Abstract: An object of the present invention is to achieve generation of a prediction model appropriate for each site without a necessity of transfer of data located at a plurality of sites to the outside of the sites. An analysis device capable of communicating with a plurality of learning devices includes a reception unit (301, 401, 1501) that receives transformed features obtained by transforming, in accordance with a predetermined rule, features contained in pieces of learning data individually retained in the plurality of learning devices, a distribution analysis unit (302) that analyzes distributions of a plurality of the features of the plurality of learning devices on the basis of the transformed features received by the reception unit (301, 401, 1501) for each of the learning devices, and an output unit (304, 1504) that outputs a distribution analysis result analyzed by the distribution analysis unit (302).

Abstract: An object of the invention is to improve prediction accuracy of data including a low-frequency medical treatment practice.

Abstract: Example implementations described herein are directed to the problem of dynamically extracting valid sets of clinical treatments and predictive features from electronic health records to construct models that can predict the effect of treatments and hence compare the effect of multiple, putative treatments. By utilizing a data pipelining technique for constructing machine learning models that only utilizes sets of valid treatment options instead of all possible options, the hardware and computational resources required for constructing the machine learning models can thereby be reduced, and the predicted treatment transition outcomes can be traced to valid treatments, thereby allowing the clinician to understand the effects from a clinical perspective.

Abstract: An analysis apparatus comprises: a generation module configured to generate a second piece of input data having a weight for a first feature item of a patient based on: a first piece of input data relating to the first feature item; a second feature item relating to a transition to a prediction target in a clinical pathway relating to a process for diagnosis or treatment; and a clinical terminology indicating relevance between medical terms; a neural network configured to output, when being supplied with the first piece of input data and the second piece of input data generated, a prediction result for the prediction target in the clinical pathway and importance of the first feature item; an edit module configured to edit the clinical pathway based on the prediction result and the importance output from the neural network; and an output module configured to output an edit result.

Abstract: To implement a highly accurate prediction analysis that does not depend on data amount. A data analysis apparatus has a processor that is configured to execute: an acquisition processing of acquiring a first statistical model based on a distribution of actual measurement results of a group and a second statistical model based on a distribution of a first actual measurement result of first samples having a smaller number of samples than the number of samples of the group; a calculation processing of calculating correction information indicating a difference between the first statistical model and the second statistical model; a learning processing of generating a first prediction model by performing machine learning using the first actual measurement result and first feature amount data corresponding to the first actual measurement result; and a correction processing of correcting a first prediction result , and outputting a second prediction result.

Abstract: To provide an appropriate social security service considering a plurality of goals.

Abstract: Provided is an integration device that is accessible to statistical information based on analysis target data of each of a plurality of analysis target devices and includes a processor configured to execute a program and a storage device configured to store the program. The integration device executes acquisition processing of acquiring first statistical information and second statistical information from a plurality of pieces of statistical information, integration processing of integrating the first statistical information and the second statistical information acquired by the acquisition processing by statistical processing based on the number of first data of first analysis target data used for statistical processing of the first statistical information and the number of second data of second analysis target data used for statistical processing of the second statistical information, and output processing of outputting integration statistical information obtained by the integration processing.

Abstract: It is provided a treatment selection support system comprising: a target achievement determination module configured to create target achievement determination information; a blood sugar controllability estimation module configured to create blood sugar controllability information; an achievement level prediction model creation module configured to create an achievement level prediction model; an appropriateness level calculation model creation module configured to create an appropriateness level calculation model for calculating an appropriateness level of a blood sugar control means based on formatted information, the target achievement determination information, and the blood sugar controllability information; an achievement level prediction module configured to use the achievement level prediction model; an appropriateness level calculation module configured to use the appropriateness level calculation model; and a blood sugar control means suggestion module configured to provide information on the blood

Abstract: An analyzer calculates a first feature amount data group from an intermediate layer by inputting each training data of a training data group into a learning model which includes an input layer, one or more intermediate layers, and an output layer, and is learned based on the training data group assigned to the input layer and a correct answer data group assigned to the output layer. A second feature amount data is calculated from the intermediate layer by inputting prediction target data of the learning model. A search processing of searching specific first feature amount data similar to the second feature amount data is calculated by the second calculation processing, from the first feature amount data group, and an extraction processing of extracting, from the training data group, specific training data, which is a calculation source of the specific first feature amount data searched by the search processing.

