Systems and methods for relation inference

Abstract

Presented are relation inference methods and systems that use deep learning techniques for data mining documents to discover a relation between terms of interest in a given field covering a specific topic. For example, in the healthcare domain, various embodiments of the present disclosure provide for a relation inference system that mines large-scale medical documents in a free-text database to extract symptom and disease terms and generates relation information that aids in disease diagnosis. In embodiments, this is accomplished by training and using an RNN, such as an LSTM, a Gated Recurrent Unit (GRU), etc., that takes advantage of a term dictionary to examine co-occurrences of terms of interest within documents to discover correlations between the terms. The correlation may then be used to predict statistically most probable terms (e.g., a disease) related to a given search term (e.g., a symptom).