Information determination method, device and equipment, computer readable storage medium and computer program product

Abstract

The embodiment of the invention discloses an information determination method, and the method comprises the steps: determining the event development time of a to-be-arranged doctor based on the popularity and fatigue degree of the to-be-screened doctor; wherein the to-be-screened doctors comprise the to-be-arranged doctors, and the popularity degree represents the popularity degree of the to-be-screened doctors; the fatigue degree represents the fatigue degree of the physical and / or mental state of the to-be-screened doctor. The embodiment of the invention further discloses an information determination device and equipment, a computer readable storage medium and a computer program product.

Description

Technical Field

[0001] This application relates to the field of telemedicine technology, and in particular to an information determination method, apparatus, device, computer-readable storage medium, and computer program product. Background Technology

[0002] Follow-up mainly refers to the communication and interaction between medical staff and inpatients or outpatients after they leave the hospital. Follow-up management systems can be used to arrange follow-up work for doctors. However, existing follow-up management systems usually only focus on the accurate management of follow-up data, ignoring the workload of most doctors who not only need to conduct consultations and surgeries but also need to carry out follow-up work. This results in doctors not having enough energy to follow up with patients, leading to poor quality of follow-up work. Summary of the Invention

[0003] To address the aforementioned technical problems, embodiments of this application aim to provide an information determination method, apparatus, device, computer-readable storage medium, and computer program product that can solve the problem of poor quality in follow-up work in related technologies.

[0004] The technical solution of this application is implemented as follows:

[0005] An information determination method, the method comprising:

[0006] The timing of events for doctors to be scheduled is determined based on the popularity and fatigue levels of the doctors to be screened; wherein, the doctors to be screened include the doctors to be scheduled, the popularity level represents the popularity of the doctors to be screened, and the fatigue level represents the degree of physical and / or mental exhaustion of the doctors to be screened.

[0007] In the above scheme, the information determination method further includes:

[0008] Select a target pre-selection time for the doctors to be screened;

[0009] For the doctors to be screened, determine the workload of processing pending transactions within the target pre-selected time period; wherein, the workload represents the number of pending transactions and / or the complexity of the pending transactions; the pending transactions represent tasks or activities that are the same as or similar to the pending events.

[0010] Determine the fatigue parameter corresponding to the doctor to be screened, and determine the proficiency parameter corresponding to the doctor to be screened;

[0011] The degree of fatigue is determined based on the workload, the fatigue parameters, and / or the proficiency parameters.

[0012] In the above scheme, determining the fatigue parameters corresponding to the doctors to be screened includes:

[0013] The fatigue parameter is determined based on the complexity parameter and / or the first risk parameter and / or the second risk parameter; wherein the first risk parameter characterizes the risk level of the candidate doctor in handling target type transactions; the second risk parameter characterizes the risk level of the candidate doctor in handling transactions within a preset time period; and the complexity parameter characterizes the complexity corresponding to the target type transaction.

[0014] In the above scheme, the information determination method further includes:

[0015] The first risk parameter is determined based on the first number of the target type transactions processed by the doctor to be screened and the second number of the first sub-transactions in the target type transactions.

[0016] The second risk parameter is determined based on the third number of transactions handled by the doctor to be screened within the preset time period and the fourth number of second sub-transactions within those transactions.

[0017] In the above scheme, determining the proficiency parameters corresponding to the doctors to be screened includes:

[0018] The proficiency parameter is determined based on the first quantity and the average cycle of the target type of transactions handled by the doctors to be screened within the target time.

[0019] In the above scheme, the information determination method further includes:

[0020] The popularity of the doctors to be screened is determined based on their transaction processing status and / or evaluations and / or feedback. The transaction processing status represents the quantity and / or quality of transactions handled by the doctors to be screened when they are actively booked.

[0021] In the above scheme, determining the event schedule for the doctor to be scheduled based on the popularity and fatigue level of the doctors to be screened includes:

[0022] Based on the popularity, the doctor to be arranged is determined from the doctors to be screened;

[0023] The timing of the event for the doctor to be scheduled is determined based on the doctor's fatigue level and fatigue threshold.

[0024] An information determination method, the information determination method comprising:

[0025] Based on disease similarity and the number of patients meeting the disease similarity criteria, a target doctor is determined from the list of doctors to be determined; wherein, the disease similarity refers to the similarity between the target historical disease of the patient to be matched and the disease to be treated of the target patient, and the number of patients represents the number of patients whose disease similarity meets the disease similarity criteria; the target patient refers to the patient who currently needs to be identified by the target doctor; and the patients to be matched are the set of patients treated by the doctor to be determined in the past.

[0026] In the above scheme, determining the target doctor from the pool of doctors to be determined based on disease similarity and the number of patients meeting the disease similarity criteria includes:

[0027] Determine the disease similarity between the target historical disease to be treated and the disease to be treated; wherein the disease similarity characterizes the similarity between the target historical disease and the disease to be treated in terms of disease progression rate, disease progression speed, and disease fluctuation degree;

[0028] For the doctor to be identified, determine the number of patients to be matched corresponding to the target historical disease whose disease similarity meets the disease similarity condition;

[0029] Based on the number of patients, the target doctor is determined from the list of doctors to be identified.

[0030] In the above scheme, determining the disease similarity between the target historical disease of the patient to be matched and the disease to be treated in the patient to be treated by the doctor to be determined includes:

[0031] The similarity of the diseases is determined based on the disease development trend and / or the disease development speed and / or the disease fluctuation degree; wherein, the disease development trend represents the changes before and after the onset of the disease, the disease development speed represents the rate of deterioration of the disease, and the disease fluctuation degree represents the stability of the disease.

[0032] In the above scheme, determining the disease similarity based on the disease development trend and / or the disease development speed and / or the degree of disease fluctuation includes:

[0033] Based on the first disease development trend of the target patient and the second disease development trend of the patient to be matched, a first similarity is determined between the target historical disease and the disease to be treated; wherein, the disease development trend includes the first disease development trend and the second disease development trend; the first similarity characterizes the degree of similarity between the changes in the condition before and after the target historical disease and the disease to be treated; and / or

[0034] Based on the first disease progression rate of the target patient and the second disease progression rate of the patient to be matched, a second similarity is determined between the target historical disease and the disease to be treated; wherein, the disease progression rate includes the first disease progression rate and the second disease progression rate; the second similarity characterizes the degree of similarity in the rate of disease deterioration between the target historical disease and the disease to be treated; and / or

[0035] Based on the first disease fluctuation level of the target patient and the second disease fluctuation level of the patient to be matched, a third similarity is determined between the target historical disease and the disease to be treated; wherein, the disease fluctuation level includes the first disease fluctuation level and the second disease fluctuation level; the third similarity characterizes the similarity of the stability of the condition between the target historical disease and the disease to be treated; the disease similarity includes the first similarity and / or the second similarity and / or the third similarity.

[0036] In the above scheme, the information determination method further includes:

[0037] Based on the first prognostic physical status index of the target patient at a first time and the second prognostic physical status index at a second time, the first disease development trend is determined; wherein, the first time is prior to the second time.

[0038] The second disease progression trend is determined based on the third prognostic physical status index of each patient to be matched at the first time and the fourth prognostic physical status index at the second time.

[0039] In the above scheme, the information determination method further includes:

[0040] Based on the development trend of the first disease, the first time, and the second time, the development speed of the first disease is determined;

[0041] Based on the development trend of the second disease, the first time, and the second time, the development speed of the second disease is determined.

[0042] In the above scheme, the information determination method further includes:

[0043] Based on the first quantity of discrete data on the development trend of the first disease within the first time period to the second time period, the first time period, and the second time period, the degree of fluctuation of the first disease is determined;

[0044] The degree of fluctuation of the second disease is determined based on the second quantity of discrete data on the development trend of the second disease from the first time to the second time period, the first time, and the second time period.

[0045] An information determination method, the information determination method comprising:

[0046] The patient's valuable information is determined based on the patient's first keyword and the occurrence of the first keyword in the target dataset; wherein, the first keyword represents key information related to the patient's attributes and condition; and the target dataset represents a collection of data related to the doctor's research.

[0047] In the above scheme, determining the patient's valuable information based on the patient's first keyword and the occurrence of the first keyword in the target dataset includes:

[0048] Obtain the first keyword;

[0049] Determine the occurrence of the first keyword in the target dataset; wherein, the occurrence represents the occurrence position and frequency of the first keyword in the target dataset;

[0050] Based on the aforementioned circumstances, valuable information about the patient is determined.

[0051] In the above scheme, determining the occurrence of the first keyword in the target dataset includes:

[0052] Determine the occurrence position of the first keyword in the target dataset and the second keyword in the target dataset, and determine the occurrence frequency of the first keyword in the target dataset and the second keyword.

[0053] In the above scheme, determining the occurrence position of the first keyword in the target dataset and the second keyword in the target dataset, and determining the occurrence frequency of the first keyword in the target dataset and the second keyword, includes:

[0054] The first keyword is classified to obtain multiple categories of keywords;

[0055] For each category of keywords, determine the occurrence position of each sub-keyword in the target dataset and the second keyword;

[0056] Determine the frequency of occurrence of each sub-keyword in the target dataset and the second keyword.

[0057] In the above scheme, determining the patient's valuable information based on the occurrence of the situation includes:

[0058] For the patient, the value information is determined based on the frequency of occurrence of the occurrence location, the second weight corresponding to the occurrence location, and the first weight of each type of keyword.

[0059] An information determination system, the information determination system comprising:

[0060] The scheduling module is used to determine the event execution time for doctors to be scheduled based on the popularity and fatigue level of the doctors to be screened; wherein, the doctors to be screened include the doctors to be scheduled, the popularity represents the popularity of the doctors to be screened, and the fatigue level represents the degree of physical and / or mental exhaustion of the doctors to be screened;

[0061] The appointment module is used to determine a target doctor from a pool of doctors to be determined based on disease similarity and the number of patients meeting the disease similarity criteria. Disease similarity refers to the similarity between the target historical disease of the patient to be matched and the disease to be treated in the target patient; the number of patients represents the number of patients whose disease similarity meets the disease similarity criteria; the target patient refers to the patient currently needing to be identified by the target doctor; and the patients to be matched are the set of patients treated by the doctor to be determined in historical time periods.

[0062] The value information determination module is used to determine the value information of the patient based on the patient's first keyword and the occurrence of the first keyword in the target dataset; wherein, the first keyword represents key information related to the patient's attributes and condition; and the target dataset represents a set of data related to the doctor's research.

[0063] A first information determining device, the first information determining device comprising:

[0064] The first determining unit is used to determine the event execution time of the doctor to be scheduled based on the popularity and fatigue level of the doctor to be screened; wherein, the doctor to be screened includes the doctor to be scheduled, the popularity represents the popularity of the doctor to be screened, and the fatigue level represents the degree of physical and / or mental exhaustion of the doctor to be screened.

[0065] A second information determining device, the second information determining device comprising:

[0066] The second determining unit is used to determine a target doctor from the doctors to be determined based on disease similarity and the number of patients that meet the disease similarity condition; wherein, the disease similarity refers to the similarity between the target historical disease of the patient to be matched and the disease to be treated of the target patient, and the number of patients represents the number of patients whose disease similarity meets the disease similarity condition; the target patient refers to the patient who needs to be determined by the target doctor at present; and the patients to be matched is the set of patients treated by the doctor to be determined in the historical time.

[0067] A third information determining device, the third information determining device comprising:

[0068] The third determining unit is used to determine the patient's value information based on the patient's first keyword and the occurrence of the first keyword in the target dataset; wherein, the first keyword represents key information related to the patient's attributes and condition; and the target dataset represents a set of data related to the doctor's research.

[0069] A first information determining device, the device comprising: a first processor, a first memory, and a first communication bus;

[0070] The first communication bus is used to establish a communication connection between the first processor and the first memory;

[0071] The first processor is used to execute the information determination program stored in the first memory to implement the steps of the above information determination method.

[0072] A second information determining device, the device comprising: a second processor, a second memory, and a second communication bus;

[0073] The second communication bus is used to establish a communication connection between the second processor and the second memory;

[0074] The second processor is used to execute the information determination program stored in the second memory to implement the steps of the above-described information determination method.

