify the notation of the detected target information and related information by adding or changing standard notations or standard codes predetermined in the dictionary information.

[0070] For example, if natural language analysis processing technology is used to detect "headache" as target information for text information of the information type "history of current illness," dictionary information is referenced, the standard symptom name is changed to "headache," and the positive / negative expression is determined to be "+," and "headache" and "+" are stored in association with each other in the table of the information type "history of current illness."Similarly, if natural language analysis processing technology is used to detect "aspirin" and "statin" as target information for text information of the information type "oral medication," dictionary information is referenced, and the standard drug names are changed to "bay aspirin" and "statin," and their codes (pharmaceutical classification codes) are added, and these are stored in association with each other in the table of the information type "oral medication."

[0071] The structuring processing unit 21 converts the target information and related information for each information type extracted from the text information recorded in the "free text field" as described above into standard notation, adds codes, and stores them in tables for each information type.

[0072] For example, when text information is input in the free text field of the electronic medical record as shown in FIG. 4(a), the structuring processing unit 21 detects the information type "history of current illness" with "S:", the information type "oral medication" with "oral medication:", the information type "physical findings" with "O:", and the information type "progress after hospital visit" with "A / P:" as tags indicating the information type. In addition, the information type "medical history" is detected by a context analysis of the text information "After catheter treatment for myocardial infarction". Then, the text information between the detected information type and the next information type is extracted as the text information of the information type detected first (including physical extraction and also cases where it is specified as a processing target). That is, the information type "history of current illness" is detected by detecting "S:", and the information type "medical history" is detected by a context analysis of the text information "After catheter treatment for myocardial infarction". Then, the text information between the information type "history of current illness" and the information type "medical history" is divided as text information corresponding to the information type "history of current illness". Figure 4(b) shows a schematic diagram of the text information extracted by information type.

[0073] In addition to the above, the tag indicating the type of information may be any character string or symbol, such as representing "current medical history" with "#".

[0074] Target information is extracted from the text information corresponding to the divided information type "history of current illness" by natural language analysis processing and by referring to the medical term dictionary in the dictionary information. Since target information is associated with each information type, for example, target information "symptoms" is extracted from the information type "history of current illness". At this time, the medical term dictionary in the dictionary information is referred to in order to extract whether there is a medical term equivalent to "symptoms", rather than whether "symptoms" is included as specific text information. Positive and negative expressions within a specified range are then extracted from the extracted target information "symptoms". The positive and negative expressions are then associated with the extracted "symptoms" and stored in a table.

[0075] By dividing the target information into the different information types in this way and storing them in a table as structured information, a table for each information type can be created, as shown in Figure 4(c).

[0076] The processing in the structuring processing unit 21 is not limited to the above-mentioned processing, and can be realized by various natural language analysis processing. For example, natural language analysis processing by deep learning, which executes machine learning by referring to a learning model in which the weighting coefficients between neurons in each layer of a neural network consisting of many intermediate layers are optimized, may be used. In this case, text information recorded in the "free text field" of the electronic medical record in the electronic medical record information storage unit 201 may be input to the learning model, and structured information may be output as the output value. As a learning model, it is possible to use one in which the text information in the "free text field" of the electronic medical record is used as an input value, and the corresponding structured information is given as correct answer information.

[0077] Natural language analysis may be performed using AI (artificial intelligence) that uses deep learning or machine learning, or AI that does not use these. Machine learning such as SVM (support vector machine) may also be used.

[0078] The above-mentioned dictionary information stores a notation variation dictionary for determining notation variation of symptom names, disease names, drug names, etc., a pattern table dictionary of negative expressions and ambiguous expressions, etc., a dictionary of frequently appearing abbreviations and specific tag patterns (for example, "Chief complaint:", "A / P", etc.) according to the purpose of the information processing system 1 such as electronic medical records and nursing records, a medical term dictionary of medical terms, etc. The medical term dictionary may store medical terms as target information, and conditions, expressions, and notations for extracting related information corresponding to the medical terms as target information.

[0079] In particular, the structuring processing unit 21 extracts target information to be structured information by referring to a medical term dictionary in the dictionary information. The medical term dictionary is a dictionary that stores standard medical terms, and further extracts highly relevant surrounding text information, so that the information extracted through structuring does not include descriptions of obviously non-medical information, such as patient attribute information for the purpose of sharing information between staff at a medical institution.

[0080] The spelling variation dictionary for symptom names, disease names, and drug names stores, for example, standard spellings, codes, and spelling patterns for symptom names, disease names, and drug names. Figure 5 shows an example of a spelling variation dictionary for disease names, showing a case where standard disease names, ICD codes (international standard codes), disease name conversion codes (domestic general-purpose medical record codes), and spelling patterns are stored in association with each other. Figure 6 shows an example of a spelling variation dictionary for drug names, showing a case where standard drug names, generic names, therapeutic classification codes, and spelling patterns are stored in association with each other.

[0081] The dictionary information is not limited to the above, and it is sufficient if any dictionary necessary for executing structuring processing based on text information is provided.

[0082] An example of information structured by the structuring processing unit 21 is shown in Fig. 7. The structured information is preferably stored in a table format, but is not limited to this. It may also be stored in a database or in other storage formats. Fig. 7(a) is a table of structured information indicating standard symptom names and their presence or absence, Fig. 7(b) is a table of structured information indicating standardized medical history names and corresponding information (remarks), Fig. 7(c) is a table of structured information indicating standardized commonly used drug names and corresponding pharmacological classification codes, and Fig. 7(d) is a table of structured information indicating standardized diagnosis names (clinical diagnosis names) and corresponding information (remarks).

[0083] In Fig. 7(a), the system is structured by storing the current standardized symptoms (symptoms that have absorbed spelling variations) of the patient in correspondence with information indicating their presence or absence, Fig. 7(b) is structured by storing the standardized medical history names (medical history names that have absorbed spelling variations) in correspondence with information regarding them, Fig. 7(c) is structured by storing the standardized commonly used medication names (commonly used medication names that have absorbed spelling variations) in correspondence with codes indicating them, and Fig. 7(d) is structured by storing the standardized clinical diagnosis names in correspondence with information regarding them (whether a suspected (undetermined) clinical diagnosis name or a confirmed clinical diagnosis name).

