Information processing device, information processing method, and computer program

Abstract

This information processing device is for predicting a prognosis of a subject patient affected by a disease, and comprises a model acquisition unit, a subject patient information acquisition unit, and a prognosis prediction execution unit. The model acquisition unit acquires a prognosis prediction model that is a machine learning model which uses, as an input, time-series information indicating time-series transition of factors of the disease, and as an output, the prognosis of the disease. The subject patient information acquisition unit acquires time-series information about the subject patient. The prognosis prediction execution unit executes prognosis prediction of the subject patient by using the time-series information about the subject patient and the prognosis prediction model, and outputs the result of the prognosis prediction.

Description

[Technical Field]

[0001] The technology disclosed in this specification relates to information processing for predicting the prognosis of a patient suffering from a disease. [Background technology]

[0002] Interstitial pneumonia is a general term for chronic, progressive fibrotic lung diseases. Acute exacerbation of interstitial pneumonia is a condition in which the condition rapidly worsens within one month, and the prognosis is extremely poor, with an in-hospital mortality rate of approximately 50%. If acute exacerbation of interstitial pneumonia could be predicted with high accuracy, it would be possible to suppress the onset of the disease with anti-fibrotic drugs, or improve the prognosis through early diagnosis and therapeutic intervention.

[0003] A clinical model has been proposed for predicting the risk of acute exacerbation in patients with idiopathic pulmonary fibrosis, a type of interstitial pneumonia (see, for example, Non-Patent Document 1). [Prior art documents] [Non-patent literature]

[0004] [Non-Patent Document 1] Qi Wu et al., "A Clinical Model for the Prediction of Acute Exacerbation Risk in Patients with Idiopathic Pulmonary Fibrosis," BioMed Research International, Hindawi, 2020, pp. 1-6 Summary of the Invention [Problem to be solved by the invention]

[0005] The progression of interstitial pneumonia varies from patient to patient, and the patient's condition changes over time. The conventional prediction models described above do not take into account the chronological changes in disease factors, including the patient's condition, resulting in low prediction accuracy. This issue is not limited to prediction of acute exacerbation of interstitial pneumonia, but is common to disease prognosis prediction in general.

[0006] This specification discloses a technique that can solve the above-mentioned problems. [Means for solving the problem]

[0007] The technology disclosed in this specification can be realized, for example, in the following forms.

[0008] (1) The information processing device disclosed in this specification is a device for predicting the prognosis of a target patient suffering from a disease, and includes a model acquisition unit, a target patient information acquisition unit, and a prognosis prediction execution unit. The model acquisition unit acquires a prognosis prediction model, which is a machine learning model that receives time-series information indicating the chronological changes in disease factors and outputs the prognosis of the disease. The target patient information acquisition unit acquires time-series information about the target patient. The prognosis prediction execution unit uses the time-series information about the target patient and the prognosis prediction model to predict the prognosis of the target patient and outputs the prognosis prediction result.

[0009] According to this information processing device, it is possible to predict the prognosis of a disease for each individual patient based on time-series information showing the time-series changes in disease factors, and to predict the prognosis of the disease with high accuracy.

[0010] (2) In the information processing device, the disease factors may include environmental factors. By adopting this configuration, the prognosis of a disease can be predicted with higher accuracy by using information indicating time-series changes in environmental factors that can have a significant impact on the prognosis of the disease.

[0011] (3) In the information processing device, the disease factors may include environmental factors of the patient's residence. By adopting this configuration, the prognosis of the disease can be predicted with higher accuracy by using information indicating time-series changes in factors related to the environment to which the patient is primarily exposed, compared to using environmental factors of the location of the hospital to which the patient is treated, for example.

[0012] (4) In the information processing device, the environmental factors of the patient's residential location may be environmental factors of a location within a straight-line distance of 200 km from the patient's current address. By using environmental factors of a location relatively close to the patient's current address, information that more accurately indicates the time-series changes in factors related to the environment to which the patient is primarily exposed can be used, enabling the prognosis of the disease to be predicted with extremely high accuracy.

[0013] (5) In the information processing device, the environmental factors may include at least one of the presence of environmental pollutants and meteorological parameters. By adopting this configuration, the prognosis of a disease can be predicted with higher accuracy by using information indicating the time-series changes in environmental factors that can have a significant impact on the prognosis of the disease.

[0014] (6) In the information processing device, the time-series information may include information indicating the amount of change in the environmental factor. By adopting this configuration, the prognosis of a disease can be predicted with higher accuracy by using the information indicating the amount of change in the environmental factor that may have a significant impact on the prognosis of the disease.

[0015] (7) In the information processing device, the time-series information may be information specifying the values ​​of the disease factors at regular time intervals. By adopting this configuration, it is possible to predict the prognosis of the disease with higher accuracy than when information specifying the values ​​of the disease factors at irregular intervals is used.

[0016] (8) In the information processing device, the time-series information may include information specifying at least monthly values ​​of the disease factors. By adopting this configuration, the prognosis of the disease can be predicted with higher accuracy by using the time-series information showing monthly changes in the disease factors.

[0017] (9) In the information processing device, the prognosis of the disease may include the occurrence of multiple events that are in a competing risk relationship, and the prognosis prediction model may be a model trained using a machine learning algorithm corresponding to the multiple events that are in a competing risk relationship. By adopting this configuration, the occurrence of multiple events that are in a competing risk relationship can be predicted with high accuracy.

