Information Processing System

Abstract

To provide an information processing system that improves prediction accuracy for data that includes low frequent medical care treatment.SOLUTION: An information processing system includes: a transition information generation unit 110 that generates, on the basis of medical information of a plurality of patients, event transition information for each patient; a medical care process classification unit 111 that classifies, from the generated transition information, on the basis of a threshold related to a frequency of medical care treatment, a process of medical care treatment included in an event into a plurality of groups; a medical care process granularity adjustment unit 112 that aggregates items of the process of medical care treatment in at least a part of the groups; a predictive model generation unit 113 that generates a predictive model for each group; and an output unit 114 that classifies, on the basis of input data of medical information of a new patient, the new patient into one of the groups and outputs, occurrence of an event of the new patient using a prediction model.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an information processing system for processing medical information. [Background technology]

[0002] In recent years, machine learning has been used to predict the occurrence of events such as a patient's life or death on medical information, including patient conditions and medical treatment, and to support doctors' medical treatment based on the results.

[0003] Patent Document 1 describes a method of supporting doctors in their medical practice by selecting multiple medical procedure processes from clinical guidelines, generating a predictive model for each selected medical procedure process, calculating the probability of patient outcomes, and presenting this to the doctor. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] US2014-0058738 Summary of the Invention [Problem to be solved by the invention]

[0005] The aforementioned medical information may contain infrequent medical procedures due to the diversification and complexity of medical treatments accompanying the development of precision medicine. When machine learning is applied to such data, learning is performed based on data with low correlation with the objective variable, and there is a possibility that sufficient prediction accuracy cannot be obtained.

[0006] Therefore, in order to utilize medical information for medical support, it has been necessary to improve the prediction accuracy for data that includes infrequent medical procedures, but this method is not taken into consideration in Patent Document 1. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems, the present invention provides an information processing system for processing medical information, comprising: a transition information generation unit that generates transition information of events for each patient based on medical information of a plurality of patients; a medical process classification unit that classifies the medical procedure processes included in the events into a plurality of groups from the transition information based on a threshold value related to the frequency of the medical procedure; a medical process granularity adjustment unit that aggregates items of the medical procedure processes in at least some of the groups; a predictive model generation unit that generates a predictive model for each of the groups; and an output unit that classifies new patients into one of the groups based on input data of their medical information, and outputs the occurrence of events for the new patients using the predictive model. Effect of the Invention

[0008] According to the present invention, by dividing the medical procedure process into multiple groups, adjusting the granularity of the items in the medical procedure process so as to meet the frequency required in machine learning, and generating a predictive model for each group, it is possible to predict the occurrence of events with high accuracy even from data that includes low-frequency medical procedures. [Brief description of the drawings]

[0009] [Figure 1] 1 is a block diagram showing a hardware configuration of an information processing system according to a first embodiment. [Diagram 2] 4 is a diagram for explaining the data configuration of patient information stored in a patient information storage unit of the system according to the first embodiment. FIG. [Diagram 3] 4 is a diagram for explaining the data configuration of test information stored in a test information storage unit of the system according to the first embodiment. FIG. [Figure 4] 4 is a diagram illustrating a data configuration of diagnostic information stored in a diagnostic information storage unit of the system according to the first embodiment. FIG. [Diagram 5] 3 is a diagram for explaining the data configuration of medical information stored in a medical information storage unit of the system according to the first embodiment. FIG. [Figure 6]3 is a diagram illustrating a data configuration of dictionary information stored in a dictionary information storage unit of the system according to the first embodiment. FIG. [Figure 7] 11 is a flowchart of an analysis subject extraction process in the system according to the first embodiment. [Figure 8] FIG. 4 is a diagram illustrating objective variable information generated in an objective variable generation process of the system according to the first embodiment. [Figure 9] 11 is a flowchart of a transition information generation process of the system according to the first embodiment. [Figure 10] FIG. 11 is a diagram illustrating transition information generated in a transition information generation process of the system according to the first embodiment. [Figure 11] 11 is a flowchart of a medical process classification process of the system according to the first embodiment. [Figure 12] FIG. 13 is a diagram showing the frequency of processes of medical treatments calculated in the medical treatment process classification processing of the system according to the first embodiment. [Figure 13] 11 is a flowchart of a clinical process granularity adjustment process in the system according to the first embodiment. [Figure 14] 11 is a flowchart of a prediction model generation process of the system according to the first embodiment. [Figure 15] 11 is a flowchart of an output process of the system according to the first embodiment. [Figure 16] FIG. 11 is a diagram showing a medical support screen output in the output process of the system according to the first embodiment. [Figure 17] 13 is a flowchart of a medical process classification process of the system according to the second embodiment. [Figure 18] FIG. 13 is a diagram showing the frequency and average information amount of the process of medical treatment calculated in the medical treatment process classification processing of the system according to the second embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings. EXAMPLES

