1061 results about "Posterior probability" patented technology

A method of modelling data, comprising: training an objective function of a linear classifier, based on a set of labeled data, to derive a set of classifier weights; defining a posterior probability distribution on the set of classifier weights of the linear classifier; approximating a marginalized loss function for an autoencoder as a Bregman divergence, based on the posterior probability distribution on the set of classifier weights learned from the linear classifier; and classifying unlabeled data using the autoencoder according to the marginalized loss function.

The invention discloses a method and device for establishing a medical knowledge graph, and an auxiliary diagnosis method. The method for establishing the medical knowledge graph comprises the steps that a user dictionary is established according to a medical database; electronic medical record data is processed, and named entity recognition is conducted; correlation relations are established for each recognized entity; and the medical knowledge graph is established according to the correlation relations. The auxiliary diagnosis method based on the medical knowledge graph comprises the steps that a patient's chief complaint data and inspection data are acquired and processed, so that a symptom entity and a sign entity of the patient can be obtained; a disease entity correlated with the symptom entity and the sign entity is searched in the medical knowledge graph, and a posterior probability of each disease entity in a set composed of the corresponding symptom entity and the sign entity is computed respectively; and the disease entity with the maximum posterior probability and data corresponding to correlated nodes of the disease entity are output. According to the invention, intelligent auxiliary diagnosis is provided for clinical medical science, so that working burdens of medical workers are relieved; medical stress is relieved; and occurrence rate of medical accidents is reduced.

Embodiments of a computer-implemented method of calculating estimates of a joint posterior probability of class membership given combinations of attribute values of a pair of attributes are disclosed. The calculating is performed on the basis of data representing a training set of a plurality of instances defined by attribute values for a plurality of attributes together with a class membership outcome. Embodiments of the method comprise calculating first and second estimates of a posterior probability of class membership given attribute values of a first and second attribute of the pair, respectively, and binning the first and second estimates into a respective plurality of first and second probability range bins. Instances of the training set are mapped to combinations of one of each of the first and second pluralities of probability range bins, and on the basis of the mapping, calculating estimates of a joint posterior probability of class membership.