Abstract: In an information processing device, an information acquisition means acquires a first data subset created by extracting a partial data group included in a first data set, a first distance corresponding to a distance between the first data subset and the first data set, a second data subset created by extracting a partial data group included in a second data set, and a second distance corresponding to a distance between the second data subset and the second data set. An information generation means calculates a third distance corresponding to a distance between the first data subset and the second data subset, and to generate an estimation distance information which is information capable of estimating a fourth distance corresponding to a distance between the first data set and the second data set based on the first distance, the second distance, and the third distance.

Abstract: An information processing device 100 of the present disclosure includes a data acquisition unit 121 and a data selection unit 122. The data acquisition unit 121 acquires biological data measured from a person whose state related to agitation is to be determined, and a determination result of the state by a determiner with respect to the person. The data selection unit 122 selects learning data from the biological data on the basis of a skill value and the determination result. The skill value represents the ability of determining the state and is set for the determiner. Selection of the learning data is optimized by the data selection unit 122 of the present disclosure.

Abstract: Based on receipt data indicating a detailed statement of medical fees, receipt data conversion information indicating a presence or absence of a medical care information item that corresponds to a plurality of examination failure conditions is generated. A failure risk level based on the medical care information item included in the receipt data is calculated by using the receipt data conversion information and an examination failure risk predictor. A risk factor indicating a medical care information item with a degree of contribution to the failure risk level calculation that is greater than or equal to a prescribed threshold value for degree of contribution is specified. A revision screen for the medical care information item that is a risk factor when the failure risk level is greater than or equal to a prescribed threshold value for risk level is output.

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: 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: Based on receipt data indicating a detailed statement of medical fees, receipt data conversion information indicating a presence or absence of a medical care information item that corresponds to a plurality of examination failure conditions is generated. A failure risk level based on the medical care information item included in the receipt data is calculated by using the receipt data conversion information and an examination failure risk predictor. A risk factor indicating a medical care information item with a degree of contribution to the failure risk level calculation that is greater than or equal to a prescribed threshold value for degree of contribution is specified. A revision screen for the medical care information item that is a risk factor when the failure risk level is greater than or equal to a prescribed threshold value for risk level is output.

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: Based on receipt data indicating a detailed statement of medical fees, receipt data conversion information indicating a presence or absence of a medical care information item that corresponds to a plurality of examination failure conditions is generated. A failure risk level based on the medical care information item included in the receipt data is calculated by using the receipt data conversion information and an examination failure risk predictor. A risk factor indicating a medical care information item with a degree of contribution to the failure risk level calculation that is greater than or equal to a prescribed threshold value for degree of contribution is specified. A revision screen for the medical care information item that is a risk factor when the failure risk level is greater than or equal to a prescribed threshold value for risk level is output.

Abstract: Based on receipt data indicating a detailed statement of medical fees, receipt data conversion information indicating a presence or absence of a medical care information item that corresponds to a plurality of examination failure conditions is generated. A failure risk level based on the medical care information item included in the receipt data is calculated by using the receipt data conversion information and an examination failure risk predictor. A risk factor indicating a medical care information item with a degree of contribution to the failure risk level calculation that is greater than or equal to a prescribed threshold value for degree of contribution is specified. A revision screen for the medical care information item that is a risk factor when the failure risk level is greater than or equal to a prescribed threshold value for risk level is output.

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 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 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 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 information processing apparatus (2000) acquires state information representing states of persons, for a first person and a second person associated with the first person. The information processing apparatus (2000) determines whether a predetermined condition regarding states of the first person and the second person is satisfied, by using state information of the first person and state information of the second person. The information processing apparatus (2000) sends a notification of recommending at least the first person to take a break when the predetermined condition is satisfied.

Abstract: A measuring device includes: a holding device that holds a container containing a measured object detachably; a measuring unit that measures a weight of the container or a volume of the measured object; a calculation unit configured to calculate an amount of use of the measured object based on the weight or the volume measured by the measuring unit and a last measurement result by the measuring unit; and a communication device that transmits the amount of use of the measured object to an external terminal.