[0075] A third information determining device, the device comprising: a third processor, a third memory, and a third communication bus;

[0076] The third communication bus is used to realize the communication connection between the third processor and the third memory;

[0077] The third processor is used to execute the information determination program stored in the third memory to implement the steps of the above information determination method.

[0078] A computer-readable storage medium storing one or more programs that can be executed by one or more processors to perform the steps of the information determination method described above.

[0079] A computer program product includes a computer program that, when executed by a processor, implements the steps of the information determination method described above.

[0080] The information determination method, apparatus, device, computer-readable storage medium, and computer program product provided in this application determine the event execution time for the doctor to be scheduled based on the popularity and fatigue level of the doctor to be screened. The doctor to be screened includes the doctor to be scheduled. Popularity represents the popularity of the doctor to be screened; fatigue level represents the degree of physical and / or mental exhaustion of the doctor to be screened. In this way, doctors with high popularity can be selected by popularity, and suitable event execution times can be selected by fatigue level. That is, the event execution time of the doctor can be determined based on both popularity and fatigue level. It also takes into account the problem that doctors have heavy workloads and are under great pressure and physical fatigue due to follow-up time, which affects the quality of follow-up work, rather than only considering the time of the follow-up doctor to determine the execution time as in related technologies, thereby improving the quality of follow-up work. Attached Figure Description

[0081] Figure 1 A flowchart illustrating an information determination method provided in an embodiment of this application;

[0082] Figure 2 A schematic diagram of the scheduling module in an information determination method provided in an embodiment of this application;

[0083] Figure 3 A flowchart illustrating yet another information determination method provided in an embodiment of this application;

[0084] Figure 4 A schematic diagram of the reservation module in an information determination method provided in an embodiment of this application;

[0085] Figure 5 A flowchart illustrating another information determination method provided in an embodiment of this application;

[0086] Figure 6 A schematic diagram of the value information determination module in an information determination method provided in this application embodiment;

[0087] Figure 7 A flowchart illustrating an information determination method provided in an embodiment of this application;

[0088] Figure 8 A schematic diagram illustrating follow-up scheduling in an information determination method provided in this application embodiment;

[0089] Figure 9 A schematic diagram illustrating fatigue level prediction in an information determination method provided in this application embodiment;

[0090] Figure 10 A flowchart illustrating yet another information determination method provided in an embodiment of this application;

[0091] Figure 11 A flowchart illustrating another information determination method provided in an embodiment of this application;

[0092] Figure 12 This is a schematic diagram of an information determination system provided in an embodiment of this application;

[0093] Figure 13 This is a schematic diagram of the structure of a first information determining device provided in an embodiment of this application;

[0094] Figure 14 This is a schematic diagram of the structure of a second information determining device provided in an embodiment of this application;

[0095] Figure 15 This is a schematic diagram of the structure of a third information determining device provided in an embodiment of this application;

[0096] Figure 16 A schematic diagram of the structure of a first information determining device provided in this application embodiment.

[0097] Figure 17 A schematic diagram of the structure of a second information determining device provided in this application embodiment.

[0098] Figure 18 This is a schematic diagram of the structure of a third information determining device provided in an embodiment of this application. Detailed Implementation

[0099] The technical solutions in the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings.

[0100] It should be understood that the specific embodiments described herein are merely illustrative of this application and are not intended to limit this application.

[0101] It should be noted that the existing technology proposes an intelligent follow-up management and quality evaluation system for radiotherapy patients. By connecting with the Hospital Information System (HIS) and with the patient's authorization, it can automatically extract the current patient's diagnosis and treatment data, which can reduce the problems of asymmetry and errors in medical information caused by patient retelling.

[0102] However, in practical use, existing follow-up management systems only focus on the accurate management of follow-up data, neglecting the workload that follow-up work places on doctors. Most doctors are already burdened with heavy workloads such as consultations and surgeries, leaving them with insufficient energy for patient follow-up. Assigning follow-up work to doctors in this situation may lead to fatigue, affecting the quality of follow-up work, resulting in inadequate understanding of the patient's condition, hindering patient recovery, and impeding the doctor's understanding and research of the patient's condition. Furthermore, regarding doctor selection, doctor introductions typically only include a general overview, lacking detailed information and statistics such as past treatment cases and related statistics. Therefore, patients may struggle to accurately determine which doctor best suits their needs and experience decision-making difficulties. Moreover, based on past projects, hospitals have research needs and hope to identify high-value patients for rapid screening and research analysis, improving the efficiency and accuracy of research activities to better treat and serve patient groups in the future and promote the development of medical technology.

[0103] It should be noted that this application relates to the field of telemedicine technology, specifically to business areas such as telemedicine, follow-up, and chronic disease follow-up, and to technical fields such as intelligent scheduling, doctor fatigue algorithms, doctor fatigue prediction, intelligent doctor-patient matching, and patient research value analysis.

[0104] Based on this, embodiments of this application provide an information determination method, which can be applied to a first information determination device, referring to... Figure 1 As shown, the method includes the following steps:

[0105] Step 101: Based on the popularity and fatigue level of the doctors to be screened, determine the event schedule for the doctors to be scheduled.

[0106] Among them, the doctors to be screened include doctors to be assigned, the popularity level represents the popularity of the doctors to be screened, and the fatigue level represents the degree of physical and / or mental exhaustion of the doctors to be screened.

[0107] In this embodiment, the doctor to be screened can refer to all doctors in the hospital; the doctor to be arranged can refer to the doctor selected from the doctors to be screened to handle the event, and the event can refer to follow-up (i.e., return visit); the event execution time can refer to the follow-up schedule of the doctor to be arranged, and the follow-up schedule is mainly for patients who need to be followed up within the target period according to the follow-up plan, but have not yet made an appointment for follow-up; popularity can refer to the degree to which the doctor is recognized by the patient, which also reflects the doctor's understanding of the patient's condition. Selecting the doctor who understands the patient's condition best to follow up the patient allows the doctor to propose the most suitable treatment plan for the patient's current situation, thereby accelerating the patient's recovery; considering the degree of fatigue, it can ensure that the doctor faces the follow-up work in a better working state, thereby better understanding the patient's condition and collecting more comprehensive and accurate information. The lower the degree of fatigue, the lower the doctor's physical exertion, and the higher the degree of fatigue (such as high surgical complexity or high surgical risk requiring concentration), the higher the doctor's physical exertion.

[0108] In this application embodiment, follow-up is a medical industry professional term, which refers to the communication and exchange between medical staff and inpatients (or outpatients) after they leave the hospital. Typically, follow-up includes changes in the patient's condition, the patient's subsequent treatment, and the patient's opinions and suggestions on medical care and hospital management. Follow-up can improve the hospital's pre- and post-treatment service levels. At the same time, it facilitates doctors to track and observe patients, obtain first-hand data for statistical analysis and experience accumulation, and is also conducive to the development of medical research and the improvement of the professional level of medical staff, thereby better serving patients. Currently, there are various methods and channels for follow-up, including on-site follow-up (such as follow-up clinics), telephone follow-up, voice follow-up, and online consultations. Follow-up is a follow-up, supplementary, and closed-loop service outside of medical diagnosis and treatment. It is usually an extra service provided by doctors in addition to their normal outpatient and surgical work (i.e., realizing the reuse of manpower). Specifically, taking follow-up clinics as an example, the service provider (such as the hospital) first schedules follow-up shifts according to needs. Then, patients make appointments or register to select a follow-up doctor from the scheduled doctors. Finally, the follow-up doctor conducts the follow-up on the patient.

[0109] In this embodiment, the first information determining device can use an intelligent scheduling module to determine the event execution time for the doctor to be scheduled based on the popularity and fatigue level of the doctors to be screened. Furthermore, the intelligent scheduling module can also be applied to the hospital's periodic intelligent follow-up scheduling, thereby generating a follow-up schedule; specifically, as... Figure 2As shown, the scheduling module can include a scheduling configuration module, a doctor information retrieval module, an automatic scheduling table generation module, and a fatigue prediction and judgment module. First, hospital system operators (or the system itself) can configure the intelligent scheduling, such as scheduling cycle, scheduling time, scheduling frequency, and number of people scheduled. Second, the doctor information retrieval module retrieves doctor work information (such as outpatient and surgical records), doctor personal details (such as past medical records), and relevant information such as possible hospital research papers from the hospital information system; the scheduling configuration module retrieves information such as the target frequency of scheduling. Next, the automatic scheduling table generation module performs intelligent scheduling based on the previously retrieved scheduling configuration and doctor work information, automatically creating a proposed schedule for a specific doctor on a specific date. Finally, the fatigue prediction and judgment module analyzes and identifies the feasibility of the proposed schedule for the doctor and whether fatigue is a possibility, using fatigue algorithms. Finally, the doctor's shift schedule for the current period is confirmed by the automatic schedule generation module based on the results returned by the fatigue prediction and judgment module. (If the proposed schedule is met, the schedule is confirmed; if the proposed schedule is not met, the automatic schedule generation module is re-entered to find a more suitable time for scheduling, or to find the most suitable doctor to schedule at the most suitable time, until the scheduling is completed.) Afterwards, the final follow-up schedule can be stored through the intelligent scheduling module.

[0110] It should be noted that the first information determination device may also include a doctor interaction module and a patient interaction module. The doctor interaction module is used to provide viewing and confirmation of follow-up schedules, as well as viewing appointment status, the status of patients with appointments, research value analysis, and the ability to view all possible doctors and interact with doctors and patients, and even provide internet follow-up services. The patient interaction module is used to provide patients with the ability to interact with the hospital, such as viewing follow-up schedule information, viewing appointment or registration doctors, and viewing and confirming smart follow-up appointments. Optionally, doctors can also use the doctor interaction module to confirm or even adjust the generated follow-up schedule. After the periodic intelligent follow-up doctor scheduling: For patients who can proactively schedule follow-ups, or patients who can independently schedule or register for the required doctor through the patient interaction module, the scheduling or registration results are synchronized to the intelligent scheduling module; For patients who do not proactively schedule but need to be followed up within the target period according to the follow-up plan, they can use the intelligent scheduling module described below to automatically match doctors and patients for appointments. After receiving the intelligent scheduling notification, patients can view it through the patient interaction module, and finally confirm the intelligent scheduling through the notification module, doctor interaction module, and patient interaction module, and / or conduct follow-up with the doctor via the Internet through the patient interaction module.

[0111] The information determination method provided in this application can select doctors with high popularity based on their popularity and select suitable times for conducting events based on their fatigue levels. That is, the event time for doctors can be determined based on both popularity and fatigue levels. It also takes into account the problem that doctors are under great pressure and physically exhausted due to the long follow-up time, which affects the quality of follow-up work, rather than only considering the follow-up doctor's time to determine the event time as in related technologies, thereby improving the quality of follow-up work.

[0112] This application provides another information determination method, which can be applied to a second information determination device, as described below. Figure 3 As shown, the method may include the following steps:

[0113] Step 201: Based on disease similarity and the number of patients who meet the disease similarity criteria, identify the target doctor from the list of doctors to be identified.

[0114] Among them, disease similarity refers to the similarity between the target historical disease of the patient to be matched and the disease to be treated of the target patient; patient number represents the number of patients whose disease similarity meets the disease similarity condition; target patient refers to the patient who needs to be identified as the target doctor; and patients to be matched are the set of patients to be treated by the doctor to be identified in the past.

[0115] In this embodiment of the application, the doctor to be determined may refer to a doctor who has been scheduled, and the doctor to be determined may be a doctor whose scheduling is determined by the above-mentioned popularity and fatigue level, or a doctor whose scheduling is done by other methods; the target doctor may refer to a doctor who is intelligently matched for the patient from the doctors who have been scheduled; all doctors to be determined and the set of patients to be matched for all the same disease types they are responsible for can be obtained, representing the set of patients with the same type of disease.

[0116] In this embodiment, the main focus is on identifying target doctors for patients who have not made appointments or registrations but are required to undergo follow-up according to the follow-up plan. Specifically, based on the pre-arranged doctor schedules, intelligent appointments are made for patients who should be followed up but have not made appointments. An intelligent matching algorithm selects the most suitable doctor (i.e., the target doctor) for precise follow-up. This can be achieved through an automatic doctor-patient matching and appointment system using the intelligent appointment module in the second information determination device. The intelligent appointment module can contain the final doctor-patient follow-up appointment form. The purpose of identifying target doctors from among the doctors to be identified, based on disease similarity and the number of patients meeting the disease similarity criteria, is to find the doctor responsible for the largest number of patients with "particularly high disease similarity" to the patient, thereby facilitating the selection of the most suitable doctor and the patient's recovery.