[0011] Example 1 is an information processing system that processes medical information, and includes a transition information generation unit that generates transition information of events for each patient based on medical information of multiple patients, a medical process classification unit that classifies the medical procedure processes included in the events from the transition information into multiple groups based on a threshold value related to the frequency of the medical procedure, a medical process granularity adjustment unit that aggregates items of the medical procedure processes in at least a part of the groups, a predictive model generation unit that generates a predictive model for each of the groups, and an output unit that classifies a new patient's medical information into one of the groups based on input data, and outputs the occurrence of an event for the new patient using the predictive model.

[0012] That is, in the information processing system of this embodiment, the medical treatment process is divided into multiple groups based on a frequency threshold, the granularity of the items of the medical treatment process in the groups that do not meet the threshold is aggregated, and a prediction model is generated for each group. This allows the granularity of the items of the medical treatment process to be adjusted to meet the frequency required in machine learning, and the occurrence of events can be predicted with high accuracy.

[0013] 1 is a block diagram showing a hardware configuration of an information processing system according to an embodiment 1. The information processing system includes a server 101 and a database 102. The server 101 and the database 102 are connected to each other so that the server 101 can access data stored in the database 102.

[0014] The server 101 is a computer having an input device 103, an output device 104, an arithmetic unit 105 that executes programs, a memory 106 that stores the programs, and a storage device 107. The input device 103 is an interface that accepts input to the server 101, such as a mouse and a keyboard. The output device 104 is a display device and a printer, such as a display device and a printer, and outputs the results of the arithmetic unit 105.

[0015] The arithmetic device 105 is a CPU, a GPU, or the like, and executes a program loaded into the memory 106. The memory 106 includes a ROM, which is a non-volatile storage element, and a RAM, which is a volatile storage element. The ROM stores immutable programs (e.g., BIOS) and the like. The RAM is a high-speed, volatile storage element such as a DRAM (Dynamic Random Access Memory), and temporarily stores programs stored in the storage device 107 and data used when the programs are executed. The storage device 107 is a non-volatile storage device such as a magnetic storage device (HDD) and a flash memory (SSD), and stores the programs executed by the arithmetic device 105 and the data used when the programs are executed.

[0016] Specifically, the memory device 107 stores programs for implementing each of the following units: the analysis subject extraction unit 108, the objective variable generation unit 109, the transition information generation unit 110, the medical process classification unit 111, the medical process granularity adjustment unit 112, the prediction model generation unit 113, and the output unit 114.

[0017] The analysis subject extraction unit 108 extracts analysis subjects by executing a predetermined program (see FIG. 7). The objective variable generation unit 109 generates objective variables for each patient by executing a predetermined program (see FIG. 8). The transition information generation unit 110 generates transition information of events for each patient by executing a predetermined program (see FIG. 9). The medical process classification unit 111 classifies the medical procedure processes included in the events into a plurality of groups based on a threshold value related to the frequency of the medical procedure by executing a predetermined program (see FIG. 11). The medical process granularity adjustment unit 112 adjusts the granularity of items of the medical procedure process in the group that does not satisfy the threshold value by executing a predetermined program (see FIG. 13). The prediction model generation unit 113 generates a prediction model for each classified group by executing a predetermined program (see FIG. 14).

[0018] The output unit 114 classifies input data of new patients into one of the groups by executing a predetermined program, and outputs the occurrence of events for the new patients using a prediction model (see FIG. 15). The database 102 stores data for the server 101 to analyze medical information, that is, a patient information storage unit 115 (see FIG. 2), an examination information storage unit 116 (see FIG. 3), a diagnosis information storage unit 117 (see FIG. 4), a medical care information storage unit 118 (see FIG. 5), and a dictionary information storage unit 119 (see FIG. 6).

[0019] 2 is a diagram illustrating the configuration of patient information stored in the patient information storage unit 115 of the embodiment 1. The patient information includes data of a patient ID 201, a sex 202, an age 203, a date of admission 204, a date of discharge 205, and a date of death 206.