[0018] (10) In the information processing device, the prognosis prediction model may be configured to output, as the prognosis of the disease, an index value representing the likelihood of an event occurring for a plurality of events in a competing risk relationship. By adopting this configuration, it is possible to predict the occurrence of a plurality of events in a competing risk relationship with high accuracy.

[0019] (11) In the information processing device, the plurality of events having a competing risk relationship may include an acute exacerbation and death. By adopting this configuration, it is possible to predict with high accuracy the occurrence of each of the competing risks, namely, acute exacerbation and death.

[0020] (12) In the information processing device, the disease may be a respiratory or circulatory disease. By adopting this configuration, it is possible to predict the prognosis of a respiratory or circulatory disease with high accuracy using time-series information that indicates a chronological change in factors of the respiratory or circulatory disease.

[0021] (13) In the information processing device, the prognosis prediction execution unit may be configured to execute a virtual prognosis prediction for the target patient using virtual information obtained by changing a part of the time-series information for the target patient and the prognosis prediction model, predict the effect of an intervention corresponding to the change based on the prediction result of the actual prognosis and the prediction result of the virtual prognosis, and output the prediction result of the effect of the intervention. By adopting this configuration, it is possible to determine whether or not to actually perform the intervention based on the output prediction result of the effect of the intervention.

[0022] The technology disclosed in this specification can be realized in various forms, such as an information processing device, an information processing method, a computer program that realizes those methods, a non-transitory recording medium on which that computer program is recorded, etc. [Brief explanation of the drawings]

[0023] [Figure 1] An explanatory diagram conceptually showing the prognosis prediction model MO in this embodiment. [Figure 2] An explanatory diagram showing how to predict the effect of intervention based on prognosis prediction results [Figure 3] FIG. 1 is an explanatory diagram showing a schematic configuration of an information processing device 100. [Figure 4] 1 is a flowchart showing a prognosis prediction model acquisition process according to this embodiment. [Figure 5] An explanatory diagram showing specific examples of factors (features) for interstitial pneumonia [Figure 6] An explanatory diagram conceptually illustrating interpolation processing for original information Io. [Figure 7] FIG. 10 is an explanatory diagram showing an example of learning data LD obtained through preprocessing. [Figure 8] Conceptual diagram of a model using LSTM [Figure 9] 1 is a flowchart showing a prognosis prediction process according to this embodiment. [Figure 10] An explanatory diagram showing the prediction accuracy of the prognosis prediction model MO of the embodiment. [Figure 11]Another explanatory diagram showing the prediction accuracy of the prognosis prediction model MO of the embodiment. [Figure 12] Another explanatory diagram showing the prediction accuracy of the prognosis prediction model MO of the embodiment. [Figure 13] An explanatory diagram showing the prediction accuracy of the prognosis prediction model MO of the embodiment. [Figure 14] FIG. 1 is an explanatory diagram showing an example of a prognosis prediction result using the prognosis prediction model MO of the embodiment. [Figure 15] FIG. 10 is an explanatory diagram showing the prediction accuracy of the prognosis prediction model MO according to another embodiment. [Figure 16] An explanatory diagram showing an example of the importance of each disease factor in an example. [Figure 17] FIG. 10 is an explanatory diagram showing an example of the importance of each factor of a disease in another embodiment. [Figure 18] An explanatory diagram showing the relationship between the upper limit of the straight-line distance from the patient's current address to the measurement station and prediction accuracy. [Figure 19] An explanatory diagram showing the relationship between the upper limit of the straight-line distance from the patient's current address to the measurement station and prediction accuracy. DETAILED DESCRIPTION OF THE INVENTION

[0024] A. Implementation: A-1. Overview of the prognostic prediction model MO: First, an overview of the prognosis prediction model MO in this embodiment will be described. FIG. 1 is an explanatory diagram conceptually showing the prognosis prediction model MO in this embodiment. The prognosis prediction model MO is a model for predicting the prognosis of a patient suffering from a disease. The prognosis prediction model MO is a machine learning model that receives time-series information indicating the time-series changes in disease factors (features) as input and outputs the prognosis of the disease. In this specification, machine learning refers to a general term for techniques and methods for finding rules and patterns by using a computer to learn based on large amounts of data (i.e., in a data-driven manner), and includes deep learning.

[0025] In this embodiment, interstitial pneumonia is used as a specific example of a disease. Events of interstitial pneumonia include acute exacerbation and death. Because an acute exacerbation event does not occur after a death event occurs, the two events can be said to be in a competing risk relationship.

[0026] Factors (features) for interstitial pneumonia include, for example, patient background (smoking status, BMI, etc.), test findings (blood test, chest CT image, etc.), and environmental factors (NO2, PM 2.5 These include environmental pollutants such as urinary tract infections, meteorological parameters such as temperature, and treatment information (administration of antifibrotic agents, etc.). Time-series information showing the time-series changes in these factors is, for example, information that specifies the values ​​and amounts of change of these factors at regular time intervals (e.g., monthly) over a certain period (e.g., the period from the initial diagnosis to the Mth month).

[0027] In the prognosis prediction using the prognosis prediction model MO, for example, an index value indicating the possibility of each event occurring is calculated. In the example shown in FIG. 1, the probability of acute exacerbation and death occurring at a certain timing (e.g., M+N months) is calculated as the prognosis prediction. However, the prognosis prediction may be performed in other ways. For example, the prognosis prediction may involve classifying the patient's predicted state (survival, acute exacerbation, death) based on the probability of acute exacerbation and death occurring at a certain timing.