[0084] In the structuring process in the structuring processor 21 described above, the information type does not include "chief complaint", but the information type "chief complaint" may be included. In that case, similar to the process for other information types, a tag corresponding to the information type "chief complaint", for example "#", is set, and by executing the same process as for other information types, the text information related to the chief complaint can be made into structured information.

[0085] The candidate processing unit 22 uses the information on the criteria of the clinical trial stored in the clinical trial related information storage unit 203, refers to the electronic medical record information stored in the electronic medical record information storage unit 201 and the test information stored in the test information storage unit 202, and identifies (selects) patients who meet the criteria of the clinical trial as candidates for clinical trial subjects. For example, as information on the criteria to be stored in the clinical trial related information storage unit 203, "clinical diagnosis name", "age", "comorbid disease (presence or absence of medical history)", "presence or absence of regular medication", "ADL (energy) before the onset of the disease", "severity of the disease", "image findings", etc. are extracted, and patients who meet each of these criteria are identified by referring to the electronic medical record information and test information.

[0086] As an example, if the criteria stored in the clinical trial-related information storage unit 203 are set as "cerebral infarction" as the "clinical diagnosis," "60 to 85 years old" as the age, "none" as the "comorbid disease (presence or absence of medical history)," "none" as the "presence or absence of regular medication," "1 or less" as the "ADL (health level) before the onset of the disease" (able to walk independently), and "NIHSS score (stroke severity) 8 to 22 points" as the "severity of the disease," patients who meet each of these criteria are identified as candidates for the clinical trial by referring to the electronic medical record information in the electronic medical record information storage unit 201 and the test information stored in the test information storage unit 202.

[0087] When the candidate processing unit 22 identifies a patient as a candidate for a clinical trial, the notification processing unit 23 displays the fact that the patient is a clinical trial subject in the patient's electronic medical record, or notifies the clinical trial physician by email, message, or the like. When making a notification on the identified patient's electronic medical record, information related to the clinical trial information stored in the clinical trial related information storage unit 203, such as the criteria for the clinical trial, contact information for related parties, and inquiries, may be extracted and displayed. Figure 8 shows an example of a case where a notification of identification as a candidate for a clinical trial is displayed on a patient's electronic medical record. Figure 8(a) is a part of a normal electronic medical record screen 100, and Figure 8(b) is a part of the electronic medical record screen 100 when a notification 101 of identification as a candidate for a clinical trial is displayed. EXAMPLES

[0088] Next, an example of the processing of the information processing system 1 of the present invention will be described with reference to the flowchart of FIG.

[0089] A doctor starts the information processing system 1 of the present invention by performing a predetermined operation. Then, the doctor sequentially inputs information acquired by interviewing the patient and checking predetermined vital signs into a predetermined input field of the electronic medical record, such as a free entry field (S100). Note that examination information may be input into the examination information input field as necessary.

[0090] The information thus input is sequentially stored in the electronic medical record information storage unit 201, the test information storage unit 202, etc. Note that information may be input automatically from a test device, etc. Also, input into the free entry field and input field of the electronic medical record may be input by voice, etc.

[0091] The structuring processing unit 21 executes structuring processing on the text information entered in the free-text field of the electronic medical record (S110) to generate structured information. For example, "headache" is stored as structured information in the electronic medical record information storage unit 201 as the "chief complaint." The structured information, "headache" associated with the information type "chief complaint," is sequentially stored in the electronic medical record information storage unit 201. Similarly, the structuring processing unit 21 executes structuring processing on the text information entered in the free-text field of the electronic medical record sequentially to store the information in the electronic medical record information storage unit 201.

[0092] At a predetermined timing, the candidate processing unit 22 executes a process (S120) to identify candidates for clinical trial subjects who meet the criteria of each clinical trial stored in the clinical trial-related information storage unit 203. For example, the candidate processing unit 22 uses information on the clinical trial criteria stored in the clinical trial-related information storage unit 203, refers to the electronic medical record information stored in the electronic medical record information storage unit 201 and the test information stored in the test information storage unit 202, and identifies patients who meet the criteria of the clinical trial as candidates for clinical trial subjects.

[0093] The process by the candidate processing unit 22 to identify candidates for clinical trial subjects may be performed periodically, or at any time, such as when new information is entered into the electronic medical record, or when the electronic medical record file of the patient is opened, closed, or saved.

[0094] When the electronic medical record of a patient identified by the candidate processing unit 22 is opened, the notification processing unit 23 displays a mark 101 indicating that the patient is a candidate for the clinical trial on the screen 100 of the electronic medical record (FIG. 8(b)). Note that the indication that the patient is a candidate for the clinical trial is not limited to a mark, but may be a pop-up window, or may be any output indicating that the patient is a candidate for the clinical trial, such as outputting a predetermined sound or changing the display at a predetermined location on the electronic medical record.

[0095] Also, the notification processing unit 23 may notify the investigator by notifying a message indicating that there is a patient who is a candidate for the clinical trial, outputting a list, etc., instead of displaying on the electronic medical record screen 100. Furthermore, when the indication that the patient is a candidate for the clinical trial is selected, information regarding the clinical trial may be extracted from the clinical trial related information storage unit 203 and displayed in a pop-up window 102 as shown in FIG.

[0096] Also, in the display of the pop-up window 102 shown in FIG. 9, the criteria for the clinical trial subjects may be displayed, and an input field such as a check box or a text box may be provided for each criterion, and the doctor may check the input field again. After the check is entered, a predetermined operation, for example, pressing the button 103, may be performed to notify the doctor in charge of the clinical trial. In other words, the notification processing unit 23 may first display in the electronic medical record that the patient is a candidate for the clinical trial subject, and in the pop-up window 102 or the like displayed there, the doctor in charge of the patient may check again whether the patient meets the criteria for the clinical trial by entering the check box or the like, and then by pressing the button 103 such as "send", a notification may be sent to the doctor in charge of the clinical trial indicating that there is a patient who is a candidate for the clinical trial subject. An example of the electronic medical record screen in this case is shown in FIG. 10.