[0020] The patient ID 201 is an identifier that uniquely identifies a patient. The gender 202 is the gender of the patient. The age 203 is the age of the patient. The admission date 204 is the date on which the patient was admitted. The discharge date 205 is the date on which the patient was discharged from the hospital. If the patient has not been discharged from the hospital, NULL is assigned. The death date 206 is the date on which the patient died. If the patient has not died, NULL is assigned.

[0021] 3 is a diagram for explaining the configuration of test information stored in the test information storage unit 116 of the system of the first embodiment. The test information includes data of a patient ID 201, a test date 301, a test item 302, a measurement value 303, and a unit 304. The test date 301 is the date on which the doctor performed the test. The test item 302 is the item of the test. The measurement value 303 is the measurement value of the test item 302. The measurement unit 304 is the measurement unit of the test item 302.

[0022] 4 is a diagram for explaining the configuration of the diagnosis information stored in the diagnosis information storage unit 117 of the system of the first embodiment. The diagnosis information includes data of a patient ID 201, a diagnosis date 401, and a disease name 402. The diagnosis date 401 is the date on which a doctor diagnosed the patient's illness. The disease name 402 is the name of the illness.

[0023] 5 is a diagram for explaining the configuration of medical information stored in the medical information storage unit 118 of the first embodiment. The medical information includes data of a patient ID 201, a medical treatment date 501, and a medical treatment item 502. The medical treatment date 501 is the date on which the doctor performed the medical treatment. The medical treatment item 502 is an item of the medical procedure.

[0024] 6 is a diagram for explaining the configuration of dictionary information stored in the dictionary information storage unit 119 of the system of Example 1. The dictionary information includes data of medical care items 502, medical care item classification first level 601, and medical care item classification second level 602.

[0025] The first level medical item classification 601 and the second level medical item classification 602 are both classifications of medical items 502, and the same classification is assigned to medical items 502 that are of the same system in terms of site, action, etc. For example, if the medical item 502 is a medical procedure related to diabetes, such as insulin injection, the first level medical item classification 601 is assigned "A Gastrointestinal tract and metabolism," and the second level medical item classification 602 is assigned "A10 Diabetes medication," which is the next largest classification after the first level medical item classification 601, based on the Anatomical Therapeutic Chemical Classification (ATC classification) created by the World Health Organization.

[0026] 7 is a flowchart of the analysis subject extraction process of the system of Example 1. This analysis subject extraction process is executed by the analysis subject extraction unit 108 of the server 101.

[0027] First, patient information, examination information, diagnosis information, and medical information are acquired (S701). The patient information is acquired from the patient information storage unit 115. The examination information is acquired from the examination information storage unit 116. The diagnosis information is acquired from the diagnosis information storage unit 117. The medical information is acquired from the medical information storage unit 118.

[0028] Next, the name of the disease and the treatment period to be analyzed are specified from the acquired diagnostic information (S702), and patient information, examination information, diagnostic information and treatment information having the specified disease name and treatment period are extracted (S703), and this process is terminated.

[0029] 8 is a diagram for explaining the configuration of objective variable information generated in the objective variable generation process of the system of the first embodiment. This objective variable generation process is executed by the objective variable generation unit 109 of the server 101. An objective variable 801 is an objective variable representing an event to be predicted. For example, when the discharge or death of a patient is to be predicted, if the date of death of a discharged patient is NULL, 0 is assigned as the objective variable representing discharge, and if the date of death is not NULL, 1 is assigned as the objective variable representing death.

[0030] 9 is a flowchart of the transition information generation process of the system of the first embodiment. This transition information generation process is executed by the transition information generation unit 110 of the server 101. First, patient information, medical information, and dictionary information are acquired (S901). The patient information and medical information are extracted by the analysis subject extraction process (FIG. 7). Furthermore, the dictionary information is acquired from the dictionary information storage unit 119.

[0031] Next, one medical care item classification in the dictionary information is selected (S902), and the medical procedure in the medical information is replaced with the selected medical care item classification (S903). Next, transition information that indicates the transition of events for each patient is generated from the acquired patient information and medical information (S904), and this process ends.

[0032] Fig. 10 shows transition information generated in step S904 in Fig. 9. An event occurrence date 1001 and an event 1002 respectively indicate the occurrence date and the content of an event that occurs for each patient. For example, Fig. 10 shows transition information generated when discharge and death in patient information and medical procedures in medical information are regarded as events and replaced with the second level of medical item classification 602. This makes it possible to check the transition of events for each patient.

[0033] 11 is a flowchart of the medical process classification process of the system according to the embodiment 1. This medical process classification process is executed by the medical process classification unit 111 of the server 101.