[0028] By using the prognosis prediction model MO of this embodiment, it is possible to accurately predict the prognosis of a disease for an individual patient based on time-series information showing the chronological changes in disease factors. The prognosis prediction results can be used for various purposes. For example, for patients predicted to have a high probability of developing acute exacerbation, it is possible to administer anti-fibrotic drugs to suppress the onset of the disease, or to perform early diagnosis and therapeutic intervention to improve the prognosis.

[0029] Furthermore, as shown in Figure 2, the effect of intervention can be predicted based on the prognosis prediction results. The upper part of Figure 2 shows an example of a prediction result of the probability of occurrence of acute exacerbation and death based on actual time-series information. The lower part of Figure 2 shows an example of a hypothetical prediction result of the probability of occurrence of acute exacerbation and death based on virtual information in which part of the time-series information has been changed to correspond to an expected intervention (e.g., smoking cessation, medication, rehabilitation, nutritional therapy, etc.). Based on the prognosis prediction result based on the actual time-series information and the prognosis prediction result based on the virtual information, the effect of intervention corresponding to the above changes (e.g., XX% reduction in the risk of acute exacerbation, YY% reduction in the risk of death) can be predicted. Based on the output predicted result of the effect of intervention, a decision can be made as to whether or not to actually perform intervention.

[0030] A-2. Configuration of information processing device 100: Next, we will explain the configuration of an information processing device 100 for creating a prognosis prediction model MO and executing prognosis prediction using the prognosis prediction model MO. Figure 3 is an explanatory diagram showing a schematic configuration of the information processing device 100. The information processing device 100 is composed of a computer (PC, server, etc.).

[0031] The information processing device 100 includes a control unit 110, a storage unit 120, a display unit 130, an operation input unit 140, and an interface unit 150. These units are connected to each other so as to be able to communicate with each other via a bus 190. The information processing device 100 may also include a speaker as an output means.

[0032] The display unit 130 of the information processing device 100 is configured, for example, by a liquid crystal display or the like, and displays various images and information. The operation input unit 140 is configured, for example, by a keyboard, mouse, buttons, a microphone, a trackpad, or the like, and receives operations and instructions from an administrator. The display unit 130 may also function as the operation input unit 140 by being equipped with a touch panel. The interface unit 150 is configured, for example, by a LAN interface, a USB interface, or the like, and communicates with other devices via wired or wireless connections.

[0033] The storage unit 120 of the information processing device 100 is configured with, for example, a ROM, a RAM, a hard disk drive (HDD), etc., and is used to store various programs and data, and as a work area when executing various programs, and as a temporary storage area for data. For example, the storage unit 120 stores a prognosis prediction program CP, which is a computer program for executing the prognosis prediction model acquisition process and the prognosis prediction process described below. The prognosis prediction program CP is provided in a state stored in a computer-readable recording medium (not shown), such as a CD-ROM, a DVD-ROM, or a USB memory, or is provided in a state retrievable from an external device (a server or other terminal device on a network) via the interface unit 150, and is stored in the storage unit 120 in a state operable on the information processing device 100.

[0034] Furthermore, learning data LD, prognosis prediction model MO, target patient information Ip, and prognosis prediction result data RD are stored in the storage unit 120 of the information processing device 100 in advance or during the execution of the prognosis prediction model acquisition process and prognosis prediction process described below. The contents of this information and data will be explained together with the explanation of the prognosis prediction model acquisition process and prognosis prediction process described below.

[0035] The control unit 110 of the information processing device 100 is configured with, for example, a CPU, and controls the operation of the information processing device 100 by executing a computer program read from the storage unit 120. For example, the control unit 110 reads a prognosis prediction program CP from the storage unit 120 and executes it, thereby functioning as a raw information acquisition unit 111, a learning data acquisition unit 112, a model acquisition unit 113, a target patient information acquisition unit 114, and a prognosis prediction execution unit 119 for executing a prognosis prediction model acquisition process and a prognosis prediction process, which will be described later. The functions of each of these units will be described in conjunction with the prognosis prediction model acquisition process and the prognosis prediction process, which will be described later.

[0036] A-3. Prognostic prediction model acquisition process: Next, a prognosis prediction model acquisition process executed by the information processing device 100 of this embodiment will be described. FIG. 4 is a flowchart showing the prognosis prediction model acquisition process in this embodiment. The prognosis prediction model acquisition process is a process for acquiring a prognosis prediction model MO, which is a machine learning model for predicting the prognosis of a patient suffering from a disease (interstitial pneumonia). In this embodiment, the information processing device 100 acquires the prognosis prediction model MO by creating the prognosis prediction model MO by a predetermined machine learning method. The prognosis prediction model acquisition process is started in response to a start instruction being input by a user operating the operation input unit 140 of the information processing device 100.

[0037] First, the raw information acquisition unit 111 (FIG. 3) of the information processing device 100 acquires information used to create the prognosis prediction model MO (hereinafter referred to as "raw information Io") (S110). The raw information Io is information that serves as the basis for the learning data LD used in training, verifying, and testing the prognosis prediction model MO. Specifically, the raw information Io is information in which time-series information indicating the time-series changes in factors (features) of interstitial pneumonia is associated with information indicating prognosis for multiple patients affected by interstitial pneumonia. The raw information Io is acquired via the interface unit 150 or the operation input unit 140.