[0117] In the embodiments of this application, such as Figure 4As shown, the intelligent appointment module can include an information reading module and a doctor-patient matching module. Specifically, the information reading module first reads the follow-up schedule and the relevant information of the scheduling doctors (such as past medical records and related information) through the doctor-patient information reading module, and reads the relevant information of the patients, such as the list and details of patients who need to make an appointment (i.e., patients who should be followed up according to the follow-up plan but have not made an appointment or registered). Then, through the doctor-patient matching appointment module, for the patients and diseases that need to make an appointment, intelligent matching is performed based on the comparison and matching of the patients' conditions under the care of the scheduled doctors and the patients to be booked. In this way, the most suitable and appropriate doctor (i.e., the doctor who is responsible for the largest number of patients with the same disease, consistent disease development trend, consistent disease development speed, and consistent disease fluctuation) can be selected for doctor-patient matching and appointment, and the most suitable and experienced doctor can be assigned, which is conducive to the patient's disease recovery and cure. It should be noted that the doctor-patient appointment results determined by the intelligent appointment module (i.e., the patient's target doctor) can be applied to the doctor interaction module, such as for doctors to view, adjust, and confirm follow-up appointments and execute follow-up processes, and can also be applied to the patient interaction module to facilitate patients' final confirmation.

[0118] The information determination method provided in this application embodiment allows for the selection of the most suitable and appropriate doctor from among the scheduled follow-up doctors based on disease similarity and the number of patients meeting the disease similarity criteria. This method assigns the most suitable and experienced doctor to the patient, solving the problem of patient confusion and difficulty in making choices and providing more accurate and matched follow-up services, thereby further improving the quality of follow-up work.

[0119] This application provides another information determination method, which can be applied to a third information determination device, as described below. Figure 5 As shown, the method may include the following steps:

[0120] Step 301: Determine the patient's valuable information based on the patient's first keyword and the occurrence of the first keyword in the target dataset.

[0121] Among them, the first keyword represents key information related to the patient's attributes and condition; the target dataset represents a collection of data related to the doctor's research.

[0122] In this embodiment, the first keyword can refer to key information in the patient's medical records that represents the patient's attributes and condition; the target dataset can be papers by hospital doctors, or related data such as patents or research projects related to the doctor's research, which to some extent can represent the hospital's research direction; the third information determination device also includes a value information determination module, which can calculate the value of the patient to the hospital's research direction based on the hospital's research needs, and can rank patients based on the value, that is, it can determine the patient's value information based on the patient's first keyword and the occurrence of the first keyword in the target dataset, so that doctors can quickly identify patients with high research value, and so on. Figure 6 As shown, the value information determination module may include a data reading module, a keyword extraction module, a text and keyword matching module, and a value analysis and calculation module.

[0123] It should be noted that patients can be tagged with research value based on their established patient information. This allows doctors to view the research value analysis data or rankings of their scheduled patients through the doctor interaction module, facilitating targeted follow-up data collection for research purposes. Furthermore, the patient's research value data or ranking can be directly applied to the follow-up order for the day.

[0124] The information determination method provided in this application embodiment can analyze the research value of patients during follow-up based on the hospital's research direction, so that doctors can quickly identify patients with high research value, assist doctors in their research work and improve the hospital's research capabilities, and improve the efficiency and output of the hospital's research activities, thereby promoting the development of medical technology.

[0125] Based on the foregoing embodiments, this application provides another information determination method, referring to... Figure 7 and Figure 8 and Figure 9 As shown, the method may include the following steps:

[0126] Step 401: The first information determination device selects the target pre-selection time for the doctor to be screened.

[0127] In this embodiment, the target pre-selection time can refer to the planned start date (i.e., date) of the event. Specifically, it can be selected based on the existing workload of the doctors to be screened, starting from the date with the lowest workload. Optionally, the target pre-selection time can be the day with the lowest workload for the doctors to be screened. Specifically, the doctor information reading module in the intelligent scheduling module can read the work calendar of the doctors to be screened, sort their workload (which can be measured by working hours) within the target period from smallest to largest, and select a work date (i.e., the target pre-selection time) from the work dates already sorted by workload based on the principle of the lowest workload for the doctors to be screened, combined with whether the schedule corresponding to the lowest workload is full. In one feasible implementation, the target period can refer to one week (i.e., 7 days).

[0128] It should be noted that it can also determine whether the target pre-selection time is a "working day". Specifically, if it is a working day, the doctor to be screened will be scheduled to follow up during the target pre-selection time. If it is not a working day or the doctor to be screened is determined to be fatigued on all working days within the target period, the next doctor to be screened (such as the doctor with the second highest popularity) will be re-selected for scheduling.

[0129] Step 402: The first information determination device determines the workload of processing pending tasks within the target pre-selected time frame for the doctors to be screened.

[0130] Workload represents the number of pending transactions and / or the complexity of pending transactions; pending transactions represent tasks or activities that are the same as or similar to pending events.

[0131] In this embodiment of the application, the pending task can refer to surgery; the workload of processing pending tasks within the target pre-selection time can be determined based on whether there is a surgery scheduled within N hours on the target pre-selection time. That is, if there is a surgery scheduled within N hours on the target pre-selection time, the workload is determined to be 1, and the fatigue level of the doctor to be screened needs to be determined; if there is no surgery scheduled within N hours on the target pre-selection time, the workload is determined to be 0, and the doctor to be screened is determined to be in a non-fatigued state.

[0132] Step 403: The first information determining device determines the fatigue parameters and proficiency parameters of the doctors to be screened.

[0133] In this embodiment, the fatigue parameter represents the physical exertion of the doctor to be screened. The smaller the fatigue parameter, the lower the physical exertion of the doctor to be screened. The larger the fatigue parameter (i.e., the high complexity of the surgery or the high risk of the surgery requiring concentration), the higher the physical exertion of the doctor to be screened. The proficiency parameter represents the experience, skills and stability of the doctor to be screened in handling events. The higher the proficiency parameter, the easier it is for the doctor to handle events, and the lower the physical exertion of the doctor to be screened.

[0134] It should be noted that the fatigue parameter for determining the doctor to be screened in step 403 can be achieved through step 403A, and the proficiency parameter for determining the doctor to be screened in step 403 can be achieved through step 403B.

[0135] Step 403A: The first information determination device determines fatigue parameters based on complexity parameters and / or first risk parameters and / or second risk parameters.

[0136] Among them, the first risk parameter represents the risk level of the doctor to be screened in handling the target type of transaction; the second risk parameter represents the risk level of the doctor to be screened in handling the transaction within the preset time period; and the complexity parameter represents the complexity of the target type of transaction.

[0137] In this application embodiment, the target type transaction can refer to the surgery of a patient with a specific disease, such as diabetes surgery, heart surgery, hypertension surgery, etc., and the complexity coefficient value defined by the industry or hospital for various different types of transactions; the higher the complexity parameter of the target type transaction handled by the doctor to be screened, the higher the risk of handling the target type transaction, and the greater the physical effort consumed by the doctor to be screened, and the complexity parameter can be represented by T.

[0138] In this embodiment of the application, the first risk parameter may refer to the probability of risks and accidents occurring when the doctor to be screened handles the target type of affairs, and also indicates whether the doctor to be screened needs to concentrate highly; the first risk parameter can be represented by D; it should be noted that events that result in risks and accidents may include medical accidents, surgical hemorrhage, etc.

[0139] In this embodiment of the application, the second risk parameter may refer to the coefficient of risk and unexpected events that occur when the doctor to be screened handles affairs within a preset time period. It also represents the doctor's working status, whether the doctor needs to concentrate highly and the level of physical exertion. The affairs include similar affairs and different types of affairs. The second risk parameter can be represented by H.

[0140] In this embodiment, the fatigue parameter is determined based on the complexity parameter and / or the first risk parameter and / or the second risk parameter. Specifically, the fatigue parameter can be determined based on the complexity parameter, or based on the first risk parameter, or based on the second risk parameter, or based on the complexity parameter and the first risk parameter, or based on the complexity parameter and the second risk parameter, or based on the first risk parameter and the second risk parameter, or based on the complexity parameter, the first risk parameter and the second risk parameter, or based on the complexity parameter, the first risk parameter and the second risk parameter. Furthermore, the fatigue parameter can be determined based on the complexity parameter, the first risk parameter and the second risk parameter by multiplying the complexity parameter, the first risk parameter and the second risk parameter, as shown in the following formula (1):

[0141] R = T × D × H Formula (1)

[0142] It should be noted that step 403A can be achieved in the following way:

[0143] Step 403a1: The first information determining device determines the first risk parameter based on the first number of target type transactions processed by the doctor to be screened and the second number of the first sub-transactions in the target type transactions.

[0144] In this embodiment of the application, the first quantity can refer to the total number of target type transactions processed; the first sub-event can refer to the transaction in the target type transaction where risks and accidents occur; the second quantity can refer to the number of transactions in the target type transaction where risks and accidents occur; specifically, the first risk parameter can be obtained by dividing the first quantity by the second quantity; it should be noted that when the number of times a doctor encounters medical risks in the target type transaction is 0, a base value can be set for the second quantity.

[0145] Step 403a2: The first information determining device determines the second risk parameter based on the third number of transactions processed by the doctor to be screened within a preset time period and the fourth number of second sub-transactions within those transactions.

[0146] In this embodiment, the pre-selected time period can refer to a period of time close to the target pre-selected time; the second sub-transaction can refer to the transaction in which risks and unexpected events occur in the target type transaction and non-target type transaction; the third quantity can refer to the total number of target type transactions and non-target type transactions processed within the target time period; the fourth quantity can refer to the number of transactions in which risks and unexpected events occur in the target type transactions and non-target type transactions processed; specifically, the second risk parameter can be obtained by dividing the third quantity by the fourth quantity; it should be noted that when the number of times a doctor encounters medical risks in the processing of target type transactions is 0, a base value can be set for the fourth quantity.

[0147] Step 403B: The first information determination device determines the proficiency parameters based on the first quantity and the average cycle of the target type of transactions handled by the doctors to be screened within the target time.

[0148] In this embodiment, the proficiency parameter (denoted as S) can represent the proficiency of the selected physician in handling the target type of task, that is, the physician's experience, skill, and stability in performing the same type of task; and the shorter the average time interval between the selected physician's handling of the target type of task, or the more target type of task the selected physician handles, the more proficient the physician is in handling the target type of task, the lower the surgical risk, the easier the physician can handle the surgery, and the lower the physician's physical exertion; the larger the first quantity (denoted as C) or the higher the processing frequency, the more proficient the physician is in handling the target type of task; the average period (denoted as P) t This can refer to the average time interval (e.g., weekly, monthly) during which a doctor handles the same type of task within the target time period. In other words, it refers to how often a doctor is scheduled to handle the target type of task on average. The shorter the average period, the more proficient the doctor is. The proficiency parameter can be obtained by dividing the first quantity and the average period. Specifically, the following formula (2) can be used for the calculation:

[0149] S = C / P t Formula (2)

[0150] Step 404: The first information determination equipment determines the degree of fatigue based on workload, fatigue parameters, and / or proficiency parameters.

[0151] In this embodiment, the degree of fatigue can be determined based on workload and fatigue parameters (i.e., multiplying workload and fatigue parameters), or based on workload and proficiency parameters (i.e., multiplying workload and proficiency parameters), or based on workload, fatigue parameters, and proficiency parameters. Specifically, determining the degree of fatigue based on workload, fatigue parameters, and proficiency parameters involves: first, performing a division operation on fatigue parameters and proficiency parameters (the lower the value of this operation, the lower the degree of fatigue), and then multiplying the result with the value corresponding to workload to obtain the degree of fatigue. When workload can be represented by O, fatigue parameters can be represented by R, and the degree of fatigue can be represented by F, the degree of fatigue can be calculated using the following formula (3):

[0152] F = O × (R / S) Formula (3)

[0153] It should be noted that if a doctor has no surgeries scheduled within the next N hours on the scheduled work day (i.e., the target pre-selected time) (workload is 0), they can be considered to be in a non-fatigued state. If they have surgeries scheduled within the next N hours on the scheduled work day (workload is 1), the degree of fatigue is determined based on fatigue parameters and / or proficiency parameters (e.g., by reading relevant information about the required transactions (such as transaction type) and the doctor's historical transaction data through the fatigue prediction and judgment module, the risk and unexpectedness of the doctor's surgery on that day are predicted based on the historical transaction data; at the same time, the proficiency parameters of the doctor's target type surgery are determined based on historical target type transaction data (such as number and frequency).