[0097] In this way, the pop-up window 102 or the like may be provided with an input field such as a checkbox for whether the criteria for the clinical trial are met, and when the doctor makes an input, a display indicating that the patient has become a candidate for the clinical trial by being automatically identified by the candidate processing unit 22, or a different display, etc., may be output. For example, by outputting different values ​​such as changing the display color or displaying "checked by doctor", etc., it becomes easy to distinguish whether the patient was automatically selected or whether the doctor's check has been completed.

[0098] By executing the above-mentioned processing, the doctor who examines the patient can immediately recognize that the patient is a subject of the clinical trial, and the patient can be promptly led to the clinical trial. In addition, the information processing system 1 of the present invention structures and stores the clinical diagnosis name entered in the free entry field of the electronic medical record, and absorbs notational variations, so that it is possible to appropriately identify patients who are candidates for clinical trial subjects without compromising the freedom of input by the doctor in the free entry field of the electronic medical record. EXAMPLES

[0099] In the first embodiment, the processing is performed using the text information entered in the free text field of the electronic medical record, but the processing can also be performed using an input support system (an input support processing unit 24 described later) that supports input to the electronic medical record. An example of the system configuration of the information processing system 1 in this case is shown in FIG.

[0100] In this case, the information processing system 1 has an input support processing unit 24 and an input information storage unit 204. The input support processing unit 24 is a system that supports text input, and when the input support processing unit 24 receives text input, the received text information is reflected, for example, in a free entry field, an ordering field, or the like, of the electronic medical record. That is, the text information received by the input support processing unit 24 is stored in the electronic medical record information storage unit 201. Here, the text information input on the input screen of the input support processing unit 24 is reflected in which field of the electronic medical record, which can be realized by providing an input field in advance on the input screen of the input support processing unit 24 and associating the input field with each field of the electronic medical record. In addition, the text information input on the input screen of the input support processing unit 24 may be reflected in the electronic medical record automatically, or may be reflected manually by, for example, pressing a button provided on the input screen, outputting the text data as text data, and cutting and pasting the data into a free entry field of the electronic medical record.

[0101] The text information inputted by the input support processing unit 24 may be stored in the electronic medical record information storage unit 201, and then the structuring processing unit 21 may execute the structuring process. Alternatively, the structured information (e.g., each table in FIG. 7 ) obtained after the structuring processing unit 21 executes the structuring process on the text information inputted by the input support processing unit 24 may be stored in the electronic medical record information storage unit 201. Moreover, the text information inputted by the input support processing unit 24 may be stored in the electronic medical record information storage unit 201, and the structured information (e.g., each table in FIG. 7 ) obtained after the structuring processing unit 21 executes the structuring process on the text information inputted by the input support processing unit 24 may be stored in the electronic medical record information storage unit 201.

[0102] The input information storage unit 204 stores text information input by the input support processing unit 24. In addition, the input information storage unit 204 may store structured information after the structuring process is executed by the structuring processing unit 21.

[0103] In this embodiment, information is not directly input into the electronic medical record, but text information input via an input screen (not shown) of the input support processing unit 24 is reflected in the electronic medical record. Therefore, the input to the input support processing unit 24 may be input by a doctor, or may be reflected in the electronic medical record from the input support processing unit 24 provided on a portable communication terminal such as a smartphone used by a medical professional such as a doctor or an emergency worker.

[0104] The input screen used by the input support processing unit 24 may be capable of inputting and displaying part or all of the attribute information of the patient, information in the free entry field of the electronic medical record, information in the ordering field, etc., in the same way as the electronic medical record. When displaying the electronic medical record information on this input screen, the electronic medical record information of the patient may be sequentially extracted from the electronic medical record information storage unit 201 and displayed.

[0105] Furthermore, when the candidate processing unit 22 has identified a patient open on an input screen used by the input support processing unit 24 as a candidate for a clinical trial, the notification processing unit 23 may display a mark 101 indicating that the patient is a candidate for a clinical trial on the input screen. In this case, when the display indicating that the patient is a candidate for a clinical trial is selected, information relating to the clinical trial may be extracted from the clinical trial-related information storage unit 203, and a pop-up window 102 as shown in FIG. 9 may be displayed on the input screen used by the input support processing unit 24.

[0106] As in the first embodiment, the display of the pop-up window 102 or the like may display the criteria for the clinical trial subject, provide an input field such as a check box or text box for each criterion, and the doctor may check the input field again. Then, as in FIG. 10, after a check is entered in the input field of the pop-up window 102, a predetermined operation, for example, a button 103 may be pressed to notify the clinical trial doctor. That is, the notification processing unit 23 may first display on the input screen used by the input support processing unit 24 that the patient is a candidate for the clinical trial subject, and in the pop-up window 102 or the like displayed there, the doctor in charge of the patient may check again whether the patient satisfies the criteria for the clinical trial by entering the check box or the like, and then the doctor may press a button 103 such as "send" to send a notification to the clinical trial doctor indicating that there is a patient who is a candidate for the clinical trial subject.

[0107] Next, an example of the processing process in this embodiment will be described with reference to the flow chart of FIG.

[0108] A doctor starts up the information processing system 1 of the present invention by performing a predetermined operation. Then, the input support processing unit 24 extracts electronic medical record information, such as the attribute information of the patient to be input from the electronic medical record information storage unit 201, and displays it on a predetermined input screen. Information obtained by interviewing the patient and checking predetermined vital information is input sequentially into predetermined input fields on the input screen of the input support processing unit 24 (S200). The input screen may or may not be divided into patient attribute information, a free entry field, an ordering field, etc.