[0038] 5 is an explanatory diagram showing specific examples of factors (feature amounts) of interstitial pneumonia. In this embodiment, 44 factors classified into four types, namely, patient background, test findings, environmental factors, and treatment information, are used as factors of interstitial pneumonia.

[0039] Patient background information includes, for example, the following 12 factors: Patient background information can be obtained, for example, through interviews and examinations. Age, BMI, GAP score, current smoker, former smoker, IPF, PPFE, SSc, collagen disease, gender, CCI≥3, mMRC≥2

[0040] Test findings include, for example, the following 18 factors: Test finding information is obtained, for example, from blood tests, chest CT images, etc. LDH, BNP, WBC, neutrophils, lymphocytes, eosinophils, albumin, KL-6, SP-D, FVC (%pred), FEV1 (%pred), DLco (%pred), 6-minute walking distance, PCO2, PO2, minimum SpO2, logCRP, CT image UIP pattern

[0041] The environmental factors are factors related to the environment of the patient's residence, and include, for example, the presence (e.g., concentration) of environmental pollutants and meteorological parameters. More specifically, the environmental factors include, for example, the following 10 factors: NO2, NO, SO2, PM 2.5 , SPM, precipitation, temperature, season (autumn, summer, winter)

[0042] Examples of environmental factors that can be used include representative values ​​for a certain period of time (e.g., monthly average values, daily average values), the amount of change for a certain period of time (e.g., the amount of change in the monthly average values, the amount of change in the daily average values), and / or the number of times that the standard value is exceeded for a certain period of time (e.g., the number of days per month that the environmental standard value is exceeded, the number of hours per day that the environmental standard value is exceeded).

[0043] The patient's place of residence may be the region to which the patient's current address belongs. In this case, information regarding environmental pollutants among the environmental factors can be obtained, for example, by referencing measurement data from a measurement station located in that region. Specifically, the measurement data from the measurement station closest in a straight line to the patient's current address is referenced. If there is no measurement station within a predetermined upper limit distance (e.g., 100 km) from the patient's current address, data is deemed missing for the environmental factor for which measurement data from the measurement station was referenced. Measurement station data can be obtained, for example, from the website of the National Institute for Environmental Studies. Furthermore, information regarding meteorological parameters (precipitation, temperature, season) among the environmental factors can be obtained, for example, from the website of the Japan Meteorological Agency.

[0044] The patient's residence may also be the room where the patient lives (e.g., a clean room). In this case, at least some of the information on the environmental factors can be obtained by, for example, referring to measurement data from a sensor installed in the room or a sensor attached to the patient.

[0045] The information on environmental factors may be acquired by referring to measurements taken by artificial satellites or predicted values ​​obtained by weather simulations.

[0046] The treatment information includes, for example, the following four factors: The treatment information is obtained, for example, from records of treatment results. Prednisolone, calcineurin inhibitor, immunosuppressant, antifibrotic agent

[0047] Next, the learning data acquisition unit 112 (FIG. 3) of the information processing device 100 performs preprocessing on the original information Io to acquire learning data LD (S120 in FIG. 4). The preprocessing may include, for example, interpolation, outlier removal, data extension, etc.

[0048] 6 is an explanatory diagram conceptually illustrating interpolation processing for raw information Io. As shown in the upper part of FIG. 6, before the interpolation processing, the timing of data on factors obtained by each test (e.g., pulmonary function test, blood test, chest CT image) is different for each patient, and this is due to the fact that the tests are performed at different times. Therefore, in this embodiment, interpolation processing is performed so that data at regular time intervals is obtained for all factors. The same applies to factors other than those obtained by tests.

[0049] It is preferable to select and execute an interpolation process suited to the characteristics of each factor. For example, for factors obtained from similar test items, interpolation can be performed using multiple regression analysis based on groups created using similar test items. For factors that are highly likely to be estimated from preceding and following values, linear interpolation (interpolation) or nearest neighbor interpolation (extrapolation) can be used. For factors that exhibit rapid fluctuations over a short period of time (e.g., CRP), nearest neighbor interpolation (interpolation, extrapolation) can be used. For categorical variables, nearest neighbor interpolation (interpolation, extrapolation) can be used. For factors with low correlation between time and data (e.g., environmental pollutants), interpolation can be omitted and data can be left missing. By performing such interpolation, data at regular time intervals can be obtained for each factor without compromising the characteristics of each factor.

[0050] FIG. 7 is an explanatory diagram showing an example of training data LD obtained after preprocessing. The training data LD is data in which time-series information (monthly data in the example of FIG. 7) showing the time-series changes of each factor (feature) is associated with information (correct label) showing the prognosis at each timing. FIG. 7 shows data of a patient who developed acute exacerbation t months after initial diagnosis. When predicting the onset of acute exacerbation within t months, in this patient's data, from the 1st month to the (s-1)th month, the "survival" label is "1" and the remaining labels are "0", and from the sth month onwards, the "acute exacerbation" label is "1" and the remaining labels are "0". Note that zero padding is used after the tth month.

[0051] Next, the model acquisition unit 113 (FIG. 3) of the information processing device 100 creates a prognosis prediction model MO by machine learning using the learning data LD (S130 in FIG. 4). Various known machine learning algorithms can be used for the machine learning to create the prognosis prediction model MO. For example, a long short-term memory (LSTM) may be used to create the prognosis prediction model MO. FIG. 8 is an explanatory diagram conceptually showing a model using LSTM. The LSTM is an improved version of a recurrent neural network (RNN) that can handle time-series information in order to solve the gradient vanishing problem. As shown in FIG. 8, in creating the prognosis prediction model MO using LSTM, the feature quantity X t The output value y when t and the correct label Y t The model is updated so that the loss calculated from the above is reduced.

