Abstract: In order to attain an object of generating a prediction model which not only is capable of reducing a calculation load in a prediction phase but also has a good interpretability, a prediction model generation apparatus includes: a contribution degree calculation section that calculates, with use of a test data set different from a training data set used in training of a prediction model to be tested, a degree of contribution of each of a plurality of features to a prediction result, a value of the each of the plurality of features being inputted to the prediction model to be tested; a feature selection section that selects, on the basis of the degree of contribution of the each of the plurality of features, at least one feature from among the plurality of features; and a prediction model generation section that generates a new prediction model which, upon receiving input of a value of the at least one feature selected, outputs a prediction result.

Abstract: A medical facility matching system (1) comprises: a matching processing unit (121) performing matching processing to select as a matching result, a recommended medical facility for a patient based on patient item information (D12) corresponding to patient attribute items (Di12) and facility item information (D22) corresponding to facility attribute items (Di22); a data learning unit (122) learning about the patient attribute items for the matching processing so that the matching result is likely to be approved by the patient, and learning about the facility attribute items for the matching processing so that the matching result is likely to be approved by the medical facility selected; and a data item choosing unit (123) choosing based on a learning result, the patient attribute items and facility attribute items for the matching processing, wherein the matching processing is performed based on the chosen patient attribute items and the chosen facility attribute items.

Abstract: In order to achieve an object to determine, with high accuracy, that a target is in a specific state, a state determination apparatus includes: a calculation section that calculates, on the basis of data obtained from the target, a score indicative of a degree to which the target is in the specific state; a decision section that decides a threshold on the basis of the data; and a determination section that determines, by comparing the score and the threshold, whether the target is in the specific state.

Abstract: In order to achieve an object to determine, with high accuracy, that a target is in a specific state, a state determination apparatus includes: a calculation section that calculates, on the basis of data obtained from the target, a score indicative of a degree to which the target is in the specific state; a decision section that decides a threshold on the basis of the data; and a determination section that determines, by comparing the score and the threshold, whether the target is in the specific state.

Abstract: In order to achieve an object to determine, with high accuracy, that a target is in a specific state, a state determination apparatus includes: a calculation section that calculates, on the basis of data obtained from the target, a score indicative of a degree to which the target is in the specific state; a decision section that decides a threshold on the basis of the data; and a determination section that determines, by comparing the score and the threshold, whether the target is in the specific state.

Abstract: In information processing device, the information acquisition means acquires patient information, medical professional information, and environment information. The treatment order determination means determines an order of treatment of the patients for each medical professional based on the patient information, the medical professional information, and the environment information.

Abstract: In order to achieve an object to determine, with high accuracy, that a target is in a specific state, a state determination apparatus includes: a calculation section that calculates, on the basis of data obtained from the target, a score indicative of a degree to which the target is in the specific state; a decision section that decides a threshold on the basis of the data; and a determination section that determines, by comparing the score and the threshold, whether the target is in the specific state.

Abstract: A motion estimation system 80 includes a pose acquisition unit 81 and an action estimation unit 82. The pose acquisition unit 81 acquires, in time series, pose information representing a posture of one person and a posture of another person identified simultaneously in a situation in which a motion of the one person affects a motion of the other person. The action estimation unit 82 divides the acquired time series pose information on each person by unsupervised learning to estimate an action series that is a series of motions including two or more pieces of pose information.

Abstract: A medical checkup presentation apparatus (1) includes: a selection unit (121) for selecting, as a recommended medical checkup, a medical checkup that is recommended to a medical checkup target person based on medical chart data (1110) including an information (1112) related to a history of a medical examination for the medical checkup target person and past medical checkup data (1120) including an information related to a history of a medical checkup; and presentation unit (122) for presenting, to a presentation target person, a recommended medical checkup information including an information related to an item name of the recommended medical checkup.

Abstract: A facility presentation apparatus (1) includes: a selection unit (121) for selecting, as a recommended facility, at least one medical checkup facility recommended to a medical checkup target person from a plurality of medical checkup facilities based on a medical checkup information indicating a medical checkup that is related to a healthcare and that is intended to be taken by the medical checkup target person, a medical checkup facility information (1110) related to the plurality of medical checkup facilities in each of which the medical checkup can be performed and medical chart data (1220) of the medical checkup target person; and a presentation unit (122) for presenting, to a presentation target person, a recommended facility information related to the recommended facility.