[0109] The information thus input is stored in the input information storage unit 204, and is also stored in the electronic medical record information storage unit 201, the test information storage unit 202, etc. (S210). As in the first embodiment, information may be automatically input from a test device, etc. Also, input into the free entry field and input field of the electronic medical record may be input by voice, etc.

[0110] The structuring processing unit 21 executes structuring processing on the information stored in the input information storage unit 204 or the text information entered in the free entry field of the electronic medical record stored in the electronic medical record information storage unit 201 (S220) to generate structured information. This processing is the same as in the first embodiment.

[0111] The candidate processing unit 22 executes, at a predetermined timing, a process of identifying candidates for clinical trial subjects who meet the criteria of each clinical trial stored in the clinical trial-related information storage unit 203 (S230). For example, the candidate processing unit 22 uses information on the criteria of the clinical trial stored in the clinical trial-related information storage unit 203 to refer to one or more of the electronic medical record information stored in the electronic medical record information storage unit 201, the test information stored in the test information storage unit 202, and the text information entered on the input screen stored in the input information storage unit 204, and identifies patients who meet the criteria of the clinical trial as candidates for clinical trial subjects.

[0112] The process by the candidate processing unit 22 to identify candidates for clinical trial subjects may be performed periodically, or may be performed at any timing, such as when new information is entered into the electronic medical record or input screen used by the input support processing unit 24, or when a file of the patient's electronic medical record or input screen used by the input support processing unit 24 is opened, closed or saved.

[0113] When the electronic medical record of a patient identified by the candidate processing unit 22 is opened, the notification processing unit 23 displays a mark 101 indicating that the patient is a candidate for the clinical trial on the input screen of the input support processing unit 24. Note that the indication of the candidate for the clinical trial is not limited to a mark, but may be a pop-up window, or may be any output indicating that the patient is a candidate for the clinical trial, such as outputting a predetermined sound or changing the display at a predetermined location on the input screen, as in the first embodiment.

[0114] Also, the notification processing unit 23 may notify the investigator by notifying a message indicating that there is a patient who is a candidate for the clinical trial, outputting a list, etc., instead of displaying on the input screen of the input support processing unit 24. Furthermore, when the indication that the patient is a candidate for the clinical trial is selected, information regarding the clinical trial may be extracted from the clinical trial-related information storage unit 203 and displayed in a pop-up window 102 as shown in FIG.

[0115] Also, in the display of the pop-up window 102 shown in Fig. 9, the criteria for the clinical trial subjects may be displayed, and an input field such as a check box or text box may be provided for each criterion, and the doctor may check the input field again. After the check is entered, a predetermined operation, for example, pressing the button 103, may be performed to notify the doctor in charge of the clinical trial. That is, the notification processing unit 23 may first display on the input screen of the input support processing unit 24 that the patient is a candidate for the clinical trial subject, as in Fig. 10, and in the pop-up window 102 displayed there, the doctor in charge of the patient may check again whether the patient meets the criteria for the clinical trial by entering the check box or the like, and then by pressing the button 103 such as "send", a notification may be sent to the doctor in charge of the clinical trial indicating that there is a patient who is a candidate for the clinical trial subject.

[0116] In this way, the pop-up window 102 or the like may be provided with an input field such as a checkbox for whether the criteria for the clinical trial are met, and when the doctor makes an input, a display indicating that the patient has become a candidate for the clinical trial by being automatically identified by the candidate processing unit 22, or a different display, etc., may be output. For example, by outputting different values ​​such as changing the display color or displaying "checked by doctor", etc., it becomes easy to distinguish whether the patient was automatically selected or whether the doctor's check has been completed.

[0117] By executing the above-mentioned process, the same effect as in the first embodiment can be obtained. EXAMPLES

[0118] This embodiment is a modification of the first and second embodiments, and describes a case where a patient who is a candidate for a clinical trial is identified even before a clinical diagnosis is given. An example of the processing process of the candidate processing unit 22 in this case is shown in the flowchart of Fig. 13. Note that the processing process up to the processing in the candidate processing unit 22 in S120 and S230 is the same as in the first and second embodiments.

[0119] In this embodiment, since a confirmed clinical diagnosis has not yet been given, the confirmed clinical diagnosis is not written in the free entry field of the electronic medical record. Therefore, the confirmed clinical diagnosis is not stored in the electronic medical record information storage unit 201 and the input information storage unit 204 (a state in which the standardized diagnosis (clinical diagnosis) in FIG. 7(d) is in doubt, or a state in which a standardized diagnosis has not been stored, or the like, in which a confirmed standardized diagnosis has not been stored).

[0120] In the clinical trial related information storage unit 203 in this embodiment, in addition to the clinical diagnosis, symptoms and test values ​​that are likely to occur in the case of the disease are stored in advance as the criteria for the clinical trial. For example, in the case of "cerebral infarction" as the "clinical diagnosis", the criteria stored include one or more of "paralysis", "numbness", "impaired consciousness", "difficulty walking", and "weakness in the limbs" as the "symptoms".

[0121] In addition, the disease and symptoms that are likely to occur in the disease (disease-related symptoms) may be stored in the clinical trial-related information storage unit 203 as a criterion for the clinical trial, or a table may be provided in which diseases and disease-related symptoms are stored in advance in association with each other. In addition, the disease-related symptoms corresponding to each disease may be identified by deep learning or machine learning, and stored in the clinical trial-related information storage unit 203 or a predetermined table.

[0122] A doctor starts the information processing system 1 of the present invention by performing a predetermined operation. Then, the doctor sequentially inputs information acquired by interviewing the patient and checking predetermined vital signs into a predetermined input field, such as a free entry field, of the input screen used in the electronic medical record or the input support processing unit 24 (S300). Note that test information may be input into the test information input field as necessary.