[0052] Alternatively, a Dynamic-DeepHit model may be used to create a prognosis prediction model MO. The Dynamic-DeepHit model is a known machine learning algorithm that supports multiple events that are in a competing risk relationship. As described above, acute exacerbation and death, which are events of interstitial pneumonia, are in a competing risk relationship. Therefore, by using a machine learning algorithm that supports multiple events that are in a competing risk relationship, a prognosis prediction model MO with high prediction accuracy can be created. Details of the Dynamic-DeepHit model are described in, for example, the following literature. Lee Changhee and three others, "DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks," Proceedings of the 31st AAAI Conference on Artificial Intelligence, Association for the Advancement of Artificial Intelligence, 2018, pp. 2314-2321

[0053] The prognosis prediction model MO created by machine learning is stored in the storage unit 120 of the information processing device 100. This completes the process of acquiring the prognosis prediction model MO (FIG. 4). When creating the prognosis prediction model MO, for example, a portion of the learning data LD is used as training data for updating the model parameters (weights, etc.), another portion of the learning data LD is used as verification data for setting hyperparameters, and another portion of the learning data LD is used as test data for checking the generalization performance of the model.

[0054] A-4. Prognosis prediction processing: Next, a prognosis prediction process executed by the information processing device 100 of this embodiment will be described. Fig. 9 is a flowchart showing the prognosis prediction process in this embodiment. The prognosis prediction process is a process for predicting the prognosis (predicting the risk of acute exacerbation and death) of a patient suffering from interstitial pneumonia using a prognosis prediction model MO. The prognosis prediction process is started in response to a start instruction input by a user operating the operation input unit 140 of the information processing device 100.

[0055] First, the target patient information acquisition unit 114 (FIG. 3) of the information processing device 100 acquires target patient information Ip (S310). The target patient information Ip is the above-mentioned time-series information about the patient who is the target of the prognosis prediction process. The target patient information Ip is acquired via the interface unit 150 or via the operation input unit 140, and stored in the storage unit 120.

[0056] Next, the prognosis prediction execution unit 119 (FIG. 3) of the information processing device 100 executes prognosis prediction for the target patient using the target patient information Ip and the prognosis prediction model MO (S320). That is, the prognosis prediction execution unit 119 inputs the target patient information Ip into the prognosis prediction model MO, thereby acquiring the prognosis prediction result output from the prognosis prediction model MO. The prognosis prediction execution unit 119 generates prognosis prediction result data RD, which is information indicating the prognosis prediction result, and stores it in the storage unit 120 of the information processing device 100.

[0057] Next, the prognosis prediction execution unit 119 outputs the prognosis prediction result based on the prognosis prediction result data RD (S330). For example, the prognosis prediction execution unit 119 causes the prognosis prediction result to be displayed on the display unit 130. This completes the prognosis prediction process.

[0058] For example, doctors can refer to the displayed prognosis prediction results and, for patients predicted to have a high probability of developing acute exacerbations, administer anti-fibrotic drugs to suppress the onset of the disease or perform early diagnosis and therapeutic intervention to improve the prognosis.

[0059] 2, the prognosis prediction execution unit 119 may execute a virtual prognosis prediction for the target patient using virtual information obtained by changing part of the time-series information for the target patient and the prognosis prediction model MO, predict the effect of an intervention corresponding to the above changes based on the prediction result of the actual prognosis and the prediction result of the virtual prognosis, and output the prediction result of the effect of the intervention. In this way, it is possible to determine whether or not to actually perform the intervention based on the output prediction result of the effect of the intervention.

[0060] A-5. Working Example: An example of the prognosis prediction model MO described above is described below. The prognosis prediction model MO in this example was created through a multicenter, retrospective study of patients with newly diagnosed interstitial pneumonia at two hospitals (Tosei Public Hospital and Hamamatsu University School of Medicine) between 2008 and 2015. Of the 839 cases from Tosei Public Hospital, 80% were used as training data for model construction, and the remaining 20% ​​were used as validation data for internal validity verification. In addition, 336 cases from Hamamatsu University School of Medicine were used as test data for external validity (generalization performance) verification.

[0061] FIG. 10 is an explanatory diagram showing the prediction accuracy of the prognosis prediction model MO of the embodiment. FIG. 10 shows the results of internal validity verification using validation data (C-index value) and external validity verification using test data (same) for the prognosis prediction model MO created using the Dynamic-DeepHit model described above. The example in FIG. 10 shows the accuracy of the prognosis prediction results T months later (T = 6, 12, 24, 36) when the prediction time point is 12 months after the initial diagnosis. As shown in FIG. 10, a high C-index value of 0.85 or higher was obtained for both acute exacerbation and death, indicating that the prognosis prediction model MO of the embodiment generally achieved high prediction accuracy. The C-index is an index of prediction accuracy, with a higher value (maximum value: 1) indicating better model performance.

[0062] 11 and 12 are other explanatory diagrams showing the prediction accuracy of the prognosis prediction model MO of the example. FIGS. 11 and 12 show the results of internal validity verification (C-index values) and external validity verification (same results) when monthly concentration or change was used as the environmental factor. FIG. 11 shows an example using data from 12 months after initial diagnosis, and FIG. 12 shows an example using data from 24 months after initial diagnosis. As shown in FIGS. 11 and 12, the example using change as the environmental factor achieved prediction accuracy equal to or higher than that of the example using monthly concentration as the environmental factor.