[0034] First, the transition information is obtained (S1101). The transition information is generated by the transition information generation process (FIG. 9). Next, the frequency of each process of the medical treatment is calculated (S1102).

[0035] Fig. 12 shows the frequency of the medical care process calculated in step S1102 in Fig. 11. A medical care process 1201 is two consecutive medical care processes in the transition information. A frequency 1202 is the frequency of patients in the medical care process 1201.

[0036] Next, a threshold for the frequency of medical care is set (S1103). Next, one patient ID is selected (S1104), and it is determined whether the selected patient ID has a medical care process that satisfies the threshold (S1105). As a result, if the selected patient ID has a medical care process that satisfies the threshold, the transition information of the selected patient ID is classified into a group that satisfies the threshold (S1106). On the other hand, if the selected patient ID does not have a medical care process that satisfies the threshold, the transition information of the selected patient ID is classified into a group that does not satisfy the threshold (S1107). For example, if 200 is set as the threshold for frequency, the transition information of a patient ID that has a medical care process with a frequency of 200 or more is classified into a group that satisfies the threshold. This makes it possible to extract transition information of patients that have a medical care process that satisfies the frequency required in machine learning.

[0037] Next, it is determined whether processing has been completed for all patient IDs (S1108). As a result, if processing has not been completed for some patient IDs, the process returns to step S1104 and the next patient ID is selected. On the other hand, if processing has not been completed for all patient IDs, this process ends.

[0038] FIG. 13 is a flowchart of the clinical process granularity adjustment process of the system according to the first embodiment. This clinical process granularity adjustment process is executed by the clinical process granularity adjustment unit 112 of the server 101.

[0039] First, the transition information and dictionary information of the group not satisfying the threshold are obtained (S1301). The transition information of the group not satisfying the threshold is obtained from the medical process classification process (FIG. 11). Furthermore, the dictionary information is obtained from the dictionary information storage unit 119.

[0040] Next, one unselected major category is selected from among the medical item classifications in the dictionary information (S1302), and the medical procedure in the transition information is replaced (S1303). For example, if the medical item classification second level 602 in the dictionary information was selected in the previous process, the medical item classification first level 601 is selected in step S1302, and replacement with the medical item classification first level 601 is performed in S1303. This makes it possible to increase the frequency of each medical procedure process for transition information classified into a group that does not satisfy the threshold in the medical process classification process.

[0041] Next, a medical care process division process is executed (S1304), and it is determined whether there is an unselected major category in the medical care item classification of the dictionary information (S1305). If there is an unselected major category, the process returns to step S1301, and the transition information of the group that does not satisfy the threshold value extracted in step S1304 is obtained. On the other hand, if there is no unselected major category, this process ends.

[0042] 14 is a flowchart of the prediction model generation process of the system of Example 1. This prediction model generation process is executed by the prediction model generation unit 113 of the server 101.

[0043] First, patient information, examination information, diagnosis information, objective variable information, and transition information of all groups are obtained (S1401). Patient information, examination information, and diagnosis information are extracted by the analysis subject extraction process (Fig. 7). Moreover, objective variable information is generated by the objective variable generation process. Moreover, transition information of all groups is obtained from the medical process classification process (Fig. 11) and medical process adjustment process (Fig. 13).

[0044] Next, for each group of acquired transition information, feature information is generated from the patient information, examination information, diagnosis information, and transition information (S1402), and machine learning is performed based on each feature information and objective variable information (S1403), and this process ends. At this time, a different machine learning algorithm may be used for each group of transition information. Specifically, overlearning can be suppressed by changing the complexity of the machine learning algorithm in consideration of the frequency of the medical procedure process.

[0045] 15 is a flowchart of the output process of the system of Example 1. This output process is executed by the output unit 114 of the server 101.

[0046] First, new patient information and transition information of all groups are obtained (S1501). New patient information is input from the input device 103. In addition, transition information of all groups is obtained from the clinical process classification process (FIG. 11) and the clinical process granularity adjustment process (FIG. 13).

[0047] Next, transition information and feature information are generated from the acquired information (S1502), and new patients are classified into one of the groups based on the transition information (S1503). This makes it possible to select an appropriate prediction model according to the type of medical procedure process. Next, feature information is input into the prediction model generated for the classified group, and the occurrence of events for new patients is output (S1504), and this process ends.

[0048] 16 shows a medical support screen output in the output process of the system of Example 1. The medical support screen is made up of an analysis condition area 1601 and an analysis result area 1602.