[0123] The structuring processing unit 21 executes structuring processing on the text information inputted into the free-text field of the electronic medical record or the input screen used by the input support processing unit 24 (S310) to make it into structured information. For example, "headache" is stored as structured information as the "chief complaint" in the electronic medical record information storage unit 201 or the input information storage unit 204. The structured information, "headache" associated with the information type "chief complaint", is sequentially stored in the electronic medical record information storage unit 201 or the input information storage unit 204. The structured information, "headache" associated with the information type "chief complaint", is sequentially stored in the electronic medical record information storage unit 201 or the input information storage unit 204. Similarly, the structuring processing unit 21 executes structuring processing on the text information inputted into the free-text field of the electronic medical record or the input screen used by the input support processing unit 24, and stores it in the electronic medical record information storage unit 201 or the input information storage unit 204.

[0124] Then, the candidate processing unit 22 executes a process of identifying candidates for clinical trial subjects who meet the criteria of each clinical trial stored in the clinical trial-related information storage unit 203 (S320).

[0125] Specifically, the candidate processing unit 22 first extracts information on the criteria of the clinical trial to be stored in the clinical trial-related information storage unit 203. It also determines whether or not a confirmed standardized diagnosis (clinical diagnosis) is stored in the electronic medical record information storage unit 201 or the input information storage unit 204 for the patient, for example, in the table of Fig. 7(d) (S300).

[0126] If the confirmed clinical diagnosis is stored in the electronic medical record information storage unit 201 or the input information storage unit 204, the clinical diagnosis in the extracted clinical trial criteria information is also used to refer to one or more of the electronic medical record information stored in the electronic medical record information storage unit 201, the test information stored in the test information storage unit 202, and the information stored in the input information storage unit 204, and patients who meet the clinical trial criteria are identified as candidate clinical trial subjects (S310).

[0127] On the other hand, if the confirmed clinical diagnosis is not stored in the electronic medical record information storage unit 201 or the input information storage unit 204, the clinical diagnosis is not used from the extracted clinical trial criteria information, but one or more of the electronic medical record information stored in the electronic medical record information storage unit 201, the test information stored in the test information storage unit 202, and the information stored in the input information storage unit 204 are referenced to identify patients who meet the conditions (S320).

[0128] Then, from among the patients identified in S320, patients for whom a clinical diagnosis other than the clinical diagnosis in the extracted clinical trial criteria is stored in the electronic medical record information in the electronic medical record information storage unit 201 or in the information in the input information storage unit 204 are excluded (S330), and the remaining patients are identified as patients who are candidates for the clinical trial (S340), and the notification processing unit 23 displays an indication 101 on the electronic medical record screen 100 or the input screen of the input support processing unit 24 that the patients are candidates for the clinical trial.

[0129] For example, the criteria for a certain clinical trial are set as "cerebral infarction (within 12 hours after onset)" as the "clinical diagnosis" (confirmed clinical diagnosis), one or more of "paralysis," "numbness," "impaired consciousness," "difficulty walking," and limb weakness" as the "symptoms," "60 to 85 years old" as the age, "ADL (activity) before the onset of the disease" of "1 or less" (able to walk independently), and "severity of the disease" of "NIHSS score (stroke severity) 8 to 22 points," and these are stored in the clinical trial related information storage unit 203. Note that the time from onset in the clinical diagnosis is obtained by the doctor during the examination and entered in the free entry field of the electronic medical record or the input screen of the input support processing unit 24, and can be determined from appropriate time information such as the onset time information stored in the standard symptom name (table in FIG. 7(a)) and the date and time of transport.

[0130] In this case, for a 25-year-old male patient who has been transported to the emergency department, the electronic medical record stores "symptoms" such as "vomiting" and "diarrhea," "ADL (activity) before the onset of illness" of "less than 1," and "severity of illness" such as "NIHSS score (stroke severity) 10 points." A doctor at the emergency department may examine the patient and the results of that examination may be stored, or the results of an examination by a doctor at a medical institution who examined the patient before the emergency transport may be stored. In this case, since the patient has just been transported to the emergency department, the confirmed clinical diagnosis is not stored in the electronic medical record information storage unit 201 or the input information storage unit 204, and only standard symptom names and the like are stored. Therefore, since the confirmed clinical diagnosis is not stored in the electronic medical record information storage unit 201 or the input information storage unit 204 (S300), the candidate processing unit 22 determines whether the patient meets the clinical trial criteria without using the "clinical diagnosis" among the clinical trial criteria extracted from the clinical trial-related information storage unit 203 (S320).

[0131] The patient in question does not meet the conditions because none of the symptoms "paralysis," "numbness," "altered consciousness," "difficulty walking," or "weakness in the limbs" are listed in the table of standard symptom names (FIG. 7(a)) stored in the electronic medical record information storage unit 201 or the input information storage unit 204. Therefore, the patient is not identified as a candidate for the clinical trial. Therefore, as shown in FIG. 14, the notification processing unit 23 does not display that the patient is a candidate for the clinical trial on the electronic medical record screen 100 or the input screen of the input support processing unit 24.

[0132] On the other hand, suppose that for a 75-year-old male patient who has been transported to the emergency room, the doctor at the emergency room or the doctor at the medical institution who examined him before the emergency transport has stored in the electronic medical record the symptoms of "paralysis," "numbness," "ADL (activity) before the onset of the disease" of "less than 1," and "severity of the disease" of "NIHSS score (stroke severity) 10 points." In this case, since the patient has just been transported to the emergency room, the confirmed clinical diagnosis is not stored in the electronic medical record information storage unit 201 or the input information storage unit 204, and only standard symptom names and the like are stored. Therefore, since the confirmed clinical diagnosis is not stored in the electronic medical record information storage unit 201 or the input information storage unit 204 (S300), the candidate processing unit 22 determines whether the patient satisfies the clinical trial criteria without using the "clinical diagnosis" among the clinical trial criteria extracted from the clinical trial-related information storage unit 203 (S320).