Abstract: Example implementations described herein are directed to the problem of dynamically extracting valid sets of clinical treatments and predictive features from electronic health records to construct models that can predict the effect of treatments and hence compare the effect of multiple, putative treatments. By utilizing a data pipelining technique for constructing machine learning models that only utilizes sets of valid treatment options instead of all possible options, the hardware and computational resources required for constructing the machine learning models can thereby be reduced, and the predicted treatment transition outcomes can be traced to valid treatments, thereby allowing the clinician to understand the effects from a clinical perspective.

Abstract: An analysis apparatus comprises: a generation module configured to generate a second piece of input data having a weight for a first feature item of a patient based on: a first piece of input data relating to the first feature item; a second feature item relating to a transition to a prediction target in a clinical pathway relating to a process for diagnosis or treatment; and a clinical terminology indicating relevance between medical terms; a neural network configured to output, when being supplied with the first piece of input data and the second piece of input data generated, a prediction result for the prediction target in the clinical pathway and importance of the first feature item; an edit module configured to edit the clinical pathway based on the prediction result and the importance output from the neural network; and an output module configured to output an edit result.

Abstract: It is provided a treatment selection support system comprising: a target achievement determination module configured to create target achievement determination information; a blood sugar controllability estimation module configured to create blood sugar controllability information; an achievement level prediction model creation module configured to create an achievement level prediction model; an appropriateness level calculation model creation module configured to create an appropriateness level calculation model for calculating an appropriateness level of a blood sugar control means based on formatted information, the target achievement determination information, and the blood sugar controllability information; an achievement level prediction module configured to use the achievement level prediction model; an appropriateness level calculation module configured to use the appropriateness level calculation model; and a blood sugar control means suggestion module configured to provide information on the blood

Abstract: An automatic analytical apparatus includes a reaction container for mixing a sample with a reagent to react the sample to the reagent, a measurement unit that irradiates a reaction solution in the reaction container with light and measures the intensity of transmitted light or scattered light, a control unit that processes time-series light intensity data obtained through the measurement in the measurement unit, a storage unit that stores one or more approximation functions each approximating to a time-series change in the light intensity data, and an output unit that outputs a processing result of the control unit.

Abstract: Provided is technology for blood clotting reactions capable of analyzing a blood clotting reaction with a high degree of precision, by precisely detecting and removing noise, regardless of the location where the noise is generated in the light intensity data. This automated analyzer approximates, with an approximation curve, time series data for transmitted light intensity or scattered light intensity of light emitted onto a sample, and, in this process, removes abnormal data points that deviate from the approximation curve (see FIG. 2).

Abstract: Provided is technology for blood clotting reactions capable of analyzing a blood clotting reaction with a high degree of precision, by precisely detecting and removing noise, regardless of the location where the noise is generated in the light intensity data. This automated analyzer approximates, with an approximation curve, time series data for transmitted light intensity or scattered light intensity of light emitted onto a sample, and, in this process, removes abnormal data points that deviate from the approximation curve (see FIG. 2).

Abstract: An image processing apparatus includes a process flow building unit, a process flow execution unit, a display unit, a selection unit, a process flow evaluation unit, and a storage unit including a process flow database. In addition, an image processing apparatus includes an image processing parameter adjusting unit, a process flow execution unit, a display unit, a selection unit, a parameter evaluation unit, and a storage unit including a parameter database.