[0049] The analysis condition area 1601 is composed of a feature amount information input area 1603, a transition information input area 1604, and an analysis execution button 1605. A new patient can input information into the feature amount information input area 1603 and the transition information input area 1604, and click the analysis execution button 1605 to execute output processing.

[0050] The analysis result area 1602 is composed of a risk of death 1606, a medical item classification 1607, a medical item granularity 1608, and an event transition 1609, and is displayed by clicking the analysis execution button 1605.

[0051] The risk of death 1606 is the probability of an event occurring that is output from the executed machine learning model. This makes it possible to display the risk of death for a new patient.

[0052] Medical care item classification 1607 and medical care item granularity 1608 are the dictionary information and medical care item classification names explained in Fig. 6. Here, an example is shown in which Anatomical Therapeutic Classification is selected as the dictionary information and medical care item classification second level is selected as the medical care item classification. This makes it possible to confirm the granularity of items in the medical care procedure process displayed in event transition 1609, which will be described later.

[0053] An event transition 1609 is an example of visualization of transition information of a group to which an executed machine learning model belongs. By displaying the event transition and frequency, it is possible to easily confirm the difference in medical performance due to the medical treatment process.

[0054] As described above, in the system of the first embodiment, the medical treatment process is divided into a plurality of groups based on a frequency threshold, the granularity of the items of the medical treatment process in the groups that do not meet the threshold is aggregated, and a prediction model is generated for each group. This allows the granularity of the items of the medical treatment process to be adjusted so as to satisfy the frequency required in machine learning, and the occurrence of an event can be predicted with high accuracy. EXAMPLES

[0055] The system of the second embodiment divides the medical treatment process into multiple groups based on thresholds related to frequency and average information amount (entropy), aggregates the granularity of items in the medical treatment process in groups that do not meet the thresholds, and generates a prediction model for each group. This adjusts the granularity of items in the medical treatment process so as to satisfy the frequency required in machine learning and reduce the uncertainty related to the objective variable, making it possible to predict the occurrence of events with high accuracy.

[0056] The hardware configuration of the information processing system of the second embodiment is the same as that of the system of the first embodiment, and therefore the description thereof will be omitted. The analysis subject extraction process, the objective variable generation process, and the transition information generation process of the system of the second embodiment are the same as those of the system of the first embodiment, and therefore the description thereof will be omitted.

[0057] In the analysis subject extraction process of the system of the second embodiment, an analysis subject is extracted. In the response variable generation process of the system of the second embodiment, a response variable is generated for each patient. In the transition information generation process of the system of the second embodiment, event transition information is generated for each patient.

[0058] 17 is a flowchart of the medical process classification process of the system of Example 2. This medical process classification process is executed by the medical process classification unit 111 of the server 101.

[0059] First, the transition information is acquired (S1701). The transition information is generated by the transition information generation process (FIG. 9). Next, the frequency and the average information amount are calculated for each process of the medical treatment (S1702).

[0060] Fig. 18 shows the frequency and average information amount of the medical procedure process calculated in step S1702 in Fig. 17. The average information amount 1801 is calculated from the ratio of discharged patients and the ratio of deceased patients in the medical procedure process 1201, and is 0 when all patients have been discharged or died, and is 1 when the ratio of discharged patients and the ratio of deceased patients are equal. This makes it possible to quantify the uncertainty of patient discharge or death in the medical procedure process. Next, thresholds are set for the frequency and average information amount of the medical procedure (S1703).

[0061] Next, one patient ID is selected (S1704), and it is determined whether the selected patient ID has a medical procedure process that satisfies the threshold (S1705). As a result, if the selected patient ID has a medical procedure process that satisfies the threshold, the transition information of the selected patient ID is classified into a group that satisfies the threshold (S1706). On the other hand, if the selected patient ID does not have a medical procedure process that satisfies the threshold, the transition information of the selected patient ID is classified into a group that does not satisfy the threshold (S1707). For example, when 200 and 0.4 are set as the thresholds for frequency and average information amount, respectively, the transition information of a patient ID having a medical procedure with a frequency of 200 or more and an average information amount of 0.4 or less is classified into a group that satisfies the threshold. This makes it possible to extract transition information of a patient having a medical procedure process that satisfies the frequency required in machine learning and that is easy to predict the patient's discharge or death.

[0062] Next, it is determined whether processing has been completed for all patient IDs (S1708). As a result, if processing has not been completed for some patient IDs, the process returns to step S1704 and the next patient ID is selected. On the other hand, if processing has not been completed for all patient IDs, this process ends.