[0154] Step 405: The first information determining device determines the popularity of the doctor to be screened based on the doctor's transaction processing status and / or the evaluation status and / or the feedback status.

[0155] Among them, the transaction processing status represents the quantity and / or quality of transactions handled by the doctors to be screened when they are proactively scheduled.

[0156] In this embodiment, the popularity of a doctor to be screened can be determined based on their transaction processing status, evaluations of the doctor, feedback received, a combination of both, or a combination of all these factors. It should be noted that other parameters of the doctor can also be used to determine the popularity of the doctor; however, this embodiment does not impose specific limitations on these methods.

[0157] In this embodiment of the application, the transaction processing status includes the number of transactions and the number of events processed (i.e., the number of times the doctor to be screened is actively booked) when the doctor is actively booked; the evaluation status of the doctor to be screened includes the number of positive reviews, evaluation content and rating stars for the doctor to be screened; and the feedback status of the doctor to be screened includes the number of likes for the doctor to be screened.

[0158] It should be noted that the automatic generation module can automatically read all historical self-appointment information sets (including the transaction status of the doctors to be screened and / or the evaluation status and / or the feedback status of the doctors to be screened) and the hospital's scheduling settings (such as scheduling frequency and number of shifts). The historical self-appointment information comes from the patients' proactive appointments in the patient interaction module, and the historical self-appointment information also includes the information of the doctors who made the appointments.

[0159] Step 406: The first information determination device determines the doctors to be arranged from the list of doctors to be screened based on popularity.

[0160] In this embodiment, the popularity of the doctors to be screened can be sorted from highest to lowest, and the doctor with the highest popularity can be selected as the doctor to be arranged; or the doctor with the highest popularity can be directly selected as the doctor to be arranged; and the more transactions processed, the higher the popularity, and the better the evaluation and feedback, the higher the popularity.

[0161] Step 407: The first information determination device determines the event execution time for the doctor to be scheduled based on the doctor's fatigue level and fatigue threshold.

[0162] In this embodiment, the fatigue threshold can be determined based on historical experience, and can be preset in the fatigue prediction and judgment module. The module can also read the doctor's data, historical surgical data, and recent work arrangement data based on the doctor and time selected during the automatic scheduling generation module. Figure 9 As shown, after obtaining the fatigue level, a fatigue prediction assessment of the doctors to be scheduled is also required (fatigue prediction mainly considers whether the doctors to be scheduled have physically demanding work arrangements such as surgery in the near N hours of the target pre-selected time). That is, fatigue levels can be predicted based on the fatigue level and fatigue threshold. When the fatigue level is greater than or equal to the fatigue threshold, it indicates that the doctor to be scheduled is fatigued within the target pre-selected time, and other pre-selected times for that doctor (such as weekdays with the second least workload) can be tried for scheduling. When the fatigue level is less than the fatigue threshold, it indicates that the doctor to be scheduled is not fatigued, and follow-up can be arranged within the target pre-selected time (i.e., determining the event execution time for the doctor to be scheduled as the target pre-selected time). It should be noted that after completing the scheduling of one doctor, it can be determined whether the scheduling is complete based on the scheduling configuration. If complete, the scheduling ends; if not, the scheduling of the next doctor to be screened can continue until all doctors are scheduled.

[0163] It should be noted that the scheduling can be based on the existing scheduling information of the doctors to be scheduled (such as outpatient scheduling, surgical scheduling, ward round scheduling, and meeting arrangements), and through fatigue algorithm analysis, the most suitable / most popular doctors can be selected to conduct follow-up scheduling on the most suitable date (the least fatigued date). This solves the problem that the existing follow-up management system ignores the fact that doctors are under too much work pressure and fatigue due to follow-up scheduling in addition to their heavy workload, which affects the quality of follow-up work.

[0164] It should be noted that the descriptions of the same steps and contents as in other embodiments in this embodiment can be found in the descriptions in other embodiments, and will not be repeated here.

[0165] The information determination method provided in this application can select doctors with high popularity based on their popularity and select suitable times for conducting events based on their fatigue levels. That is, the event time for doctors can be determined based on both popularity and fatigue levels. It also takes into account the problem that doctors are under great pressure and physically exhausted due to the long follow-up time, which affects the quality of follow-up work, rather than only considering the follow-up doctor's time to determine the event time as in related technologies, thereby improving the quality of follow-up work.

[0166] Based on the foregoing embodiments, this application provides another information determination method, referring to... Figure 10 As shown, the method may include the following steps:

[0167] Step 501: The second information determination device determines the disease similarity between the target historical disease to be treated by the doctor to be determined and the disease to be treated.

[0168] Among them, disease similarity characterizes the degree of similarity between the target historical disease and the disease to be treated in terms of disease development trend and / or disease development speed and / or disease fluctuation.

[0169] In this application embodiment, the disease to be treated may refer to the disease of the target patient; the target historical disease may refer to the disease that matches the disease of the target patient among all patients under the follow-up schedule within the target period.

[0170] It should be noted that step 501 can be implemented in the following way:

[0171] Step 501C: The second information determining device determines the disease similarity based on the disease development trend and / or the disease development speed and / or the degree of disease fluctuation.

[0172] Among them, the disease development trend represents the changes before and after the onset of the disease, the disease development speed represents the rate of deterioration of the disease, and the degree of disease fluctuation represents the stability of the disease.

[0173] In the embodiments of this application, disease similarity can be determined based on disease development trend, disease development speed, disease fluctuation degree, disease development trend and disease development speed, disease development trend and disease fluctuation degree, disease development speed and disease fluctuation degree, or disease similarity can be determined based on disease development trend, disease development speed and disease fluctuation degree.

[0174] It should be noted that step 501C can be achieved through steps 501c1 and / or steps 501c2 and / or steps 501c3:

[0175] Step 501c1: The second information determination device determines the first similarity between the target historical disease and the disease to be treated based on the first disease development trend of the target patient and the second disease development trend of the patient to be matched.

[0176] Among them, the disease development trend includes the first disease development trend and the second disease development trend; the first similarity characterizes the degree of similarity between the changes in the condition of the target historical disease and the disease to be treated before and after.

[0177] In this embodiment, the first disease development trend can refer to the disease development trend of the target patient, the second disease development trend can refer to the disease development trend of the patient to be matched, and the first similarity can refer to the degree of similarity between the disease development trends of the target historical disease of the patient to be matched and the disease to be treated of the target patient. Before determining the first similarity between the target historical disease and the disease to be treated based on the first disease development trend of the target patient and the second disease development trend of the patient to be matched, the first disease development trend can be determined based on the first prognostic physical status index of the target patient at the first time and the second prognostic physical status index at the second time, with the first time being before the second time. Then, the second disease development trend is determined based on the third prognostic physical status index of each patient to be matched at the first time and the fourth prognostic physical status index at the second time. Specifically, the prognostic physical status index is a key parameter that can reflect the patient's symptoms and development (such as the blood glucose level of a diabetic patient, the number of cancer cells in a cancer patient, which is a time-series data set, such as the blood glucose concentration (in mg / dL) at each time interval, such as 200, 180, 190, 140, 90, 100, 80, which can be represented by E respectively). xThis indicates that prognostic physical status indicators can be pre-set by doctors (such as grouping, labeling, etc.); the first time point (T1) can refer to the time of patient discharge (including after outpatient visit); the second time point (T2) can refer to the time of the most recent monitoring; the first prognostic physical status indicator (E1) can refer to the parameters of the target patient's symptoms and progression at the first time point, the second prognostic physical status indicator (E2) can refer to the parameters of the target patient's symptoms and progression at the second time point, the third prognostic physical status indicator (E3) can refer to the parameters of the patient to be matched at the first time point, and the fourth prognostic physical status indicator (E4) can refer to the parameters of the patient to be matched at the second time point; the second prognostic physical status indicator can be used to determine the prognostic physical status indicators. The first disease progression trend (TR = E2 - E1) is obtained by subtracting the post-prognostic physical status index from the first prognostic physical status index; and the second disease progression trend (TR0 = E2 - E1) is obtained by subtracting the fourth and third prognostic physical status indices. In this way, the prognostic physical status index at the time of discharge (including after outpatient visit) of the target patient can be selected as the initial value, and the prognostic physical status index at the most recent monitoring time can be selected as the final value. The progression of the disease can be calculated based on these two values, such as how much it has decreased or increased, which can reflect the degree of improvement or deterioration of the target patient's condition after discharge. Furthermore, the dispersion of the physical status index over a period of time can reflect the degree of fluctuation in the patient's condition.

[0178] In this embodiment of the application, after obtaining the first disease development trend and the second disease development trend, the first disease development trend and the second disease development trend can be compared to determine the first similarity between the target historical disease and the disease to be treated. Patients with the second disease development trend that meet certain conditions with the first disease development trend (such as being completely identical, or the deviation between the two being less than 10%, or less than a set threshold) can be extracted to obtain set P2, that is, set P2 represents a set of patients with the same disease and consistent disease development trends.

[0179] Step 501c2: The second information determination device determines the second similarity between the target historical disease and the disease to be treated based on the first disease progression rate of the target patient and the second disease progression rate of the patient to be matched.

[0180] Among them, the disease progression rate includes the first disease progression rate and the second disease progression rate; the second similarity characterizes the degree of similarity in the rate of disease deterioration between the target historical disease and the disease to be treated.

[0181] In this embodiment, the first disease progression rate can refer to the disease progression rate of the target patient, the second disease progression rate can refer to the disease progression rate of the patient to be matched, and the second similarity can refer to the degree of similarity between the disease progression rates of the target historical disease of the patient to be matched and the disease to be treated of the target patient. Before determining the second similarity between the target historical disease and the disease to be treated based on the first disease progression rate of the target patient and the second disease progression rate of the patient to be matched, the first disease progression rate can be determined based on the first disease progression trend, the first time, and the second time. Similarly, the second disease progression rate can be determined based on the second disease progression trend, the first time, and the second time. Specifically, the disease progression trend can refer to the magnitude of the prognostic effect characteristic value trend. The time interval T (i.e., T = T2 - T1) can be calculated based on the first time and the second time. Then, a division operation is performed between the first disease progression trend and the time interval to obtain the first disease progression rate A (i.e., A = TR / T). Finally, a division operation is performed between the second disease progression trend and the time interval to obtain the second disease progression rate A0.

[0182] In this embodiment of the application, after obtaining the first disease development speed and the second disease development speed, the first disease development speed and the second disease development speed can be compared to determine the second similarity between the target historical disease and the disease to be treated. Furthermore, patients in set P2 whose second disease development speed meets certain conditions (such as being exactly the same, or the deviation between the two being less than 10%, or less than a set threshold) can be extracted to obtain set P3. That is, set P3 represents a set of patients with the same disease, disease development trend, and consistent disease development speed.

[0183] Step 501c3: The second information determination device determines the third similarity between the target historical disease and the disease to be treated based on the first disease fluctuation degree of the target patient and the second disease fluctuation degree of the patient to be matched.

[0184] Among them, the degree of disease fluctuation includes the first degree of disease fluctuation and the second degree of disease fluctuation; the third similarity characterizes the degree of similarity in the stability of the condition between the target historical disease and the disease to be treated; the disease similarity includes the first similarity and / or the second similarity and / or the third similarity.