[0063] FIG. 13 is an explanatory diagram showing the prediction accuracy of the prognosis prediction model MO of the embodiment. FIG. 13 shows the results (C-index value) of external validation of the prediction of acute exacerbation by a model (acute exacerbation model) that predicts only acute exacerbation, created using the Dynamic-DeepHit model; the results (same) of external validation of the prediction of death by a model (death model) that predicts only death; and the results (same) of external validation of the prediction of acute exacerbation and death by a model (competing model) that predicts both acute exacerbation and death, which are in a competing risk relationship. As shown in FIG. 13, the prediction accuracy of the model that takes competing risks into account was as high as the prediction accuracy of the model that takes only acute exacerbation or only death into account. Therefore, it can be said that high prediction accuracy can also be achieved by a model that takes competing risks into account.

[0064] FIG. 14 is an explanatory diagram showing an example of a prognosis prediction result by the prognosis prediction model MO of the embodiment. FIG. 14 shows the results of predicting the cumulative occurrence probability of each event using test data from the initial diagnosis to the 24th month using the prognosis prediction model MO created using the Dynamic-DeepHit model. Column A of FIG. 14 shows an example using test data in which an acute exacerbation occurred 38 months after the initial diagnosis. Column B of FIG. 14 shows an example using test data in which death occurred 54 months after the initial diagnosis. Column C of FIG. 14 shows an example using test data in which survival was terminated 81 months after the initial diagnosis. In the prediction results shown in column B of FIG. 14, the probability of death is consistently higher than in the example shown in column A of FIG. 14. Furthermore, in the prediction results shown in column C of FIG. 14, the probability of both acute exacerbation and death is consistently lower than in the examples shown in columns A and B of FIG. 14. Thus, it can be said that the prognosis prediction model MO of the embodiment generally achieves high prediction accuracy.

[0065] Fig. 15 is an explanatory diagram showing the prediction accuracy of a prognosis prediction model MO of another embodiment. Fig. 15 shows the results of internal validity verification using verification data (Balanced-Accuracy and F1 score values) for the prognosis prediction model MO created using the LSTM shown in Fig. 8. In an example where the cross-entropy weighting described below is not adjusted (the example of "k=0" in Fig. 15), the values ​​of Balanced-Accuracy and F1 score are both around 0.6, which means that a reasonable degree of prediction accuracy is achieved.

[0066] As shown in FIG. 7, in the training data LD used to create the prognosis prediction model MO, the proportion of data whose correct answer label is "survival" is very high. In other words, the training data LD is imbalanced data. In order to correct the influence of such data imbalance, the cross-entropy weight W is calculated according to the following formula (1): j The prediction accuracy of the model was confirmed by changing various W j =(N / (M×N j )) k ···(1) however, ·W j : weight at label=j N: Total number of labels M: Number of classes N j : Number of labels = j ·k: Hyperparameter

[0067] However, as shown in Figure 15, the weights W of each label can be adjusted by adjusting the value of the hyperparameter k. jEven when the parameter was changed, the prediction accuracy did not improve. Possible reasons for this include the data imbalance problem mentioned above, as well as the definition of "predicted events," which means that cases with different predicted probabilities for "acute exacerbation" can be classified as the same "acute exacerbation," and the similarity between events, which means that acute exacerbation is a fatal condition. Given the above, it can be said that it is preferable to use a machine learning algorithm (e.g., Dynamic-DeepHit) that supports multiple events with the competing risk relationship mentioned above when creating a prognostic prediction model MO for predicting acute exacerbation and / or death in interstitial pneumonia.

[0068] FIG. 16 is an explanatory diagram showing an example of the importance of each factor of a disease in the example. FIG. 16 shows the importance of each factor (top 20) determined by examining the change in the risk of acute exacerbation when the value that each factor (feature amount) can take is changed, and assuming that the greater the change in risk, the higher the importance (degree of contribution). As shown in FIG. 16, several environmental factors (SPM, NO2, PM 2.5 ) are more important than factors related to pulmonary function (FVC, DLco) and factors related to severity (GAP score). Therefore, it is preferable to use environmental factors to predict the prognosis of interstitial pneumonia.

[0069] FIG. 17 is an explanatory diagram showing an example of the importance of each factor of a disease in another embodiment. In FIG. 17, the importance of each factor is shown similarly to FIG. 16, but in the example of FIG. 17, in addition to the monthly average value, the amount of change from the previous month (the environmental factor name in FIG. 17 has "diff" added to the end) is used as the environmental factor. As shown in FIG. 17, several environmental factors (PM 2.5, NO, Ox) the importance of the amount of change from the previous month is high. Therefore, it can be said that it is preferable to use the amount of change for each fixed period in addition to or instead of the representative value for each fixed period as an environmental factor used in predicting the prognosis of interstitial pneumonia. Note that from the results shown in Figure 17, it can be said that for each environmental factor, whether the representative value for each fixed period or the amount of change is important differs. For example, the importance of the representative value for each fixed period is high for SPM, the amount of change is high for NO, and the importance of the amount of change is low for PM 2.5 and Ox are both highly important.

