Abstract: A generation device that generates a feature amount range visualization element that defines a continuous feature amount range for each of the factors based on time series data including feature amounts of different factors existing in time series and generates an inter-feature amount visualization element that defines relevance between a first feature amount of a first factor and a second feature amount of a second factor that are continuous in time. The device also generates visualization information indicating a relationship of the feature amounts related to the plurality of different factors by associating, by the inter-feature amount visualization element, a first feature amount range visualization element of the first factor with a second feature amount range visualization element of the second factor.

Abstract: Signal processing assists in searching for a mechanism through a generation equation of a signal for stratifying a patient, which refers to classification of patients suffering from a disease to enable medical treatment.

Abstract: Aspects relate to providing a counterfactual inference management technique capable of providing increased flexibility to allow users to select an appropriate counterfactual inference and offering scalability for handling tabular data and image data in a single configuration. A counterfactual inference management device comprising a classifier unit trained to determine whether a set of input data that includes a set of data features achieves a predetermined target and a counterfactual inference unit for generating a set of transformed data in which a subset of the set of data features are modified to counterfactual features. The classifier unit processes the set of transformed data to determine whether it achieves the predetermined target and calculates a counterfactual loss. The counterfactual inference unit is trained to reduce the counterfactual loss and generate a set of transformed data including counterfactual features that achieve the predetermined target.

Abstract: A data processing apparatus stores an analysis target data group having values of respective variables in a variable group and a value of an objective variable per analysis target, and an element group that the variable group and each of one or more modulation method(s) for modulating the variable(s) are set as elements. When the element selected from the element group is acquired, a modulation function of modulating the value of the variable which is contained in the acquired element is planned on the basis of the history of the acquired element; and the value of the variable per the analysis target is modulated on the basis of the modulation function. Image data are generated which gives a point of coordinates which are values of the modulation result and the objective variable defined by a first axis and a second axis, respectively, per the analysis target.

Abstract: A data analysis apparatus executes: a selection process selecting a first feature variable group that is a trivial feature variable group contributing to prediction and a second feature data group other than the first feature variable group from a set of feature variables; an operation process operating a first regularization coefficient related to a first weight parameter group corresponding to the first feature variable group in a manner that the loss function is larger, and operating a second regularization coefficient related to a second weight parameter group corresponding to the second feature variable group in a manner that the loss function is smaller, among a set of weight parameters configuring a prediction model, in a loss function related to a difference between a prediction result output in a case of inputting the set of feature variables to the prediction model and ground truth data corresponding to the feature variables.

Abstract: To infer a value of an assignment variable with high accuracy. In a causal relation inference device including a processor configured to execute a program and a storage device storing the program, the processor is configured to execute a first calculation process of calculating an internal vector based on a feature vector of a plurality of samples and a first learning parameter, a second calculation process of calculating a reallocation vector based on a second learning parameter and the internal vector calculated by the first calculation process, and a third calculation process of calculating a pointwise weight vector for each of the plurality of samples based on a third learning parameter and the reallocation vector calculated by the second calculation process.

Abstract: The analyzing apparatus: generates first internal data; converts a position of first feature data in a feature space, based on the first internal data and a second learning parameter; reallocates, based on a result of first conversion and the first feature data, the first feature data to a position obtained through the conversion in the feature space; calculates a predicted value of a hazard function of analysis time in a case where the first feature data is given, based on a result of reallocation and a third learning parameter; optimizes the first to third learning parameters, based on a response variable and a first predicted value; generates second internal data, based on second feature data and the optimized first learning parameter; converts a position of the second feature data in the feature space, based on the second internal data and the optimized second learning parameter; and calculates importance data.

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: Facilitation of an explanation about an object to be analyzed is realized with high accuracy and with efficiency. A data analysis apparatus is disclosed which uses a first neural network configured with an input layer, an output layer, and two or more intermediate layers provided between the input layer and the output layer. Each performs a calculation by giving data from a layer of a previous stage and a first learning parameter to a first activation function and outputs a calculation result to a layer of a subsequent stage. The data analysis apparatus includes a conversion section; a reallocation section; and an importance calculation section.

Abstract: To present a relationship among the factors F at different time points using continuous feature amounts. A generation device executes: visualization element generation processing, based on time series data including feature amounts of a plurality of different factors existing in time series, a feature amount range visualization element that defines a continuous feature amount range for each of the factors, and generating an inter-feature amount visualization element that defines relevance between a first feature amount of a first factor and a second feature amount of a second factor that are continuous in time, and visualization element generation processing of generating visualization information indicating a relationship of the feature amounts related to the plurality of different factors by associating, by the inter-feature amount visualization element, a first feature amount range visualization element of the first factor with a second feature amount range visualization element of the second factor.

Abstract: A data analysis apparatus executes: acquiring a group of learning input data; setting a plurality of first hash functions; substituting each learning input data in each of the plurality of first hash functions to thereby calculate a plurality of first hash values; selecting, for the group of learning input data and for each of the plurality of first hash functions, a specific first hash value that satisfies a predetermined statistical condition from among the plurality of first hash values; setting a second hash function; substituting, for the group of learning input data, each specific first hash value in the second hash function to thereby calculate a plurality of second hash values; and generating a learning feature vector that indicates features of the group of learning input data by aggregating the plurality of second hash values corresponding to respective specific first hash values and obtained as a result of the calculation.