[0185] In the embodiments of this application, the first disease fluctuation degree may refer to the disease fluctuation degree of the target patient, the second disease fluctuation degree may refer to the disease fluctuation degree of the patient to be matched, and the third similarity may refer to the similarity of the disease fluctuation degree between the target historical disease of the patient to be matched and the disease to be treated of the target patient. Before determining the third similarity between the target historical disease and the disease to be treated based on the first disease fluctuation degree of the target patient and the second disease fluctuation degree of the matching patient, the first disease fluctuation degree can be determined based on the first quantity, first time and second time of the discrete data of the first disease development trend from the first time to the second time. The second disease fluctuation degree can be determined based on the second quantity, first time and second time of the discrete data of the second disease development trend from the first time to the second time. Specifically, the disease fluctuation degree can refer to the proportion of discrete data of the prognostic effect characteristic value of each patient for the same disease within a unit time. The time interval T can be calculated based on the first time and the second time. Then, the first disease fluctuation degree R (i.e., R = N / T) can be obtained by dividing the first quantity N and the time interval. The second disease fluctuation degree R0 can be obtained by dividing the second quantity and the time interval. In this way, by comparing the dispersion of the prognostic physical status index over a period of time, the fluctuation degree of the patient's condition can be reflected. It should be noted that the first quantity can be obtained by using the deviation of the average value of E2 and E1 M%, or more precisely, by the deviation of the theoretical value and the actual value of the prognostic effect characteristic value at a certain time node under the reasonable development trend of the condition exceeding a threshold.

[0186] In this embodiment of the application, after obtaining the first disease fluctuation degree and the second disease fluctuation degree, the first disease fluctuation degree and the second disease fluctuation degree can be compared to determine the third similarity between the target historical disease and the disease to be treated. Furthermore, patients in set P3 whose second disease fluctuation degree meets certain conditions (such as being exactly the same as the first disease fluctuation degree, or the deviation between the two being less than 10%, or less than a set threshold) can be extracted to obtain set P4. That is, set P4 represents a set of patients with the same disease, disease development trend, disease development speed, and disease fluctuation degree.

[0187] Step 502: The second information determination device determines the number of patients to be matched for the target historical disease that meets the disease similarity conditions for the doctor to be determined.

[0188] In this embodiment of the application, the number of patients to be matched corresponding to the doctor to be determined in the P4 set can be counted to obtain the number of patients corresponding to the doctor to be determined in the P4 set.

[0189] Step 503: The second information determination device determines the target doctor from the list of doctors to be determined based on the number of patients.

[0190] In this embodiment, the doctor with the most patients can be identified as the target doctor. That is, the doctor with the most patients is matched with the target patients who need to make appointments for follow-up visits. It should be noted that if the number of follow-up appointments arranged by the target doctor on the same day exceeds the threshold, the doctor with the second most occurrences in reference P4 is selected (in descending order of the number of occurrences) until the number of follow-up appointments arranged by the matched doctor on the same day does not exceed the threshold, and the matching appointment is completed.

[0191] It should be noted that, within the set of all patients to be matched for the follow-up schedule of the target period, a multi-layered screening process is employed to select a group of patients whose diseases, disease progression trends, disease speeds, and disease fluctuations are consistent with those of the target patient. The frequency of the doctors responsible for these patients is then counted, and the doctor with the highest frequency of appearance is selected for appointment matching. A higher frequency of appearance indicates that the doctor is responsible for a greater number of patients with particularly high similarity to the target patient.

[0192] It should be noted that the descriptions of the same steps and contents as in other embodiments in this embodiment can be found in the descriptions in other embodiments, and will not be repeated here.

[0193] The information determination method provided in this application embodiment allows for the selection of the most suitable and appropriate doctor from among the scheduled follow-up doctors based on disease similarity and the number of patients meeting the disease similarity criteria. This method assigns the most suitable and experienced doctor to the patient, solving the problem of patient confusion and difficulty in making choices and providing more accurate and matched follow-up services, thereby further improving the quality of follow-up work.

[0194] Based on the foregoing embodiments, this application provides another information determination method, referring to... Figure 11 As shown, the method may include the following steps:

[0195] Step 601: The third information determination device obtains the first keyword.

[0196] In this embodiment of the application, the patient data reading module in the value information determination module can first read the medical records of the patient to be booked, and then, based on the read medical records of the patient to be booked, the keyword extraction module in the value information determination module can extract the keywords in the patient's medical records, and multiple first keywords of the patient can be obtained.

[0197] In one feasible implementation, multiple primary keywords could be disease type (e.g., thyroid cancer), disease progression (e.g., stage II), whether surgery was performed (e.g., surgery has been performed), prognosis (e.g., good prognosis), treatment methods (e.g., chemotherapy and drug therapy), etc.

[0198] Step 602: The third information determination device determines the occurrence of the first keyword in the target dataset.

[0199] The occurrence pattern represents the location and frequency of the first keyword in the target dataset.

[0200] In this embodiment, the location and frequency of the first keyword in the target dataset can be determined. If the target dataset is a research paper, the location and frequency of the first keyword in the research paper can be determined.

[0201] It should be noted that step 602 can be achieved in the following way:

[0202] Step 602E: The third information determining device determines the position of the first keyword in the target dataset and the second keyword in the target dataset, and determines the frequency of the first keyword in the target dataset and the second keyword.

[0203] In this embodiment, a second keyword can be obtained from the target dataset. The second keyword can refer to a keyword in the target dataset and can be represented by PK. The target dataset can be a dataset composed of all research papers from the hospital. First, the target dataset (e.g., all research papers from the hospital) of the doctor to be booked can be read using the hospital research data reading module in the value information determination module. Then, for the read research papers, keywords can be extracted from the research papers using the keyword extraction module in the value information determination module. The location of occurrence can include keywords, the title and abstract of the research paper, and the main text of the research paper. If the first keyword appears in the second keyword of the research paper in the target dataset, the research value is higher. The occurrence of the first keyword in the second keyword (i.e., the frequency of occurrence) is denoted as CK, the number of times it appears in the title and abstract of the research paper in the target dataset is denoted as CT, and the number of times it appears in the main text of the research paper is denoted as CP.

[0204] In one feasible implementation, the second keyword could be something like {thyroid cancer, cancer, drug treatment}.

[0205] It should be noted that step 602E can be implemented in the following way:

[0206] Step 602e1: The third information determining device classifies the first keyword to obtain multiple categories of keywords.

[0207] In the embodiments of this application, multiple types of keywords may include two types of keywords, namely, a first type of keyword and a second type of keyword, and the first type of keyword can be represented by KA, while the second type of keyword can be represented by KB.

[0208] In one feasible implementation, when the first keyword includes disease type, disease progression, whether surgery is required, prognosis, and treatment method, the first type of keyword can include disease type and disease progression, and the second type of keyword can include whether surgery is required, prognosis, and treatment method.

[0209] Step 602e2: The third information determination device determines the position of each sub-keyword in the target dataset and the second keyword for each type of keyword.

[0210] In the embodiments of this application, when multiple types of keywords may include two types of keywords, the occurrence position of each sub-keyword in one type of keyword in the target dataset and the second keyword can be determined, and the occurrence position of each sub-keyword in the second type of keyword in the target dataset and the second keyword can also be determined.

[0211] Step 602e3: The third information determining device determines the frequency of occurrence of each sub-keyword in the target dataset and the second keyword.

[0212] In the embodiments of this application, when multiple types of keywords may include two types of keywords, the frequency of occurrence of each sub-keyword in one type of keyword in the target dataset and the second keyword can be determined, and the frequency of occurrence of each sub-keyword in the second type of keyword in the target dataset and the second keyword can also be determined.

[0213] Step 603: The third information determination device determines the patient's value information based on the occurrence of the situation.

[0214] In the embodiments of this application, the patient's value information can be determined based on the frequency and location of occurrence.

[0215] It should be noted that step 603 can be achieved in the following way:

[0216] Step 603F: The third information determination device determines the value information for the patient based on the frequency of occurrence of the location, the second weight corresponding to the location, and the first weight of each type of keyword.

[0217] In this embodiment, the first weight can refer to the weight of each type of keyword. When including both type I and type II keywords, different weights can be set for type I and type II keywords. Type I keywords can cover a broad range of topics and can therefore be assigned a higher weight. Specifically, the weight of a type I keyword can be represented by W. KA The weight of second-class keywords can be represented by W. KBThe second weight can refer to the weight corresponding to different positions in a research paper; that is, the second weight appearing in the second keyword can be represented by W. CK The weights corresponding to the appearance of a character in the title and abstract of the target dataset can be represented by W. CT To represent the occurrence of a corresponding weight in the main text of the target dataset, we can use W. CP To express.

[0218] In this embodiment of the application, for each patient, the matching module in the value information determination module can first match each type of keyword with the second keyword and the target dataset. Then, the research value analysis and calculation module in the value information determination module determines the patient's value information based on the first weight and the second weight. That is, the patient's research value = the weighted research value of the first type of keyword appearing in all papers + the weighted research value of the second type of keyword appearing in all papers. Specifically, the patient's value information v can be calculated using the following formula (4):

[0219]

[0220] Where m represents the number of primary keywords of the patient, i represents the i-th primary keyword; n represents the number of secondary keywords of the patient, j represents the j-th secondary keyword; CK i CT represents the frequency of the i-th primary keyword among all secondary keywords; i CP represents the frequency of the i-th category keyword in the titles and / or abstracts of all research papers; i CK represents the frequency of the i-th primary keyword in the main text of all research papers; j CT represents the frequency of the j-th category keyword among all second-category keywords; j This represents the frequency of the i-th category II keyword in the titles and / or abstracts of all research papers; CP j This represents the frequency of the j-th category II keyword in the main text of all research papers.

[0221] It should be noted that after obtaining the research value data (i.e., value information) of each patient for the hospital, the research value data can be sorted and used to present the patient's research value to the doctor, or as a follow-up sorting reference value, so that the doctor can quickly identify patients with high research value.

[0222] It should be noted that the descriptions of the same steps and contents as in other embodiments in this embodiment can be found in the descriptions in other embodiments, and will not be repeated here.

[0223] The information determination method provided in this application embodiment can analyze the research value of patients during follow-up based on the hospital's research direction, so that doctors can quickly identify patients with high research value, assist doctors in their research work and improve the hospital's research capabilities, and improve the efficiency and output of the hospital's research activities, thereby promoting the development of medical technology.

[0224] Based on the foregoing embodiments, this application provides an information determination system, referring to... Figure 12 As shown, the system may include:

[0225] The scheduling module is used to determine the schedule of events for doctors to be scheduled based on the popularity and fatigue level of the doctors to be screened. The doctors to be screened include those to be scheduled, the popularity level represents the popularity of the doctors to be screened, and the fatigue level represents the degree of physical and / or mental exhaustion of the doctors to be screened.

[0226] The appointment module is used to identify target doctors from a pool of doctors to be identified based on disease similarity and the number of patients who meet the disease similarity criteria. Disease similarity refers to the similarity between the target historical disease of the patient to be matched and the disease to be treated in the target patient. The number of patients represents the number of patients whose disease similarity meets the disease similarity criteria. Target patients refer to patients who currently need to be identified by a target doctor. Patients to be matched are the set of patients treated by the doctor to be identified in the past.

[0227] The value information determination module is used to determine the patient's value information based on the patient's first keyword and the occurrence of the first keyword in the target dataset; wherein, the first keyword represents key information related to the patient's attributes and condition; the target dataset represents a set of data related to the doctor's research.

[0228] In this embodiment, firstly, the hospital conducts periodic intelligent follow-up scheduling. During scheduling, the follow-up system, based on existing doctor scheduling information (such as outpatient schedules, surgical schedules, ward round schedules, and meeting arrangements), intelligently selects the most suitable / most popular doctors for follow-up scheduling on the most appropriate dates (when doctors are least fatigued) through fatigue algorithm analysis. Secondly, after the follow-up doctors are scheduled, when a patient needs to make an intelligent appointment for a follow-up visit, the follow-up management system intelligently schedules appointments with the scheduled doctors based on a doctor-patient matching method, matching the patient with a more precise and suitable doctor to provide more accurate and tailored follow-up services. Finally, during follow-ups, for research scenarios, the follow-up management system analyzes and ranks follow-up patients based on the hospital's research direction, enabling doctors to quickly identify patients with high research value, thus assisting doctors in their research work and improving the hospital's research capabilities. The system ultimately achieves the following innovative and beneficial effects: 1. It addresses the problem that existing follow-up management systems neglect the excessive workload and fatigue experienced by doctors due to follow-up scheduling, which negatively impacts the quality of follow-up care. 2. Based on the scheduling, the follow-up management system intelligently matches patients with the most suitable doctors for follow-up, providing more accurate and tailored services. It also resolves the confusion and difficulties patients face in choosing doctors. 3. Through intelligent algorithm analysis, the system performs value analysis and ranking of follow-up patients, enabling doctors to quickly identify patients with high research value, improving the efficiency and output of hospital research activities, and promoting the development of medical technology. In other words, this application provides a system for intelligent scheduling based on doctor popularity and fatigue levels. This system can intelligently schedule doctor-patient appointments through doctor-patient matching methods and perform patient value analysis and ranking based on the patient's relevance to the hospital's research direction. This system addresses both the potential fatigue of doctors juggling their regular duties with follow-up work and the difficulties patients face in choosing doctors, as well as the inability of hospital doctors to quickly identify and assess the research value of patients.

