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 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: 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: Provided is an analysis system that presents an effective diagnosis and treatment act for each patient. The analysis system includes an evaluation index calculation unit that calculates an index value indicating medical care quality based on received medical care act data and clinical data, a cost calculation unit that calculates, by using medical cost data, costs of a medical care act that relate to a disease name to be analyzed and are generated after a period of a detected disease developing event, and an extraction unit that extracts, by using at least one of the calculated index value and the calculated costs, the received medical care act and clinical data of a patient to be analyzed and an extracted correlation, a medical care act to be recommended to the patient to be analyzed.

Abstract: The task is to provide a system which supports smooth patient referral from a referral source medical institution to a referral destination medical institution and which particularly prevents readmission due to cases that are difficult to determine simply based on patient attributes or due to the quality of medical services. To achieve this task, a referral difficult degree calculation unit calculates a referral difficulty degree which quantifies the risk that a patient cannot be discharged because a referral destination cannot be found, in consideration of a readmission risk calculated by a readmission risk calculation unit, the geographical distance between the patient and the referral destination medical institution, the total number of patients accepted, and the number of beds available. This referral difficulty degree is outputted to an output unit of the referral source medical institution or the referral destination medical institution.

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: High-quality health care information/medical data is provided. A system accumulating health care information comprises a receiving part that receives the health care information from a computer of a data provider; a memory part that stores therein criteria relating to health care and medical treatment with which the health care information is evaluated; n evaluation part that evaluates a value of the health care information based on the received health care information and the criteria; and a billing part that determines a fee for accumulating the health care information based on the value, the above-mentioned object is achieved.

Abstract: Provided is a region extraction system that uses an image data analysis process, which has overcome the problem of false positives in region extraction and assists in more accurate region extraction. A luminance distribution-analyzing unit (105) is used to determine a parameter of unknown quantity for extracting a region of interest. The luminance distribution-analyzing unit (105) searches a luminance distribution management database (106) for a region, which has a luminance distribution similar to the region of interest and is easily extracted, as a similar area. The region of the similar area is extracted using a region extraction pre-processing unit (107) and the value of the parameter of unknown quantity is determined on the basis of an image feature value calculated from the region extraction results.

Abstract: The task is to provide a system which supports smooth patient referral from a referral source medical institution to a referral destination medical institution and which particularly prevents readmission due to cases that are difficult to determine simply based on patient attributes or due to the quality of medical services. To achieve this task, a referral difficult degree calculation unit calculates a referral difficulty degree which quantifies the risk that a patient cannot be discharged because a referral destination cannot be found, in consideration of a readmission risk calculated by a readmission risk calculation unit, the geographical distance between the patient and the referral destination medical institution, the total number of patients accepted, and the number of beds available. This referral difficulty degree is outputted to an output unit of the referral source medical institution or the referral destination medical institution.

Abstract: Provided is a region extraction system that uses an image data analysis process, which has overcome the problem of false positives in region extraction and assists in more accurate region extraction. A luminance distribution-analyzing unit (105) is used to determine a parameter of unknown quantity for extracting a region of interest. The luminance distribution-analyzing unit (105) searches a luminance distribution management database (106) for a region, which has a luminance distribution similar to the region of interest and is easily extracted, as a similar area. The region of the similar area is extracted using a region extraction pre-processing unit (107) and the value of the parameter of unknown quantity is determined on the basis of an image feature value calculated from the region extraction results.

Abstract: The present invention periodically and exhaustively extracts analysis dimension candidates from text data, such as medical literatures disclosed on the Internet. A name of disease, a medical agent, a checkup, and an operation included in actual clinical data are linked to the analysis dimension candidates. In the analysis dimension candidates, clinically important candidates and non-important candidates including extraction errors are mixed. To distinguish the candidates, weighting is provided to the link. First, a weight is made large when a level of an evidence of a medical literature from which an analysis dimension candidate is extracted is high. In literature groups of each name of disease, the degree of cooccurrence between a word of an analysis dimension candidate, and a word related to a medical agent/checkup/operation is calculated, and the weight of the link is made larger according to the magnitude of the degree of cooccurrence.

Abstract: The present invention relates to a technology for extracting a target area from within an image by a combination of a plurality of image processes, enabling an automatic setting of an image process procedure without an operator inputting the image process procedure. Thus, according to the present invention, the content of an image process to be carried out in the next and subsequent rounds is automatically determined based on a history of results of image processes that have been applied to a process object image up to the immediately preceding round.

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.

Abstract: The present invention is a system for supporting an evaluation of workflow step items on the basis of a workflow by classifying workflow steps so as to have a common medical-related work by using a medical information database in which a workflow, which is a flow of the medical-related work, and information on respective workflow steps are stored in association with medical information, creating workflow step items having a parent and child identifier identified between a parent department as a client and a child department as a requested party and the medical information, calculating a workflow step evaluation value, which is an evaluation value of the workflow step item on the basis of the clinical indicator of the workflow, and displaying the same on a screen.

Abstract: Provided is a medical care support system for the sharing and reusing of medical information on the basis of a workflow which is a flow of medical-related services. A workflow step that was executed prior to an ongoing workflow step is selected by using a medical information database which has registered a workflow and information of each workflow step in association with medical information; and information is inputted by medical staff in the ongoing workflow step while the medical information registered in the selected workflow step being referred to; and the medical information being referred to and the inputted information are registered in the medical information database in association with the ongoing workflow step.