[0133] The patient has "paralysis" and "numbness" stored as symptoms in the table of standard symptom names (FIG. 7(a)) stored in the electronic medical record information storage unit 201 or the input information storage unit 204, and the patient also meets other criteria for the clinical trial, so the candidate processing unit 22 identifies the patient as a candidate for the clinical trial (S320). And, since no clinical diagnosis name (confirmed or suspected) other than "cerebral infarction" is stored in the electronic medical record information storage unit 201 or the input information storage unit 204 for the patient, the patient is not excluded as a clinical trial subject (S330), and the candidate processing unit 22 identifies the patient as a candidate for the clinical trial (S340). Therefore, as shown in FIG. 15, the notification processing unit 23 displays 101 that the patient is a candidate for the clinical trial on the screen 100 of the electronic medical record of the patient or the input screen of the input support processing unit 24 (S130).

[0134] Also, suppose that a 75-year-old male patient who was transported to the emergency room is examined by a doctor at the emergency room or a doctor at a medical institution who examined the patient before the emergency transport and the following symptoms are stored in the electronic medical record: "paralysis," "numbness," "ADL (activity) before the onset of illness" of "less than 1," "severity of illness" of "NIHSS score (stroke severity) 10 points," and "hyperventilation syndrome" as a "suspected clinical diagnosis." In this case, since the patient has just been transported to the emergency room, a confirmed clinical diagnosis is not stored in the electronic medical record information storage unit 201 or the input information storage unit 204, and only a suspected clinical diagnosis is stored. Therefore, since a confirmed clinical diagnosis is not stored in the electronic medical record information storage unit 201 or the input information storage unit 204 (S300), the candidate processing unit 22 determines whether the patient satisfies the criteria for the clinical trial without using the "clinical diagnosis" among the criteria for the clinical trial extracted from the clinical trial-related information storage unit 203 (S320).

[0135] The patient has "paralysis" and "numbness" stored as symptoms in the table of standard symptom names (FIG. 7(a)) stored in the electronic medical record information storage unit 201 or the input information storage unit 204, and the patient also meets other clinical trial criteria, so the candidate processing unit 22 identifies the patient as a candidate for the clinical trial (S320). And, since a clinical diagnosis other than "cerebral infarction" (hyperventilation syndrome) is stored as a suspected clinical diagnosis for the patient in the electronic medical record information storage unit 201 or the input information storage unit 204, the patient is excluded as a clinical trial subject (S330). Therefore, as shown in FIG. 16, the electronic medical record screen 100 of the patient or the input screen of the input support processing unit 24 does not display that the patient is a candidate for the clinical trial.

[0136] Furthermore, suppose that a 75-year-old male patient who has been transported to the emergency room is examined by a doctor at the emergency room and the doctor stores in the electronic medical record the symptoms of "paralysis" and "numbness," an "ADL (activity) before the onset of the disease" of "less than 1," an "severity of the disease" of "NIHSS score (stroke severity) 10 points," and a "suspected clinical diagnosis" of "cerebral infarction." In this case, since the patient has just been transported to the emergency room, a confirmed clinical diagnosis is not stored in the electronic medical record information storage unit 201 or the input information storage unit 204, and only a suspected clinical diagnosis is stored. Therefore, since a confirmed clinical diagnosis is not stored in the electronic medical record information storage unit 201 or the input information storage unit 204 (S300), the candidate processing unit 22 determines whether the patient satisfies the criteria for the clinical trial without using the "clinical diagnosis" among the criteria for the clinical trial extracted from the clinical trial-related information storage unit 203 (S320).

[0137] The patient has "paralysis" and "numbness" stored as symptoms in the table of standard symptom names (FIG. 7(a)) stored in the electronic medical record information storage unit 201 or the input information storage unit 204, and the patient also meets other criteria for the clinical trial, so the candidate processing unit 22 identifies the patient as a candidate for the clinical trial (S320). And, since no clinical diagnosis other than "cerebral infarction" is stored as a suspected clinical diagnosis for the patient in the electronic medical record information storage unit 201 or the input information storage unit 204, the patient is not excluded as a clinical trial subject (S330). Therefore, as shown in FIG. 17, the notification processing unit 23 displays 101 that the patient is a candidate for the clinical trial on the screen 100 of the electronic medical record or the input screen of the input support processing unit 24 (S130).

[0138] In the above-mentioned candidate processing unit 22, patients who are candidates for clinical trial subjects are identified by the processing of the flowchart in Fig. 13, but it is not limited to this, and it is also possible to identify them by using disease-related symptoms. For example, instead of using the "clinical diagnosis" in the clinical trial criteria, it is possible to use criteria other than the clinical diagnosis, such as disease-related symptoms corresponding to the "clinical diagnosis" in the clinical trial criteria, to quantify the possibility that the patient can satisfy the clinical trial criteria using a predetermined algorithm, and if the quantification is higher than a predetermined threshold, it is possible to identify the patient by a process that determines whether the patient satisfies the criteria of other clinical trials.

[0139] As an example of this algorithm, among the structured information stored in the electronic medical record information storage unit 201 or the input information storage unit 204 for the patient, a table of structured information indicating the standardized symptom names and their presence or absence (FIG. 7(a)) is compared with the disease-related symptoms corresponding to the "clinical diagnosis name" in the criteria of the clinical trial, and the proportion of those satisfied is calculated. If this is higher than a predetermined threshold, it is possible to identify the patient by determining whether or not he or she satisfies the criteria of other clinical trials.

[0140] Further, deep learning or machine learning may be used as another process in the candidate processing unit 22. A natural language analysis process using deep learning is used, which executes machine learning by referring to a learning model in which the weighting coefficients between neurons in each layer of a neural network consisting of many intermediate layers are optimized. In this case, at least the patient's symptoms may be input to the learning model, and the accuracy of the clinical diagnosis of the patient may be output as the output value. As a learning model, a model in which disease-related symptoms are input as input values ​​and the corresponding clinical diagnosis is given as correct answer information may be used.

[0141] Moreover, not only the symptoms of the patient but also information (preferably structured information) stored in the electronic medical record information storage unit 201 or the input information storage unit 204 of the patient and attribute information of the patient may be input as input values, and the accuracy of the clinical diagnosis of the patient may be output as the output value. As a learning model, disease-related symptoms, structured information and attribute information of various patients stored in the electronic medical record information storage unit 201 or the input information storage unit 204 may be used as input values, and the corresponding clinical diagnosis may be given as correct answer information.