[0070] 18 and 19 are explanatory diagrams showing the relationship between the upper limit of the straight-line distance from the patient's current address to the measurement station (upper limit distance R) and prediction accuracy. As described above, in this embodiment, if there is no measurement station within the predetermined upper limit distance R from the patient's current address, data is considered missing for the environmental factors that reference the measurement data of the measurement station. FIG. 18 shows the change in the number of data points (number of data points when there is no missing data: 68,261 (person-months)) for each environmental factor as the upper limit distance R changes. As shown in FIG. 18, the larger the value of the upper limit distance R, the fewer the number of missing data points for each environmental factor.

[0071] FIG. 19 shows the change in prediction accuracy of acute exacerbation and death with changes in upper limit distance R. Regardless of whether training data, validation data, or test data is used, the prediction accuracy is highest when the upper limit distance R is 50 km or 100 km. If the upper limit distance R is too large, the amount of missing data will be reduced, but the deviation from the actual environment to which the patient was exposed will increase, and the prediction accuracy will decrease. From the results shown in FIG. 19, it can be said that it is preferable for the upper limit distance R to be 20 km or more and 100 km or less.

[0072] A-6. Advantages of this embodiment: As described above, the information processing device 100 of this embodiment is a device for predicting the prognosis of a target patient suffering from a disease, and includes a model acquisition unit 113, a target patient information acquisition unit 114, and a prognosis prediction execution unit 119. The model acquisition unit 113 receives time-series information indicating chronological changes in disease factors and acquires a prognosis prediction model MO, which is a machine learning model that outputs the prognosis of the disease. The target patient information acquisition unit 114 acquires time-series information about the target patient. The prognosis prediction execution unit 119 uses the time-series information about the target patient and the prognosis prediction model MO to predict the prognosis of the target patient and outputs the prognosis prediction result.

[0073] In this way, the information processing device 100 of this embodiment can predict the prognosis of a disease for each individual patient based on time-series information that indicates the chronological changes in disease factors. Therefore, the information processing device 100 of this embodiment can predict the prognosis of a disease with high accuracy.

[0074] In this embodiment, the disease factors include environmental factors, and therefore, according to the information processing device 100 of this embodiment, the prognosis of a disease can be predicted with higher accuracy by using information indicating the time-series changes in environmental factors that can have a significant impact on the prognosis of the disease.

[0075] In this embodiment, the disease factors include environmental factors of the patient's residence. Therefore, the information processing device 100 of this embodiment can predict the prognosis of the disease with higher accuracy by using information indicating time-series changes in factors related to the environment to which the patient is primarily exposed, compared to using environmental factors of the location of the hospital to which the patient visits, for example.

[0076] In this embodiment, the environmental factors include at least one of the presence of environmental pollutants and meteorological parameters. Therefore, the information processing device 100 of this embodiment can predict the prognosis of a disease with higher accuracy by using information indicating the time-series changes in environmental factors that can have a significant impact on the prognosis of the disease.

[0077] In this embodiment, the time-series information includes information indicating the amount of change in environmental factors. Therefore, according to the information processing device 100 of this embodiment, by using the information indicating the amount of change in environmental factors that may have a significant impact on the prognosis of the disease, it is possible to predict the prognosis of the disease with even higher accuracy.

[0078] In this embodiment, the time-series information includes information specifying the values ​​of disease factors at regular time intervals, and therefore, the information processing device 100 of this embodiment can predict the prognosis of the disease with higher accuracy than when using information specifying the values ​​of disease factors at irregular intervals.

[0079] In this embodiment, the time-series information includes information specifying at least the values ​​of disease factors for each month. Therefore, according to the information processing device 100 of this embodiment, by using the time-series information showing the monthly changes in disease factors, it is possible to predict the prognosis of the disease with higher accuracy.

[0080] In this embodiment, the prognosis of a disease includes the occurrence of multiple events that are in a competing risk relationship, and the prognosis prediction model MO is a model trained using a machine learning algorithm that corresponds to multiple events that are in a competing risk relationship. Therefore, the information processing device 100 of this embodiment can predict the occurrence of multiple events that are in a competing risk relationship with high accuracy.

[0081] In this embodiment, the prognosis prediction model MO is a model that outputs, as a prognosis of a disease, an index value that represents the probability of occurrence of multiple events that are in a competing risk relationship. Therefore, according to the information processing device 100 of this embodiment, it is possible to predict the occurrence of multiple events that are in a competing risk relationship with high accuracy.

[0082] In this embodiment, the multiple events that are competing risks include acute exacerbation and death. Therefore, the information processing device 100 of this embodiment can accurately predict the occurrence of each of the competing risks, acute exacerbation and death.

[0083] In this embodiment, the disease is a disease of the respiratory system or the circulatory system. Therefore, according to the information processing device 100 of this embodiment, it is possible to predict the prognosis of the disease of the respiratory system or the circulatory system with high accuracy by using time-series information indicating the time-series transition of factors of the disease of the respiratory system or the circulatory system.

[0084] In addition, in this embodiment, the prognosis prediction execution unit 119 may execute a virtual prognosis prediction for the target patient using virtual information obtained by changing part of the time-series information for the target patient and the prognosis prediction model MO, predict the effect of an intervention corresponding to the above changes based on the prediction result of the actual prognosis and the prediction result of the virtual prognosis, and output the prediction result of the effect of the intervention. With this configuration, it is possible to determine whether or not to actually perform the intervention based on the output prediction result of the effect of the intervention.

[0085] B. Variations: The technology disclosed in this specification is not limited to the above-described embodiments, and can be modified into various forms without departing from the spirit thereof, for example, the following modifications are also possible.

