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

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: A computer system is accessible to a database storing learning data to generate a prediction model, the learning data includes input data and teacher data, the computer system: performs first learning to set an extraction criterion for extracting the learning data including the input data similar to prediction target data in a case of being input the prediction target data; extract the learning data from the first database based on the extraction criterion and generate a dataset; perform second learning to generate a prediction model using the dataset; generate a decision logic showing a prediction logic of the prediction model; and output information to present the decision logic.

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: A computer system is accessible to a database storing learning data to generate a prediction model, the learning data includes input data and teacher data, the computer system: performs first learning to set an extraction criterion for extracting the learning data including the input data similar to prediction target data in a case of being input the prediction target data; extract the learning data from the first database based on the extraction criterion and generate a dataset; perform second learning to generate a prediction model using the dataset; generate a decision logic showing a prediction logic of the prediction model; and output information to present the decision logic.

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: The present invention correlates information, to be processed, about an organ and/or a disease, etc., obtained from a medical image and anatomical/functional medical knowledge information, and enables the information obtained from the medial image to be effectively utilized in medical examination and treatment processes. In a medical image information system (101), an image processing unit (103) processes an image, a graph model creation unit (104) creates a graph data model from the information obtained from the image, a graph data model processing unit (106) acquires a graph data model based on anatomical/functional medical knowledge, compares with each other and integrates the graph data models and stores an integrated graph data model, and a display processing unit (110) displays the integrated graph data model, whereby the effective use of information obtained from the image is made possible.

Abstract: A unit (33) for generating count images for separate energy windows generates main measured count images and auxiliary measured count images on the basis of gamma ray (6) count information measured by a detector head (10). A main measurement window direct ray count rate estimation unit (42) estimates a count rate for direct gamma rays in a main measurement energy window, doing so by subtracting a scattered gamma ray count rate for an auxiliary measurement energy window, which has been estimated from an auxiliary measured count image and detector response data by an auxiliary measurement window scattered ray count rate estimation unit (41), from the main measured count image.

Abstract: When two detector panels are rotationally moved around the entire circumference of a region of interest and projection images of the region of interest are captured during the rotational movement, the respective detector panels are moved along the tangential direction of the rotational movement to a position where the union of the capturing ranges of the projection images captured by the respective detector panels covers the entire region of interest. The projection images captured by the respective detector panels are used to reconstruct a transaxial image of the region of interest.

Abstract: The present invention correlates information, to be processed, about an organ and/or a disease, etc., obtained from a medical image and anatomical/functional medical knowledge information, and enables the information obtained from the medial image to be effectively utilized in medical examination and treatment processes. In a medical image information system (101), an image processing unit (103) processes an image, a graph model creation unit (104) creates a graph data model from the information obtained from the image, a graph data model processing unit (106) acquires a graph data model based on anatomical/functional medical knowledge, compares with each other and integrates the graph data models and stores an integrated graph data model, and a display processing unit (110) displays the integrated graph data model, whereby the effective use of information obtained from the image is made possible.

Abstract: When two detector panels are rotationally moved around the entire circumference of a region of interest and projection images of the region of interest are captured during the rotational movement, the respective detector panels are moved along the tangential direction of the rotational movement to a position where the union of the capturing ranges of the projection images captured by the respective detector panels covers the entire region of interest. The projection images captured by the respective detector panels are used to reconstruct a transaxial image of the region of interest.

Abstract: A reaction container in which to mix a first chemical compound with a second chemical compound has a main body and a lid member formed oppositely on a top face side of the main body; a flow channel on the top face of the main body; and a labeling agent solidification section at an intermediate section of the flow channel to remove a solvent in a solution of the second chemical compound and solidify the second chemical compound. First and second chemical compound supply sections and a mixture discharge section are formed on the upstream and downstream sides of the labeling agent solidification section, respectively. The reactor is provided with a liquid sending unit to supply the first and second chemical compounds and reciprocally send a solution of the first chemical compound to an upper part of the second chemical compound solidified at the solidification section.

Abstract: There is provided is a radiation image acquiring device which corrects a positional displacement between a collimator and a detector and obtains an image without artifacts. The device includes a detector (21) to measure a radiation; a collimator (26) including a through-hole (27) having one or more detectors (21) disposed therein and configured to limit an incident direction of the radiation; a positional displacement measuring unit configured to measure a positional displacement between the detector (21) and the collimator (26) by use of a profile of a radiation source measured by the detector (21) based on the radiation source disposed corresponding to a predetermined detector (21).

Abstract: A unit (33) for generating count images for separate energy windows generates main measured count images and auxiliary measured count images on the basis of gamma ray (6) count information measured by a detector head (10). A main measurement window direct ray count rate estimation unit (42) estimates a count rate for direct gamma rays in a main measurement energy window, doing so by subtracting a scattered gamma ray count rate for an auxiliary measurement energy window, which has been estimated from an auxiliary measured count image and detector response data by an auxiliary measurement window scattered ray count rate estimation unit (41), from the main measured count image.