Abstract: Included are a 2D processing unit 11 that generates 2D latitude and longitude information by dimensionally compressing a feature vector generated from target information, and a map generation unit 12 that generates a 2D map in which a plurality of pieces of target information is plotted on a 2D plane based on a plurality of pieces of latitude and longitude information generated for a plurality of pieces of target information.

Abstract: A 2D map generation unit 3 that generates a 2D map in which positions corresponding to a plurality of feature vectors are visualized on a 2D plane based on a plurality of pieces of 2D coordinate information obtained by performing dimension compression on a plurality of word feature vectors specified for each of a plurality of words included in a plurality of texts, and a pathway generation unit 2 that uses a similarity of a plurality of word feature vectors or uses a position or a range designated in the 2D map to specify a plurality of molecules as words, and uses a knowledge database showing an intermolecular connection relationship for the specified plurality of molecules to generate a pathway representing an intermolecular interaction as a route map are included, and an environment for information analysis using a 2D map and a pathway together is provided.

Abstract: Included is a reference mark display unit 13 that specifies search target feature vectors characterizing arbitrary input search targets or relevant element feature vectors characterizing arbitrary input relevant elements, and displays a predetermined reference mark at a corresponding position on a 2D map based on coordinate information based on the specified feature vectors. By displaying a 2D map in which a reference mark is indicated at a corresponding position specified from arbitrary input information rather than a 2D map in which a plurality of search targets is merely plotted on a 2D plane, it is possible to extract a search target by designating a desired region on the 2D map with reference to a position of the reference mark corresponding to the arbitrary input information.

Abstract: Included are a 2D processing unit 11 that generates 2D latitude and longitude information by dimensionally compressing a feature vector generated from target information, and a map generation unit 12 that generates a 2D map in which a plurality of pieces of target information is plotted on a 2D plane based on a plurality of pieces of latitude and longitude information generated for a plurality of pieces of target information.

Abstract: Included are a related molecule estimation unit 12 that inputs a disease feature vector specified for a disease to be analyzed to a first trained model, thereby estimating a plurality of molecules related to the disease, a molecular property estimation unit 13 that inputs a disease feature vector and a molecule feature vector specified for the plurality of molecules estimated by the related molecule estimation unit 12 to a second trained model, thereby estimating a probability that a property of a molecule acting on the disease is causative, and a pathway generation unit 14 that generates a pathway representing an intermolecular interaction as a route map in a manner that a causative molecule is on an upstream side and a responsive molecule is on an downstream side and that a known intermolecular connection relationship is reflected by using a property of a molecule estimated by the molecular property estimation unit 13.

Abstract: A word extraction unit 11 that analyzes m texts to extract n words, a vector computation unit 12 that converts each of the m texts into a q-dimension vector and each of the n words into a q-dimension vector, thereby computing m text vectors including q axis components and n word vectors including q axis components, and an index value computation unit 13 that takes each of inner products of the m text vectors and the n word vectors, thereby computing a similarity index value reflecting a relationship between the m texts and the n words are included, and it is possible to obtain a similarity index value representing which word contributes to which text and to what extent as an inner product value by calculating an inner product of a text vector computed from a text and a word vector computed from a word included in the text.

Abstract: A relationship index value computation unit 100A that extracts n words from m texts representing contents of free conversations conducted by m patients whose severity of dementia is known, and computes a relationship index value reflecting a relationship between the m texts and the n words, a prediction model generation unit 14A that generates a prediction model for predicting severity of dementia based on a text index value group including n relationship index values for one text, and a dementia prediction unit 21A that predicts severity of dementia of a patient from a text subjected to prediction by applying the relationship index value computed by the relationship index value computation unit 100A from a text input by a prediction data input unit 20 to a prediction model are included, and severity of dementia can be predicted without performing a mini-mental state examination.

Abstract: [Problem to be Solved] Provided is a computer system that can accurately evaluate data to be analyzed without adding training data. [Solution] The computer system forms, from a matrix based on a co-occurrence frequency of first data elements forming at least one piece of data out of a plurality of data and second data elements appearing in vicinity of the first data elements, vectors for a plurality of data elements as the first data elements, calculates similarities for the first data elements on the basis of the vectors, and sets evaluation values for the first data elements on the basis of evaluation values corrected in accordance with the similarities.

Abstract: Included are a learning data input unit 10 that inputs m texts included in the medical information of patient, a similarity index value computation unit 100 that extracts n words from m texts and computes a similarity index value reflecting a relationship between the m texts and the n words, a classification model generation unit 14 that generates a classification model for classifying m texts into a plurality of phenomena based on a text index value group including n similarity index values for one text, and an unsafe incident prediction unit 21 that predicts a possibility of occurrence of falling from a text to be predicted by applying a similarity index value computed by the similarity index value computation unit 100 from a text input by a prediction data input unit 20 to a classification model, and a highly accurate classification model is generated using a similarity index value that represents which word contributes to which text and to what extent.

Abstract: Included are a learning data input unit 10 that inputs m texts as learning data, a similarity index value computation unit 100 that extracts n words from m texts and computes a similarity index value reflecting a relationship between the m texts and the n words, a classification model generation unit 14 that generates a classification model for classifying m texts into a plurality of phenomena based on a text index value group including n similarity index values for one text, and a phenomenon prediction unit 21 that predicts one of a plurality of phenomena from a text to be predicted by applying a similarity index value computed by the similarity index value computation unit 100 from a text input by a prediction data input unit 20 to a classification model, and a highly accurate classification model is generated using a similarity index value that represents which word contributes to which text and to what extent.

Abstract: A word extraction unit 11 that analyzes m texts to extract n words, a vector computation unit 12 that converts each of the m texts into a q-dimension vector and each of the n words into a q-dimension vector, thereby computing m text vectors including q axis components and n word vectors including q axis components, and an index value computation unit 13 that takes each of inner products of the m text vectors and the n word vectors, thereby computing a similarity index value reflecting a relationship between the m texts and the n words are included, and it is possible to obtain a similarity index value representing which word contributes to which text and to what extent as an inner product value by calculating an inner product of a text vector computed from a text and a word vector computed from a word included in the text.

Abstract: [Problem to be Solved] Provided is a computer system that can accurately evaluate data to be analyzed without adding training data. [Solution] The computer system forms, from a matrix based on a co-occurrence frequency of first data elements forming at least one piece of data out of a plurality of data and second data elements appearing in vicinity of the first data elements, vectors for a plurality of data elements as the first data elements, calculates similarities for the first data elements on the basis of the vectors, and sets evaluation values for the first data elements on the basis of evaluation values corrected in accordance with the similarities.