[0142] In this way, the candidate processing unit 22 estimates the accuracy of the clinical diagnosis of the patient from the patient's symptoms and other information (preferably structured information), and by determining whether it is equal to or exceeds a predetermined threshold, estimates whether the clinical diagnosis satisfies the criteria of the clinical trial. This makes it possible to execute processing even if the electronic medical record information storage unit 201 or the input information storage unit 204 does not have a "confirmed clinical diagnosis."

[0143] Whether the criteria other than the clinical diagnosis in the clinical trial criteria are met can be determined in the same manner as described above.

[0144] By performing the above-mentioned processing by the candidate processing unit 22, it is possible to identify patients who are candidates for clinical trials even if they have not been given a confirmed clinical diagnosis. Therefore, it is also possible to easily select candidates for clinical trials of new drugs for acute diseases.

[0145] In this specification, the case where the disease is "cerebral infarction" has been mainly described, however, it goes without saying that the same treatment can be applied to other diseases, such as cerebral hemorrhage, subarachnoid hemorrhage, acute myocardial infarction, moderate to severe COVID-19, acute heart failure, pneumonia / acute exacerbation of COPD (chronic obstructive pulmonary disease), head trauma, cerebral contusion, spinal cord injury, fractures and other trauma, anaphylaxis, sepsis, DIC (disseminated intravascular coagulation), severe respiratory failure, ARDS (acute respiratory distress syndrome), acute pancreatitis and other ICU diseases, asthma, seizures and other diseases that are common in emergency departments. [Industrial Applicability]

[0146] In the information processing system 1 of the present invention, information such as clinical diagnoses entered on a specified input screen is structured and stored, and variations in notation are absorbed, so that patients who are suitable candidates for clinical trials according to their current clinical diagnoses can be selected without interfering with the freedom of doctors to enter information into electronic medical records. In addition, even if a patient does not have a confirmed clinical diagnosis, they can be selected as a candidate for a clinical trial, which is particularly effective in clinical trials of new drugs for acute diseases. [Explanation of symbols]

[0147] 1: Information processing system 20: Target information storage unit 21: Structure processing section 22: Candidate processing unit 23: Notification processing unit 24: Input support processing section 70: Arithmetic device 71:Storage device 72:Display device 73: Input device 74:Communication equipment 100: Electronic medical record screen 101: Mark indicating a candidate for clinical trial 102: Clinical trial subject checklist pop-up screen 103: Send button 201: Electronic medical record information storage unit 202: Examination information storage unit 203: Clinical trial related information storage unit 204: Input information storage unit

Claims

1. An information processing system for supporting the selection of patients who are candidates for clinical trials, The information processing system includes: a structuring processing unit for structuring text information inputted on a predetermined screen; A candidate processing unit that identifies patients who are candidates for clinical trial subjects using information obtained by structuring the input text information and information on clinical trial criteria, The candidate processing unit Identifying patients who are candidates for the clinical trial using a portion of the information on the clinical trial criteria, excluding at least a clinical diagnosis from the information on the clinical trial criteria, and excluding from the candidates, from among the identified patients, patients whose clinical diagnosis includes a clinical diagnosis other than the clinical diagnosis in the information on the clinical trial criteria, thereby identifying patients who are candidates for the clinical trial. An information processing system comprising:

2. The information processing system includes: A notification processing unit that outputs, on a screen displaying information on the patient identified by the candidate processing unit, information indicating that the patient is a candidate for a clinical trial; 2. The information processing system according to claim 1, further comprising:

3. An information processing system for supporting the selection of patients who are candidates for clinical trials, The information processing system includes: A candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered on a predetermined screen and information about the clinical trial criteria; A notification processing unit that outputs, on a screen displaying information of the identified patient, an indication that the patient is a candidate for a clinical trial; The candidate processing unit Identifying patients who are candidates for the clinical trial using a portion of the information on the clinical trial criteria, excluding at least a clinical diagnosis from the information on the clinical trial criteria, and excluding from the candidates, from among the identified patients, patients whose clinical diagnosis includes a clinical diagnosis other than the clinical diagnosis in the information on the clinical trial criteria, thereby identifying patients who are candidates for the clinical trial. An information processing system comprising:

4. The notification processing unit: Displaying information related to the clinical trial on a screen displaying information of the patient identified by the candidate processing unit; 4. The information processing system according to claim 2 or 3.

5. The notification processing unit: A screen displaying information on the patient identified by the candidate processing unit displays the criteria for the clinical trial and an input field for whether the criteria are met, Notifying a predetermined person of the information entered into the input field and the information of the patient who is a candidate for the clinical trial; 5. The information processing system according to claim 2, wherein the information processing system is a data processing system.

6. The notification processing unit includes: The output indicating that the person is a candidate for a clinical trial subject identified by the candidate processing unit is different from the output indicating that the person is a candidate for a clinical trial subject after input into the input field.

6. The information processing system according to claim 5.

7. The candidate processing unit determining whether the patient has a confirmed clinical diagnosis; If the patient has a confirmed clinical diagnosis, the clinical diagnosis is also used in the clinical trial criteria information to identify patients who are candidates for the clinical trial; If the patient has not been assigned a definite clinical diagnosis, a portion of the information in the clinical trial criteria, excluding at least the clinical diagnosis, is used to identify patients who are candidates for the clinical trial, and among the identified patients, patients whose clinical diagnosis includes a clinical diagnosis other than the clinical diagnosis in the information in the clinical trial criteria are excluded from the candidates, thereby identifying patients who are candidates for the clinical trial.

7. The information processing system according to claim 1, wherein the information processing system is a data processing system.