Abstract: A data processing apparatus includes: a storage section storing an object-to-be-analyzed data group having factors and an objective variable per object to be analyzed; a first modulation section modulating a first factor and outputting a first modulation result per object to be analyzed; a second modulation section modulating a second factor and outputting a second modulation result per object to be analyzed; and a generation section that assigns, per object to be analyzed, a coordinate point representing the first modulation result from the first modulation section and the second modulation result from the second modulation section to a coordinate space specified by a first axis corresponding to the first factor and a second axis corresponding to the second factor, and that generates first image data obtained by assigning information associated with the objective variable of the object to be analyzed corresponding to the coordinate point to the coordinate point.

Abstract: To provide an optimum hyper parameter for determining a learning model using a natural language as a target. An optimization apparatus including: a processor and a memory and performing learning of a document set by natural language processing has an optimization section configured to determine a hyper parameter satisfying a predetermined condition on the basis of previously set group data, generate a learning model by the determined hyper parameter, and acquire a high-dimensional vector from the learning model; and a high-dimensional visualization section configured to convert the high-dimensional vector of a word or document as an analysis target on the basis of the group data.

Abstract: A data analysis apparatus executes: a selection process selecting a first feature variable group that is a trivial feature variable group contributing to prediction and a second feature data group other than the first feature variable group from a set of feature variables; an operation process operating a first regularization coefficient related to a first weight parameter group corresponding to the first feature variable group in a manner that the loss function is larger, and operating a second regularization coefficient related to a second weight parameter group corresponding to the second feature variable group in a manner that the loss function is smaller, among a set of weight parameters configuring a prediction model, in a loss function related to a difference between a prediction result output in a case of inputting the set of feature variables to the prediction model and ground truth data corresponding to the feature variables.

Abstract: The analyzing apparatus: generates first internal data; converts a position of first feature data in a feature space, based on the first internal data and a second learning parameter; reallocates, based on a result of first conversion and the first feature data, the first feature data to a position obtained through the conversion in the feature space; calculates a predicted value of a hazard function of analysis time in a case where the first feature data is given, based on a result of reallocation and a third learning parameter; optimizes the first to third learning parameters, based on a response variable and a first predicted value; generates second internal data, based on second feature data and the optimized first learning parameter; converts a position of the second feature data in the feature space, based on the second internal data and the optimized second learning parameter; and calculates importance data.

Abstract: A time series data analysis apparatus: generates first internal data, based on first feature data groups, first internal parameter, and first learning parameter; transforms first feature data's position in a feature space, based on the first internal data and second learning parameter; reallocates the first feature data, based on a first transform result and first feature data groups; calculates a first predicted value, based on a reallocation result and third learning parameter; optimizes the first-third learning parameters by statistical gradient, based on a response variable and first predicted value; generates second internal data, based on second feature data groups, second internal parameter, and optimized first learning parameter; transforms the second feature data's position in a feature space, based on the second internal data and optimized second learning parameter; and calculates importance data for the second feature data, based on a second transform result and optimized third learning parameter.

Abstract: An ultrasonography generation device includes an ultrasonic wave transceiver (1002), a user inputter (1006) which inputs input by an operator, a monitor (1011) capable of displaying an image, an image processor (1005) which generates tomographic image data of a fetus and the placenta based on signals acquired from the ultrasonic wave transceiver and sets a region of interest including a region between the fetus and the placenta according to the input from the inputter when the tomographic image data is displayed on the display, a 3D-ROI corrector (1008) which corrects the region of interest using the region of interest set by the operator and the tomographic image data and determines validity of the corrected region of interest, and a presentation part (1012) which presents the determination result from 3D-ROI corrector. The ultrasonography generation device generates a 3D image of the fetus using the corrected region of interest.

Abstract: To provide an optimum hyper parameter for determining a learning model using a natural language as a target. An optimization apparatus including: a processor and a memory and performing learning of a document set by natural language processing has an optimization section configured to determine a hyper parameter satisfying a predetermined condition on the basis of previously set group data, generate a learning model by the determined hyper parameter, and acquire a high-dimensional vector from the learning model; and a high-dimensional visualization section configured to convert the high-dimensional vector of a word or document as an analysis target on the basis of the group data.

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: A three-dimensional region of interest (ROI) is established with a high degree of accuracy, by a simple method without increasing a burden on the operator, in generating a three-dimensional projected image from medical volume data according to rendering, achieving more efficient interpretation of three-dimensional image and streamlining of diagnostic flow, with the use of the diagnostic image generation apparatus. An energy map is generated on a predetermined tomographic plane, assuming a preset start point as a reference and searching for a path that minimizes the energy, and then the path is set as a boundary of the three-dimensional ROI. The start point may be decided on the basis of the boundary inputted by a user, or the user may set the start point. The user may be allowed to adjust the boundary having been set. The boundary may also be determined on another plane orthogonal to the predetermined tomographic plane.