[0063] The clinical process granularity adjustment process, prediction model generation process, and output process of the system of the second embodiment are the same as those of the system of the first embodiment, and therefore will not be described here. In the clinical process granularity adjustment process of the system of the second embodiment, the granularity of the items of the process of the clinical practice in the group that does not satisfy the threshold value is adjusted.

[0064] In the prediction model generation process of the system of the second embodiment, a prediction model is generated for each classified group. In the output process of the system of the second embodiment, input data of new patients is classified into one of the groups, and the occurrence of an event for the new patient is output using the prediction model.

[0065] As described above, in the system of the second embodiment, the medical procedure process is divided into a plurality of groups based on the thresholds for frequency and average information amount, the granularity of the items of the medical procedure process in the groups that do not meet the thresholds is aggregated, and a prediction model is generated for each group. This adjusts the granularity of the items of the medical procedure process so as to satisfy the frequency required in machine learning and reduce the uncertainty regarding the objective variable, thereby making it possible to predict the occurrence of an event with high accuracy.

[0066] In the systems of the first and second embodiments, the frequency and average information amount of two consecutive medical care procedures in the transition information are calculated as the medical care process classification process, but the frequency and average information amount of all consecutive medical care procedures in the transition information may be calculated. This makes it possible to extract transition information of patients who have a medical care procedure process that satisfies the frequency required in machine learning for all medical care procedures and that makes it easy to predict the patient's discharge or death.

[0067] In the systems of the first and second embodiments, thresholds for the frequency and average information volume of medical procedures are set as part of the medical procedure classification process, but an optimal threshold may be selected from preset threshold candidates. For example, the medical procedure process may be vectorized, the average similarity between the medical procedure processes in a group that satisfies the threshold may be calculated for each threshold candidate, and the threshold that maximizes the average similarity may be selected. This allows the threshold to be optimized so as to extract transition information that is highly similar and homogeneous between the medical procedure processes.

[0068] In the systems of the first and second embodiments, the transition information is classified by determining whether the selected patient ID has a medical treatment process that satisfies a threshold value as the medical treatment process classification process, but other methods may be used. For example, the transition information may be classified by setting a threshold value for the parameters of a process mining algorithm such as a heuristic miner and extracting a medical treatment process that is potentially strongly causally related. This allows machine learning to be performed in the prediction model generation process based on data that is highly correlated with the target variable. [Explanation of symbols]

[0069] 101 Server 102 Database 103 Input Device 104 Output Device 105 Arithmetic equipment 106 Memory 107 Storage device 108 Analysis target extraction section 109 Objective variable generation unit 110 Transition information generation section 111 Medical Treatment Process Classification Section 112 Medical Process Granularity Adjustment Department 113 Prediction model generation unit 114 Output section 115 Patient information storage unit 116 Examination information storage unit 117 Diagnostic information storage unit 118 Medical information storage unit 119 Dictionary information storage unit

Claims

1. An information processing system for processing medical information, a transition information generating unit that generates transition information of events for each patient based on medical information of the plurality of patients; a medical care process classification unit that classifies the processes of medical care included in the event into a plurality of groups based on a threshold value related to a frequency of the medical care from the transition information; a medical procedure granularity adjustment unit that aggregates items of the medical procedure process in at least a part of the group; a prediction model generation unit that generates a prediction model for each of the groups; and an output unit that classifies a new patient into one of the groups based on input data of the new patient's medical information, and outputs the occurrence of an event of the new patient using the prediction model. An information processing system comprising:

2. 2. The information processing system according to claim 1, the prediction model generation unit changes a generation means of a previous-period prediction model for each of the groups based on the threshold value related to the frequency. An information processing system comprising:

3. 3. The information processing system according to claim 1, The medical process classification unit classifies the medical procedure processes included in the event into a plurality of groups based on the transition information, based on thresholds related to the frequency of the medical procedure and an average amount of information.

4. 4. The information processing system according to claim 1, The output unit classifies the new patient into one of the groups based on input data of medical information of the new patient, and outputs the occurrence of an event of the new patient using the prediction model. An information processing system comprising:

5. 5. The information processing system according to claim 1, the output unit classifies a new patient into one of the groups based on input data of medical information of the new patient, and outputs the transition information of the group using the prediction model. An information processing system comprising:

6. 6. The information processing system according to claim 1, The method further includes an analysis subject extraction unit that extracts medical information having a designated disease name and treatment period from the medical information of the patient. An information processing system comprising:

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