[0086] The configuration of the information processing device 100 in the above embodiment is merely an example and can be modified in various ways. Furthermore, the contents of the prognosis prediction model acquisition process and the prognosis prediction process in the above embodiment are merely an example and can be modified in various ways. For example, in the above embodiment, the information processing device 100 acquires the prognosis prediction model MO by creating the prognosis prediction model MO, but the information processing device 100 may also acquire a prognosis prediction model MO generated by another device.

[0087] The disease factors (features) and machine learning algorithms used to create the prognosis prediction model MO in the above embodiments are merely examples and can be modified in various ways. For example, features other than those exemplified in the above embodiments may be used as features to create the prognosis prediction model MO, or some of the features exemplified in the above embodiments may not be used. Furthermore, algorithms other than Dynamic-DeepHit and LSTM may be used as machine learning algorithms to create the prognosis prediction model MO.

[0088] In the above embodiment, information processing for predicting the prognosis of a patient suffering from interstitial pneumonia is exemplified, but the technology disclosed in this specification is not limited to interstitial pneumonia and can be similarly applied to predicting the prognosis of a patient suffering from any disease. Note that, since the prognosis of a respiratory or circulatory system disease is thought to be significantly influenced by environmental factors, it is preferable that the disease factors used for predicting the prognosis of a respiratory or circulatory system disease include environmental factors.

[0089] In the above-described embodiment, a part of the configuration realized by hardware may be replaced by software, and conversely, a part of the configuration realized by software may be replaced by hardware. [Explanation of symbols]

[0090] 100: Information processing device 110: Control unit 111: Raw information acquisition unit 112: Learning data acquisition unit 113: Model acquisition unit 114: Target patient information acquisition unit 119: Prognosis prediction execution unit 120: Memory unit 130: Display unit 140: Operation input unit 150: Interface unit 190: Bus CP: Prognosis prediction program LD: Learning data MO: Prognosis prediction model RD: Prognosis prediction result data

Claims

1. An information processing device for predicting the prognosis of a target patient suffering from a disease, a model acquisition unit that acquires a prognosis prediction model, which is a machine learning model that receives time-series information indicating time-series changes in the factors of the disease as input and outputs the prognosis of the disease; a target patient information acquisition unit that acquires the time-series information about the target patient; a prognosis prediction execution unit that uses the time-series information about the target patient and the prognosis prediction model to predict the prognosis of the target patient and outputs the prognosis prediction result; Equipped with The information processing device, wherein the disease factors include environmental factors.

2. (delete)

3. 2. The information processing device according to claim 1, An information processing device, wherein the disease factors include environmental factors of the target patient's place of residence.

4. 4. The information processing device according to claim 3, An information processing device, wherein the environmental factors of the target patient's place of residence are environmental factors of a location within a straight-line distance of 200 km from the target patient's current address.

5. 4. The information processing device according to claim 1, The information processing device, wherein the environmental factors include at least one of a presence status of environmental pollutants and meteorological parameters.

6. 4. The information processing device according to claim 1, An information processing device, wherein the time-series information includes information indicating an amount of change in the environmental factor.

7. 2. The information processing device according to claim 1, An information processing device, wherein the time-series information is information that identifies values ​​of factors of the disease at regular time intervals.

8. 8. The information processing device according to claim 7, An information processing device, wherein the time-series information includes information specifying at least monthly values ​​of the disease factors.

9. 2. The information processing device according to claim 1, The prognosis of the disease includes the occurrence of multiple events in a competing risk relationship; An information processing device, wherein the prognosis prediction model is a model trained using a machine learning algorithm that corresponds to multiple events that are in a competing risk relationship.

10. 10. The information processing device according to claim 9, The information processing device, wherein the prognosis prediction model is a model that outputs, as the prognosis of the disease, an index value that indicates the possibility of an event occurring for a plurality of events that are in a competing risk relationship.

11. 11. The information processing device according to claim 9, The information processing device, wherein the plurality of events in a competing risk relationship include acute exacerbation and death.

12. 2. The information processing device according to claim 1, The information processing device, wherein the disease is a disease of the respiratory system or the circulatory system.

13. 2. The information processing device according to claim 1, The prognosis prediction execution unit executes a virtual prognosis prediction for the target patient using virtual information obtained by changing part of the time-series information for the target patient and the prognosis prediction model, predicts the effect of an intervention corresponding to the change based on the prediction result of the actual prognosis and the prediction result of the virtual prognosis, and outputs the prediction result of the effect of the intervention.

14. An information processing method for predicting the prognosis of a target patient suffering from a disease, comprising: a step of acquiring a prognosis prediction model, which is a machine learning model that receives time-series information indicating the time-series changes in the factors of the disease as input and outputs the prognosis of the disease; acquiring the time-series information about the subject patient; a step of predicting the prognosis of the subject patient using the time-series information about the subject patient and the prognosis prediction model, and outputting the prognosis prediction result; Equipped with The disease factors include environmental factors.

15. A computer program for predicting the prognosis of a subject patient suffering from a disease, comprising: On the computer, A process of acquiring a prognosis prediction model, which is a machine learning model that receives time-series information indicating the time-series changes in the factors of the disease as input and outputs the prognosis of the disease; A process of acquiring the time-series information about the target patient; a process of predicting the prognosis of the target patient using the time-series information about the target patient and the prognosis prediction model, and outputting the prognosis prediction result; Execute The disease factors include environmental factors.

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