8. An information processing system for supporting the selection of patients who are candidates for clinical trials, comprising: The information processing system includes: a structuring processing unit for structuring text information inputted on a predetermined screen; A candidate processing unit that identifies patients who are candidates for clinical trial subjects using information obtained by structuring the input text information and information on clinical trial criteria, The candidate processing unit If the patient does not have a confirmed clinical diagnosis, identifying potential patients using at least the patient's symptoms and disease-related symptoms corresponding to the clinical diagnosis; An information processing system comprising:

9. An information processing system for supporting the selection of patients who are candidates for clinical trials, comprising: The information processing system includes: A candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered on a predetermined screen and information about the clinical trial criteria; A notification processing unit that outputs, on a screen displaying information of the identified patient, an indication that the patient is a candidate for a clinical trial; The candidate processing unit If the patient does not have a confirmed clinical diagnosis, identifying potential patients using at least the patient's symptoms and disease-related symptoms corresponding to the clinical diagnosis; An information processing system comprising:

10. The candidate processing unit If the patient has not been assigned a definite clinical diagnosis, the accuracy of the clinical diagnosis of the patient is estimated using at least the patient's symptoms and the disease-related symptoms corresponding to the clinical diagnosis, and if the accuracy satisfies a predetermined condition, the patient is determined to meet the clinical diagnosis in the clinical trial criteria.

10. The information processing system according to claim 8 or 9.

11. An information processing system for supporting the selection of patients who are candidates for clinical trials, comprising: The information processing system includes: a structuring processing unit for structuring text information inputted on a predetermined screen; A candidate processing unit that identifies patients who are candidates for clinical trial subjects using information obtained by structuring the input text information and information on clinical trial criteria, The candidate processing unit If the patient has not been assigned a definitive clinical diagnosis, at least the symptoms of the patient are input as input values ​​into a predetermined deep learning or machine learning learning model to output the accuracy of the clinical diagnosis of the patient; If the accuracy of the output clinical diagnosis satisfies a predetermined condition, it is determined that the patient satisfies the clinical diagnosis in the clinical trial criteria. An information processing system comprising:

12. An information processing system for supporting the selection of patients who are candidates for clinical trials, comprising: The information processing system includes: A candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered on a predetermined screen and information about the clinical trial criteria; A notification processing unit that outputs, on a screen displaying information of the identified patient, an indication that the patient is a candidate for a clinical trial; The candidate processing unit If the patient has not been assigned a definitive clinical diagnosis, at least the symptoms of the patient are input as input values ​​into a predetermined deep learning or machine learning learning model to output the accuracy of the clinical diagnosis of the patient; If the accuracy of the output clinical diagnosis satisfies a predetermined condition, it is determined that the patient satisfies the clinical diagnosis in the clinical trial criteria. An information processing system comprising:

13. Computer, a structuring processing unit that structures text information inputted on a predetermined screen; An information processing program that functions as a candidate processing unit that identifies patients who are candidates for clinical trial subjects using structured information of the input text information and information on clinical trial criteria, The candidate processing unit Identifying patients who are candidates for the clinical trial using a portion of the information on the clinical trial criteria, excluding at least a clinical diagnosis from the information on the clinical trial criteria, and excluding from the candidates, from among the identified patients, patients whose clinical diagnosis includes a clinical diagnosis other than the clinical diagnosis in the information on the clinical trial criteria, thereby identifying patients who are candidates for the clinical trial.

2. An information processing program comprising:

14. Computer, A candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered on a predetermined screen and information about the criteria for the clinical trial; An information processing program that functions as a notification processing unit that outputs, on a screen displaying information of the identified patient, an indication that the patient is a candidate for a clinical trial, The candidate processing unit Identifying patients who are candidates for the clinical trial using a portion of the information on the clinical trial criteria, excluding at least a clinical diagnosis from the information on the clinical trial criteria, and excluding from the candidates, from among the identified patients, patients whose clinical diagnosis includes a clinical diagnosis other than the clinical diagnosis in the information on the clinical trial criteria, thereby identifying patients who are candidates for the clinical trial.

2. An information processing program comprising:

15. Computer, a structuring processing unit that structures text information inputted on a predetermined screen; An information processing program that functions as a candidate processing unit that identifies patients who are candidates for clinical trial subjects using structured information of the input text information and information on clinical trial criteria, The candidate processing unit If the patient does not have a confirmed clinical diagnosis, identifying potential patients using at least the patient's symptoms and disease-related symptoms corresponding to the clinical diagnosis; 2. An information processing program comprising:

16. Computer, A candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered on a predetermined screen and information about the criteria for the clinical trial; An information processing program that functions as a notification processing unit that outputs, on a screen displaying information of the identified patient, an indication that the patient is a candidate for a clinical trial, The candidate processing unit If the patient does not have a confirmed clinical diagnosis, identifying potential patients using at least the patient's symptoms and disease-related symptoms corresponding to the clinical diagnosis; 2. An information processing program comprising:

17. Computer, a structuring processing unit that structures text information inputted on a predetermined screen; An information processing program that functions as a candidate processing unit that identifies patients who are candidates for clinical trial subjects using structured information of the input text information and information on clinical trial criteria, The candidate processing unit If the patient has not been assigned a definitive clinical diagnosis, at least the symptoms of the patient are input as input values ​​into a predetermined deep learning or machine learning learning model to output the accuracy of the clinical diagnosis of the patient; If the accuracy of the output clinical diagnosis satisfies a predetermined condition, it is determined that the patient satisfies the clinical diagnosis in the clinical trial criteria.

2. An information processing program comprising:

18. Computer, A candidate processing unit that identifies patients who are candidates for clinical trial subjects using text information about the patient's disease entered on a predetermined screen and information about the criteria for the clinical trial; An information processing program that functions as a notification processing unit that outputs, on a screen displaying information of the identified patient, an indication that the patient is a candidate for a clinical trial, The candidate processing unit If the patient has not been assigned a definitive clinical diagnosis, at least the symptoms of the patient are input as input values ​​into a predetermined deep learning or machine learning learning model to output the accuracy of the clinical diagnosis of the patient; If the accuracy of the output clinical diagnosis satisfies a predetermined condition, it is determined that the patient satisfies the clinical diagnosis in the clinical trial criteria.

2. An information processing program comprising:

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