[0229] It should be noted that, as Figure 12The information confirmation system (i.e., follow-up management system) shown may also include a doctor interaction module, a patient interaction module, a notification module, and / or other modules, and may involve external systems such as hospital internal systems, such as the Hospital Information System (HIS) and research management system. The doctor interaction module provides capabilities such as viewing and confirming schedules. Optionally, the doctor interaction module may also include other capabilities required by doctors, such as viewing appointment status, viewing appointment patient information and research value analysis, and all possible interactions with doctors and patients, even providing internet follow-up services. The patient interaction module provides patients with the ability to interact with the hospital, such as viewing follow-up schedule information, making appointments or registering with doctors, and viewing and confirming smart follow-up appointments. After the follow-up doctor schedule is completed: 1) Patients who proactively schedule follow-ups can use the patient interaction module to independently make appointments or register with the required doctors for follow-up, and their appointment or registration results are synchronized to the appointment module. 2) For patients who do not actively schedule an appointment but are required to attend follow-up appointments according to the follow-up plan, the appointment module will make an intelligent appointment. After receiving the intelligent appointment notification, patients can view and confirm the appointment through the patient interaction module, and / or through the patient interaction module, patients can conduct follow-up appointments with doctors online. 3) For patients who have not made an appointment or registered independently but are required to attend follow-up appointments according to the follow-up plan, the intelligent appointment module will automatically match patients with doctors for appointments. Optionally, final confirmation can be made through the notification module, doctor interaction module, or patient interaction module.

[0230] The information determination system provided in the embodiments of this application includes a system and method for intelligent scheduling based on the analysis of doctors' popularity and fatigue levels. This system can intelligently match and schedule appointments with doctors through a doctor-patient matching method, and analyze and rank patients' value based on their relationship with the hospital's research direction. This can solve the problem that doctors are under too much work pressure and fatigue due to follow-up scheduling, which affects the quality of follow-up work, while also solving the problem that patients have difficulty choosing doctors and that hospital doctors cannot quickly identify and assess the research value of patients.

[0231] Based on the foregoing embodiments, this application provides a first information determining device, which can be applied to... Figure 1 and Figure 7 In the information determination method provided in the corresponding embodiment, refer to Figure 13 As shown, the first information determining device 7 may include: a first determining unit 71, wherein:

[0232] The first determining unit 71 is used to determine the event execution time of the doctor to be scheduled based on the popularity and fatigue level of the doctor to be screened; wherein, the doctor to be screened includes the doctor to be scheduled, the popularity level represents the popularity of the doctor to be screened, and the fatigue level represents the degree of physical and / or mental exhaustion of the doctor to be screened.

[0233] In other embodiments of this application, the first determining unit 71 is further configured to perform the following steps:

[0234] Select the target pre-selection time for the doctors to be screened;

[0235] For the doctors to be screened, determine the workload of handling pending tasks within the target pre-selected time frame; where workload represents the number of pending tasks and / or the complexity of pending tasks; pending tasks represent tasks or activities that are the same as or similar to pending events.

[0236] Determine the fatigue parameters and proficiency parameters for the doctors to be screened.

[0237] The degree of fatigue is determined based on workload, fatigue parameters, and / or proficiency parameters.

[0238] In other embodiments of this application, the first determining unit 71 is further configured to perform the following steps:

[0239] The fatigue parameter is determined based on the complexity parameter and / or the first risk parameter and / or the second risk parameter; wherein, the first risk parameter represents the risk level of the doctor to be screened in handling the target type of transaction; the second risk parameter represents the risk level of the doctor to be screened in handling the transaction within a preset time period; and the complexity parameter represents the complexity of the target type of transaction.

[0240] In other embodiments of this application, the first determining unit 71 is further configured to perform the following steps:

[0241] The first risk parameter is determined based on the first number of target type transactions processed by the doctors to be screened and the second number of the first sub-transactions in the target type transactions.

[0242] The second risk parameter is determined based on the third number of transactions handled by the doctor to be screened within a preset time period and the fourth number of second sub-transactions within those transactions.

[0243] In other embodiments of this application, the first determining unit 71 is further configured to perform the following steps:

[0244] Based on the first number and the average cycle of the target type of transactions handled by the doctors to be screened within the target time, the proficiency parameters are determined.

[0245] In other embodiments of this application, the first determining unit 71 is further configured to perform the following steps:

[0246] The popularity of the doctors to be screened is determined based on their transaction processing status and / or evaluations and / or feedback. Transaction processing status represents the quantity and / or quality of transactions handled by the doctors when they are proactively booked.

[0247] In other embodiments of this application, the first determining unit 71 is further configured to perform the following steps:

[0248] Based on popularity, select doctors to be scheduled from the pool of doctors to be screened;

[0249] The timing of events for doctors to be scheduled is determined based on their fatigue levels and fatigue thresholds.

[0250] It should be noted that the specific implementation process of the steps performed by each module in the embodiments of this application can be referred to Figure 1 and Figure 7 The implementation process of the information determination method provided in the corresponding embodiments will not be described in detail here.

[0251] The first information determining device provided in the embodiments of this application can select doctors with high popularity based on popularity and select suitable time to carry out the event based on fatigue level. That is, the event carrying time of doctors can be determined based on popularity and fatigue level. It also takes into account the problem that doctors have heavy workloads and are under great pressure and physical fatigue due to follow-up time, which affects the quality of follow-up work, rather than only considering the follow-up doctor's time to determine the event carrying time as in related technologies, thereby improving the quality of follow-up work.

[0252] Based on the foregoing embodiments, embodiments of this application provide a second information determining device, which can be applied to... Figure 3 and Figure 10 In the information determination method provided in the corresponding embodiment, refer to Figure 14 As shown, the information determining device 8 may include: a second determining unit 81, wherein:

[0253] The second determining unit 81 is used to determine the target doctor from the doctors to be determined based on the disease similarity and the number of patients that meet the disease similarity condition; wherein, the disease similarity refers to the similarity between the target historical disease of the patient to be matched and the disease to be treated of the target patient, and the number of patients represents the number of patients whose disease similarity meets the disease similarity condition; the target patient refers to the patient who needs to be determined as the target doctor at present; the patients to be matched are the set of patients treated by the doctor to be determined in the historical time.

[0254] In other embodiments of this application, the second determining unit 81 is further configured to perform the following steps:

[0255] Determine the disease similarity between the target historical disease to be treated and the disease to be treated; wherein, the disease similarity characterizes the similarity between the target historical disease and the disease to be treated in terms of disease development trend and / or disease development speed and / or disease fluctuation degree;

[0256] For the doctor to be identified, determine the number of patients to be matched for the target historical disease that meets the disease similarity criteria.

[0257] Based on the number of patients, target doctors are identified from the pool of doctors to be identified.

[0258] In other embodiments of this application, the second determining unit 81 is further configured to perform the following steps:

[0259] Disease similarity is determined based on disease development trend and / or disease development speed and / or disease fluctuation degree; whereby disease development trend represents changes before and after the onset of the disease, disease development speed represents the rate of deterioration of the disease, and disease fluctuation degree represents the stability of the disease.

[0260] In other embodiments of this application, the second determining unit 81 is further configured to perform the following steps:

[0261] Based on the first disease progression trend of the target patient and the second disease progression trend of the matched patient, a first similarity is determined between the target historical disease and the disease to be treated; wherein, the disease progression trend includes the first disease progression trend and the second disease progression trend; the first similarity characterizes the degree of similarity between the changes in the condition before and after the target historical disease and the disease to be treated; and / or

[0262] Based on the first disease progression rate of the target patient and the second disease progression rate of the matched patient, a second similarity is determined between the target historical disease and the disease to be treated; wherein, the disease progression rate includes the first disease progression rate and the second disease progression rate; the second similarity characterizes the degree of similarity in the rate of disease deterioration between the target historical disease and the disease to be treated; and / or

[0263] Based on the first disease fluctuation level of the target patient and the second disease fluctuation level of the matching patient, a third similarity is determined between the target historical disease and the disease to be treated; wherein, the disease fluctuation level includes the first disease fluctuation level and the second disease fluctuation level; the third similarity characterizes the similarity of the stability of the condition between the target historical disease and the disease to be treated; the disease similarity includes the first similarity and / or the second similarity and / or the third similarity.

[0264] In other embodiments of this application, the second determining unit 81 is further configured to perform the following steps:

[0265] The first disease progression trend is determined based on the first prognostic physical status index of the target patient at the first time and the second prognostic physical status index at the second time; wherein the first time is prior to the second time.

[0266] The second disease progression trend is determined based on the third prognostic physical status index at the first time and the fourth prognostic physical status index at the second time for each patient to be matched.

[0267] In other embodiments of this application, the second determining unit 81 is further configured to perform the following steps:

[0268] The speed of disease progression is determined based on the development trend of the first disease, the first time point, and the second time point.

[0269] Based on the development trend of the second disease, the first time and the second time, the development speed of the second disease is determined.

[0270] In other embodiments of this application, the second determining unit 81 is further configured to perform the following steps:

[0271] Based on the first quantity, the first time and the second time of discrete data on the development trend of the first disease from the first time to the second time, the degree of fluctuation of the first disease is determined.

[0272] The degree of fluctuation of the second disease is determined based on the second quantity, the first time and the second time of discrete data on the development trend of the second disease from the first time to the second time period.

[0273] It should be noted that the specific implementation process of the steps performed by each module in the embodiments of this application can be referred to Figure 3 and Figure 10 The implementation process of the information determination method provided in the corresponding embodiments will not be described in detail here.

[0274] The second information determination device provided in this application embodiment can, when a patient needs to schedule a follow-up visit, select the most suitable and appropriate doctor from among the scheduled follow-up doctors based on the similarity of the disease and the number of patients who meet the disease similarity criteria, and perform doctor-patient matching and appointment. This not only solves the problem of patients' confusion and difficulty in choosing a doctor, but also provides a more accurate and matched follow-up service, thereby further improving the quality of follow-up work.

[0275] Based on the foregoing embodiments, embodiments of this application provide a third information determining device, which can be applied to... Figure 5 and Figure 11 In the information determination method provided in the corresponding embodiment, refer to Figure 15As shown, the information determining device 9 may include: a third determining unit 91, wherein:

[0276] The third determining unit 91 is used to determine the patient's valuable information based on the patient's first keyword and the occurrence of the first keyword in the target dataset; wherein, the first keyword represents key information related to the patient's attributes and condition; the target dataset represents a set of data related to the doctor's research.

[0277] In other embodiments of this application, the second determining unit 91 is further configured to perform the following steps:

[0278] Obtain the primary keyword;

[0279] Determine the occurrence of the first keyword in the target dataset; where occurrence represents the position and frequency of each first keyword in the target dataset;

[0280] Based on the circumstances, determine the patient's valuable information.

[0281] In other embodiments of this application, the second determining unit 91 is further configured to perform the following steps:

[0282] Determine the position of the first keyword in the target dataset and the second keyword in the target dataset, and determine the frequency of the first keyword in the target dataset and the second keyword.

[0283] In other embodiments of this application, the second determining unit 91 is further configured to perform the following steps:

[0284] The primary keyword is categorized to obtain multiple keyword categories;

[0285] For each category of keywords, determine the position of each sub-keyword in the target dataset and the second keyword;

[0286] Determine the frequency of each sub-keyword in the target dataset and the second keyword.

[0287] In other embodiments of this application, the second determining unit 91 is further configured to perform the following steps:

[0288] For patients, valuable information is determined based on the frequency of occurrence of the location, the second weight corresponding to the location, and the first weight of each keyword category.

[0289] It should be noted that the specific implementation process of the steps performed by each module in the embodiments of this application can be referred to Figure 5 and Figure 11 The implementation process of the information determination method provided in the corresponding embodiments will not be described in detail here.