Abstract: A patient condition prediction apparatus includes: an acquisition unit that obtains patient data, which are information about a patient; a selection unit that selects one predictive model from a plurality of predictive models for predicting a change in a patient condition that is a condition of the patient, on the basis of the patient data; and a prediction unit that predicts a change in the patient condition in the future by using the one predictive model. This makes it possible to predict a change in the patient condition by using an appropriate predictive model.

Abstract: A data management system is provided with: a storage unit for storing examination information including at least an examination item, an examination location, a consultation location, and a required examination time, the examination item including examination names of a plurality of examinations; an acquisition unit for acquiring patient information relating to severity and an identifier for identifying the patient to be examined; a calculation unit for calculating, in accordance with the patient, the necessary travel time for traveling from the examination location or consultation location at which a completed examination or consultation was performed to the next examination location or consultation location at which the next examination or consultation subsequent to the examination or consultation is to be performed, based on the patient information and the examination information; and a generating unit for generating a patient examination schedule based on the examination information, the patient informati

Abstract: A risk prediction apparatus includes: an acquisition unit that obtains risk transition data indicating a transition of a risk of deterioration of symptoms from a target patient; an accumulation unit that accumulates the risk transition data of a past about a plurality of patients; a prediction unit that predicts a future change in the risk of the target patient on the basis of the risk transition data about the target patient obtained by the acquisition unit and the risk transition data of the past accumulated in the accumulation unit; and a determination unit that determines whether or not to take a measure for the target patient on the basis of the change in the risk predicted by the prediction unit. This makes it possible to appropriately determine whether or not to take a measure for the patient.

Abstract: A medical facility evaluation apparatus includes: an acquisition unit that obtains a quantitatively measured evaluation information about a medical facility that is a candidate of a transfer destination of a patient; and a calculation unit that calculates a score indicating goodness of fit of the medical facility as the transfer destination with respect to the patient, on the basis of the evaluation information. According to this medical facility evaluation apparatus, scoring based on the quantitatively measured evaluation information makes it possible to find a medical facility with high goodness of fit as a transfer destination of a patient.

Abstract: A patient assessment support device includes an output unit which outputs estimated support information estimated based on question information relating to nursing care for a patient when a record of known support information relating to the patient is determined as being absent.

Abstract: A disease risk prediction device 80 includes a prediction unit 81 and a prediction result output unit 82. The prediction unit 81 predicts a development risk of an infectious disease using a prediction model for predicting a development status of the infectious disease, the prediction model being learned based on electronic data of a patient. The prediction result output unit 82 outputs the predicted development risk.

Abstract: A motion estimation system 80 includes a pose acquisition unit 81 and an action estimation unit 82. The pose acquisition unit 81 acquires, in time series, pose information representing a posture of one person and a posture of another person identified simultaneously in a situation in which a motion of the one person affects a motion of the other person. The action estimation unit 82 divides the acquired time series pose information on each person by unsupervised learning to estimate an action series that is a series of motions including two or more pieces of pose information.

Abstract: An early infectious disease sign detection device includes: an early infectious disease sign detection unit that generates early sign information indicating an early sign that a determination target patient is going to develop an infectious disease, by using learning data indicating a result of learning about biological information of a patient who has developed the infectious disease among a plurality of patients, and biological information acquired for the determination target patient; and an action information output unit that outputs action information for the infectious disease for the determination target patient, based on the early sign information.

Abstract: A data management system is provided with: a storage unit for storing examination information including at least an examination item, an examination location, a consultation location, and a required examination time, the examination item including examination names of a plurality of examinations; an acquisition unit for acquiring patient information relating to severity and an identifier for identifying the patient to be examined; a calculation unit for calculating, in accordance with the patient, the necessary travel time for traveling from the examination location or consultation location at which a completed examination or consultation was performed to the next examination location or consultation location at which the next examination or consultation subsequent to the examination or consultation is to be performed, based on the patient information and the examination information; and a generating unit for generating a patient examination schedule based on the examination information, the patient informati

Abstract: This medical information processing system is provided with: an operation policy input unit which receives a selection of an operation policy defining a plurality of operation policies which can be taken by a hospital as a whole; a model learning unit which refers to a database being managed so as to be accessible in the hospital, and generates, for each selectable operation policy, a maximization model of each operation policy with respect to an environmental change of the hospital; and a behavior optimization unit which, using the maximization model of the operation policy selected by the operation policy input unit, generates decision assistance information for a healthcare worker with respect to each patient that maximizes the overall efficiency of the hospital in accordance with the environmental change of the hospital.