[0290] The third information determination device provided in this application embodiment can analyze the research value of patients during follow-up visits based on the hospital's research direction. This allows doctors to quickly identify patients with high research value, assisting doctors in their research work and improving the hospital's research capabilities. It also enhances the efficiency and output of the hospital's research activities and promotes the development of medical technology.

[0291] Based on the foregoing embodiments, embodiments of this application provide a first information determining device, which can be applied to... Figure 1 and Figure 7 In the information determination method provided in the corresponding embodiment, refer to Figure 16 As shown, the first information determining device 10 may include: a first processor 101, a first memory 102, and a first communication bus 103, wherein:

[0292] The first communication bus 103 is used to realize the communication connection between the first processor 101 and the first memory 102;

[0293] The first processor 101 is used to execute the information determination program in the first memory 102 to perform the following steps:

[0294] The timing of events for doctors to be scheduled is determined based on the popularity and fatigue levels of the doctors to be screened. The doctors to be screened include those to be scheduled, with popularity indicating the popularity of the doctors to be screened and fatigue indicating the degree of physical and / or mental exhaustion of the doctors to be screened.

[0295] In other embodiments of this application, the first processor 101 is used to execute an information determination program in the first memory 102 to perform the following steps:

[0296] Select the target pre-selection time for the doctors to be screened;

[0297] For the doctors to be screened, determine the workload of handling pending tasks within the target pre-selected time frame; where workload represents the number of pending tasks and / or the complexity of pending tasks; pending tasks represent tasks or activities that are the same as or similar to pending events.

[0298] Determine the fatigue parameters and proficiency parameters for the doctors to be screened.

[0299] The degree of fatigue is determined based on workload, fatigue parameters, and / or proficiency parameters.

[0300] In other embodiments of this application, the first processor 101 is used to execute the information determination program in the first memory 102 to determine the fatigue parameters corresponding to the doctors to be screened, in order to achieve the following steps:

[0301] The fatigue parameter is determined based on the complexity parameter and / or the first risk parameter and / or the second risk parameter; wherein, the first risk parameter represents the risk level of the doctor to be screened in handling the target type of transaction; the second risk parameter represents the risk level of the doctor to be screened in handling the transaction within a preset time period; and the complexity parameter represents the complexity of the target type of transaction.

[0302] In other embodiments of this application, the first processor 101 is used to execute an information determination program in the first memory 102 to perform the following steps:

[0303] The first risk parameter is determined based on the first number of target type transactions processed by the doctors to be screened and the second number of the first sub-transactions in the target type transactions.

[0304] The second risk parameter is determined based on the third number of transactions handled by the doctor to be screened within a preset time period and the fourth number of second sub-transactions within those transactions.

[0305] In other embodiments of this application, the first processor 101 is used to execute the information determination program in the first memory 102 to determine the skill parameters corresponding to the doctor to be screened, in order to implement the following steps:

[0306] Based on the first number and the average cycle of the target type of transactions handled by the doctors to be screened within the target time, the proficiency parameters are determined.

[0307] In other embodiments of this application, the first processor 101 is used to execute an information determination program in the first memory 102 to perform the following steps:

[0308] The popularity of the doctors to be screened is determined based on their transaction processing status and / or evaluations and / or feedback. Transaction processing status represents the quantity and / or quality of transactions handled by the doctors when they are proactively booked.

[0309] In other embodiments of this application, the first processor 101 is used to execute an information determination program in the first memory 102 to determine the event schedule for the doctor to be scheduled based on the popularity and fatigue level of the doctors to be screened, in order to achieve the following steps:

[0310] Based on popularity, select doctors to be scheduled from the pool of doctors to be screened;

[0311] The timing of events for doctors to be scheduled is determined based on their fatigue levels and fatigue thresholds.

[0312] It should be noted that a detailed description of the steps performed by the processor can be found in [reference needed]. Figure 1 and Figure 7The implementation process of the information determination method provided in the corresponding embodiments will not be described in detail here.

[0313] The first information determination device provided in this application embodiment can select doctors with high popularity based on popularity and select suitable time to carry out the event based on fatigue level. That is, it can determine the event time of doctors based on popularity and fatigue level. It also takes into account the problem that doctors have heavy workloads and are under great pressure and physical fatigue due to follow-up time, which affects the quality of follow-up work. Instead of only considering the follow-up doctor's time to determine the event time as in related technologies, it improves the quality of follow-up work.

[0314] Based on the foregoing embodiments, embodiments of this application provide a second information determining device, which can be applied to... Figure 3 and Figure 10 In the information determination method provided in the corresponding embodiment, refer to Figure 17 As shown, the second information determining device 11 may include: a second processor 111, a second memory 112, and a second communication bus 113, wherein:

[0315] The second communication bus 113 is used to realize the communication connection between the second processor 111 and the second memory 112;

[0316] The second processor 111 is used to execute the information determination program in the second memory 112 to perform the following steps:

[0317] Based on disease similarity and the number of patients meeting the disease similarity criteria, target doctors are identified from the pool of doctors to be identified. Disease similarity refers to the similarity between the target historical disease of the patient to be matched and the disease to be treated of the target patient. The number of patients represents the number of patients whose disease similarity meets the disease similarity criteria. Target patients refer to the patients for whom the target doctor needs to be identified at present. Patients to be matched are the set of patients treated by the doctor to be identified in the past.

[0318] In other embodiments of this application, the second processor 111 is used to execute the information determination program in the second memory 112 to determine the target doctor from the doctors to be determined based on disease similarity and the number of patients meeting the disease similarity criteria, in order to achieve the following steps:

[0319] Determine the disease similarity between the target historical disease to be treated and the disease to be treated; wherein, the disease similarity characterizes the similarity between the target historical disease and the disease to be treated in terms of disease development trend and / or disease development speed and / or disease fluctuation degree;

[0320] For the doctor to be identified, determine the number of patients to be matched for the target historical disease that meets the disease similarity criteria.

[0321] Based on the number of patients, target doctors are identified from the pool of doctors to be identified.

[0322] In other embodiments of this application, the second processor 111 is used to execute the information determination program in the second memory 112 to determine the disease similarity between the target historical disease of the patient to be treated by the doctor to be determined and the disease to be treated, in order to achieve the following steps:

[0323] Disease similarity is determined based on disease development trend and / or disease development speed and / or disease fluctuation degree; whereby disease development trend represents changes before and after the onset of the disease, disease development speed represents the rate of deterioration of the disease, and disease fluctuation degree represents the stability of the disease.

[0324] In other embodiments of this application, the second processor 111 is used to execute the information determination program in the second memory 112 to determine disease similarity based on disease development trend and / or disease development speed and / or disease fluctuation degree, in order to achieve the following steps:

[0325] Based on the first disease progression trend of the target patient and the second disease progression trend of the matched patient, a first similarity is determined between the target historical disease and the disease to be treated; wherein, the disease progression trend includes the first disease progression trend and the second disease progression trend; the first similarity characterizes the degree of similarity between the changes in the condition before and after the target historical disease and the disease to be treated; and / or

[0326] Based on the first disease progression rate of the target patient and the second disease progression rate of the matched patient, a second similarity is determined between the target historical disease and the disease to be treated; wherein, the disease progression rate includes the first disease progression rate and the second disease progression rate; the second similarity characterizes the degree of similarity in the rate of disease deterioration between the target historical disease and the disease to be treated; and / or

[0327] Based on the first disease fluctuation level of the target patient and the second disease fluctuation level of the matching patient, a third similarity is determined between the target historical disease and the disease to be treated; wherein, the disease fluctuation level includes the first disease fluctuation level and the second disease fluctuation level; the third similarity characterizes the similarity of the stability of the condition between the target historical disease and the disease to be treated; the disease similarity includes the first similarity and / or the second similarity and / or the third similarity.

[0328] In other embodiments of this application, the second processor 111 is used to execute an information determination program in the second memory 112 to perform the following steps:

[0329] The first disease progression trend is determined based on the first prognostic physical status index of the target patient at the first time and the second prognostic physical status index at the second time; wherein the first time is prior to the second time.

[0330] The second disease progression trend is determined based on the third prognostic physical status index at the first time and the fourth prognostic physical status index at the second time for each patient to be matched.

[0331] In other embodiments of this application, the second processor 111 is used to execute an information determination program in the second memory 112 to perform the following steps:

[0332] The speed of disease progression is determined based on the development trend of the first disease, the first time point, and the second time point.

[0333] Based on the development trend of the second disease, the first time and the second time, the development speed of the second disease is determined.

[0334] In other embodiments of this application, the second processor 111 is used to execute an information determination program in the second memory 112 to perform the following steps:

[0335] Based on the first quantity, the first time and the second time of discrete data on the development trend of the first disease from the first time to the second time, the degree of fluctuation of the first disease is determined.

[0336] The degree of fluctuation of the second disease is determined based on the second quantity, the first time and the second time of discrete data on the development trend of the second disease from the first time to the second time period.

[0337] It should be noted that a detailed description of the steps performed by the processor can be found in [reference needed]. Figure 3 and Figure 10 The implementation process of the information determination method provided in the corresponding embodiments will not be described in detail here.

[0338] The second information determination device provided in this application embodiment can, when a patient needs to schedule a follow-up visit, select the most suitable and appropriate doctor from among the scheduled follow-up doctors based on the similarity of the disease and the number of patients who meet the disease similarity criteria, and perform doctor-patient matching and appointment. This not only solves the problem of patients' confusion and difficulty in choosing a doctor, but also provides a more accurate and matched follow-up service, thereby further improving the quality of follow-up work.

[0339] Based on the foregoing embodiments, embodiments of this application provide a third information determining device, which can be applied to... Figure 5 and Figure 11 In the information determination method provided in the corresponding embodiment, refer to Figure 18As shown, the third information determining device 12 may include: a third processor 121, a third memory 122, and a third communication bus 123, wherein:

[0340] The third communication bus 123 is used to realize the communication connection between the third processor 121 and the third memory 122;

[0341] The third processor 121 is used to execute the information determination program in the third memory 122 to perform the following steps:

[0342] Based on the patient's primary keyword and its occurrence in the target dataset, valuable information about the patient is determined; whereby the primary keyword represents key information related to the patient's attributes and condition; and the target dataset represents a collection of data related to the doctor's research.

[0343] In other embodiments of this application, the second processor 121 is used to execute the information determination program in the second memory 122 to determine the patient's value information based on the patient's first keyword and the occurrence of the first keyword in the target dataset, in order to achieve the following steps:

[0344] Obtain the primary keyword;

[0345] Determine the occurrence of the first keyword in the target dataset; where occurrence represents the position and frequency of each first keyword in the target dataset;

[0346] Based on the circumstances, determine the patient's valuable information.

[0347] In other embodiments of this application, the second processor 121 is used to execute the information determination program in the second memory 122 to determine the occurrence of each first keyword in the target dataset, in order to implement the following steps:

[0348] Determine the position of the first keyword in the target dataset and the second keyword in the target dataset, and determine the frequency of the first keyword in the target dataset and the second keyword.

[0349] In other embodiments of this application, the second processor 121 is used to execute the information determination program in the second memory 122 to determine the occurrence position of the first keyword in the target dataset and the second keyword in the target dataset, and to determine the occurrence frequency of the first keyword in the target dataset and the second keyword, so as to achieve the following steps:

[0350] The primary keyword is categorized to obtain multiple keyword categories;

[0351] For each category of keywords, determine the position of each sub-keyword in the target dataset and the second keyword;

[0352] Determine the frequency of each sub-keyword in the target dataset and the second keyword.

[0353] In other embodiments of this application, the second processor 121 is used to execute an information determination program in the second memory 122 to determine the patient's value information based on the occurrence of events, in order to perform the following steps:

[0354] For patients, valuable information is determined based on the frequency of occurrence of the location, the second weight corresponding to the location, and the first weight of each keyword category.

[0355] It should be noted that a detailed description of the steps performed by the processor can be found in [reference needed]. Figure 5 and Figure 11 The implementation process of the information determination method provided in the corresponding embodiments will not be described in detail here.

[0356] The third information determination device provided in this application embodiment can analyze the research value of patients during follow-up based on the hospital's research direction, so that doctors can quickly identify patients with high research value, assist doctors in their research work and improve the hospital's research capabilities, and improve the efficiency and output of the hospital's research activities, thereby promoting the development of medical technology.

[0357] Based on the foregoing embodiments, this application also provides a computer program product, including a computer program that can be executed by the processor 101 of the first information determining device 10, the processor 111 of the second information determining device 11, and the processor 121 of the third information determining device 12 to achieve... Figure 1 , Figure 3 and Figure 5 The steps in the information determination method provided in the corresponding embodiment.

[0358] Based on the foregoing embodiments, this application provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to achieve... Figure 1 , Figure 3 and Figure 5 The steps in the information determination method provided in the corresponding embodiment.

[0359] It should be noted that the aforementioned computer-readable storage media can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic random access memory (FRAM), flash memory, magnetic surface memory, optical disc, or compact disc read-only memory (CD-ROM), etc.; or it can be various electronic devices that include one or any combination of the above-mentioned memories, such as mobile phones, computers, tablet devices, personal digital assistants, etc.

[0360] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0361] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0362] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk) and includes several instructions to cause a terminal device (which may be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in the various embodiments of this application.

[0363] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0364] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0365] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0366] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. An information determination method characterized by comprising: The method comprises: determining an event start time of a to-be-scheduled doctor based on a popularity degree and a fatigue degree of a to-be-screened doctor; wherein the to-be-screened doctor comprises the to-be-scheduled doctor, the popularity degree represents a popularity degree of the to-be-screened doctor, and the fatigue degree represents a tiredness degree of a physical and / or mental state of the to-be-screened doctor.

2. The method of claim 1, wherein, The method further comprises: selecting a target pre-selection time of the to-be-screened doctor for the to-be-screened doctor; determining a workload of processing to-be-processed transactions within the target pre-selection time for the to-be-screened doctor; wherein the workload represents a quantity and / or a complexity of the to-be-processed transactions; the to-be-processed transactions represent the same or similar tasks or activities as to-be-processed events; determining a fatigue parameter corresponding to the to-be-screened doctor, and determining a proficiency parameter corresponding to the to-be-screened doctor; determining the fatigue degree based on the workload, the fatigue parameter, and / or the proficiency parameter.

3. The method of claim 2, wherein, The determination of the fatigue parameter corresponding to the to-be-screened doctor comprises: determining the fatigue parameter based on a complexity parameter and / or a first risk parameter and / or a second risk parameter; wherein the first risk parameter represents a risk degree of the to-be-screened doctor processing a target type transaction; the second risk parameter represents a risk degree of the to-be-screened doctor processing transactions within a preset time period; and the complexity parameter represents a complexity degree corresponding to the target type transaction.

4. The method of claim 3, wherein, The method further comprises: determining the first risk parameter based on a first quantity of the target type transactions processed by the to-be-screened doctor and a second quantity of a first sub-transaction in the target type transactions; determining the second risk parameter based on a third quantity of transactions processed by the to-be-screened doctor within the preset time period and a fourth quantity of a second sub-transaction in the transactions.

5. The method of claim 4, wherein, The determination of the proficiency parameter corresponding to the to-be-screened doctor comprises: determining the proficiency parameter based on the first quantity and an average period of the target type transactions processed by the to-be-screened doctor within a target time.

6. The method of claim 1, wherein, The method further comprises: determining the popularity degree of the to-be-screened doctor based on a transaction processing condition of the to-be-screened doctor and / or an evaluation condition for the to-be-screened doctor and / or a feedback condition for the to-be-screened doctor; wherein the transaction processing condition represents a quantity and / or a quality of transactions processed by the to-be-screened doctor in a voluntarily pre-booking condition.

7. The method of claim 1, wherein, The determination of the event start time of the to-be-scheduled doctor based on the popularity degree and the fatigue degree of the to-be-screened doctor comprises: determining the to-be-scheduled doctor from the to-be-screened doctor based on the popularity degree; determining the event start time of the to-be-scheduled doctor based on the fatigue degree of the to-be-scheduled doctor and a fatigue threshold.

8. An information determination method characterized by comprising: The method comprises: determine a target doctor from the doctors to be determined based on a disease similarity and a number of patients satisfying a disease similarity condition, wherein the disease similarity refers to a similarity between a target historical disease of a patient to be matched and a disease to be treated of a target patient, and the number of patients represents a number of patients whose disease similarity satisfies the disease similarity condition; the target patient refers to a patient currently in need of the target doctor to be determined; and the patient to be matched refers to a patient treated by the doctor to be determined at a historical time.

9. The method of claim 8, wherein, The determining a target doctor from the doctors to be determined based on a disease similarity and a number of patients satisfying a disease similarity condition comprises: determining a disease similarity between the target historical disease treated by the doctor to be determined and the disease to be treated, wherein the disease similarity represents a similarity degree of a disease development trend and / or a disease development speed and / or a disease fluctuation degree between the target historical disease and the disease to be treated; determining, for the doctor to be determined, a number of patients to be matched corresponding to the target historical disease whose disease similarity satisfies the disease similarity condition; determining the target doctor from the doctors to be determined based on the number of patients.

10. The method of claim 9, wherein, The determining a disease similarity between the target historical disease of the patient to be matched and the disease to be treated treated by the doctor to be determined comprises: determining the disease similarity based on the disease development trend and / or the disease development speed and / or the disease fluctuation degree, wherein the disease development trend represents a change before and after a disease condition, the disease development speed represents a deterioration speed of a disease condition, and the disease fluctuation degree represents a stability of a disease condition.

11. The method of claim 10, wherein, The determining the disease similarity based on the disease development trend and / or the disease development speed and / or the disease fluctuation degree comprises: determining a first similarity between the target historical disease and the disease to be treated based on a first disease development trend of the target patient and a second disease development trend of the patient to be matched, wherein the disease development trend comprises the first disease development trend and the second disease development trend, and the first similarity represents a similarity degree of a change before and after a disease condition between the target historical disease and the disease to be treated; and / or determining a second similarity between the target historical disease and the disease to be treated based on a first disease development speed of the target patient and a second disease development speed of the patient to be matched, wherein the disease development speed comprises the first disease development speed and the second disease development speed, and the second similarity represents a similarity degree of a deterioration speed of a disease condition between the target historical disease and the disease to be treated; and / or determining a third similarity between the target historical disease and the disease to be treated based on a first disease fluctuation degree of the target patient and a second disease fluctuation degree of the patient to be matched, wherein the disease fluctuation degree comprises the first disease fluctuation degree and the second disease fluctuation degree, and the third similarity represents a similarity degree of a stability of a disease condition between the target historical disease and the disease to be treated. determine a third similarity between the target historical disease and the to-be-handled disease based on the first disease fluctuation degree of the target patient and the second disease fluctuation degree of the to-be-matched patient, wherein the disease fluctuation degree comprises the first disease fluctuation degree and the second disease fluctuation degree, the third similarity represents a similarity degree of stability of conditions between the target historical disease and the to-be-handled disease, and the disease similarity comprises the first similarity, the second similarity, and / or the third similarity.

12. The method of claim 11, wherein, The method further comprises: determine the first disease development trend based on a first prognostic physical state indicator of the target patient at a first time and a second prognostic physical state indicator of the target patient at a second time, wherein the first time is before the second time; determine the second disease development trend based on a third prognostic physical state indicator of each of the to-be-matched patients at the first time and a fourth prognostic physical state indicator of each of the to-be-matched patients at the second time.

13. The method of claim 12, wherein, The method further comprises: determine the first disease development speed based on the first disease development trend, the first time, and the second time; determine the second disease development speed based on the second disease development trend, the first time, and the second time.

14. The method of claim 13, wherein, The method further comprises: determine the first disease fluctuation degree based on a first number of discrete data of the first disease development trend within the first time to the second time, the first time, and the second time; determine the second disease fluctuation degree based on a second number of discrete data of the second disease development trend within the first time to the second time, the first time, and the second time.

15. An information determination method characterized by comprising: The method comprises: determine value information of a patient based on a first keyword of the patient and occurrence of the first keyword in a target data set, wherein the first keyword represents key information related to attributes and conditions of the patient, and the target data set represents a collection of data related to scientific research of a doctor.

16. The method of claim 15, wherein, The determination of the value information of the patient based on the first keyword of the patient and the occurrence of the first keyword in the target data set comprises: obtain the first keyword; determine occurrence of the first keyword in the target data set, wherein the occurrence represents a position and a frequency of occurrence of each of the first keyword in the target data set; determine the value information of the patient based on the occurrence.

17. The method of claim 16, wherein, The determination of the occurrence of the first keyword in the target data set comprises: determine a position of the first keyword in the target data set and a second keyword in the target data set, and determine a frequency of occurrence of the first keyword in the target data set and the second keyword.

18. The method of claim 17, wherein, The determination of the position of the first keyword in the target data set and the second keyword in the target data set, and the determination of the frequency of occurrence of the first keyword in the target data set and the second keyword, comprise: classify the first keyword to obtain a plurality of categories of keywords; For each type of keyword, determine the occurrence position of each sub-keyword in the target data set and the second keyword; Determine the occurrence frequency of each sub-keyword in the target data set and the second keyword.

19. The method of claim 18, wherein, The value information of the patient is determined based on the occurrence frequency, the second weight corresponding to the occurrence position, and the first weight of each type of keyword. The information determination system comprises:

20. An information determining system characterized by comprising: The scheduling module is configured to determine the event execution time of the to-be-scheduled doctor based on the popularity and fatigue degree of the to-be-screened doctor, wherein the to-be-screened doctor includes the to-be-scheduled doctor, the popularity represents the popularity of the to-be-screened doctor, and the fatigue degree represents the fatigue degree of the physical and / or mental state of the to-be-screened doctor. The reservation module is configured to determine the target doctor from the to-be-determined doctor based on the disease similarity and the number of patients satisfying the disease similarity condition, wherein the disease similarity refers to the similarity between the target historical disease of the to-be-matched patient and the to-be-handled disease of the target patient, the number of patients represents the number of patients satisfying the disease similarity condition, the target patient refers to the patient currently needing to determine the target doctor, and the to-be-matched patient refers to the patient set handled by the to-be-determined doctor at a historical time. The value information determination module is configured to determine the value information of the patient based on the first keyword of the patient and the occurrence of the first keyword in the target data set, wherein the first keyword represents the key information related to the attribute and condition of the patient, and the target data set represents a set of data related to the research of the doctor. The first information determination device comprises:

21. A first information determining apparatus comprising: The first determination unit is configured to determine the event execution time of the to-be-scheduled doctor based on the popularity and fatigue degree of the to-be-screened doctor, wherein the to-be-screened doctor includes the to-be-scheduled doctor, the popularity represents the popularity of the to-be-screened doctor, and the fatigue degree represents the fatigue degree of the physical and / or mental state of the to-be-screened doctor. The second information determination device comprises:

22. A second information determination apparatus comprising: The second determination unit is configured to determine the target doctor from the to-be-determined doctor based on the disease similarity and the number of patients satisfying the disease similarity condition, wherein the disease similarity refers to the similarity between the target historical disease of the to-be-matched patient and the to-be-handled disease of the target patient, the number of patients represents the number of patients satisfying the disease similarity condition, the target patient refers to the patient currently needing to determine the target doctor, and the to-be-matched patient refers to the patient set handled by the to-be-determined doctor at a historical time. The third information determination device comprises:

23. A third information determining apparatus comprising: The third determination unit is configured to determine the value information of the patient based on the first keyword of the patient and the occurrence of the first keyword in the target data set, wherein the first keyword represents the key information related to the attribute and condition of the patient, and the target data set represents a set of data related to the research of the doctor. ​ 24. An information determining apparatus characterized by comprising: The device comprises a processor, a memory and a communication bus; The communication bus is used to realize the communication connection between the processor and the memory; The processor is used to execute the information determination program stored in the memory to realize the steps of the information determination method according to any one of claims 1-7 or 8-14 or 15-19.

25. A computer-readable storage medium, characterized in that, The storage medium stores one or more programs, and the one or more programs can be executed by one or more processors to realize the steps of the information determination method according to any one of claims 1-7 or 8-14 or 15-19.

26. A computer program product comprising a computer program, characterised in that, The computer program realizes the steps of the information determination method according to any one of claims 1-7 or 8-14 or 15-19 when executed by the processor.

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