Building a Mental Health Knowledge Model to Facilitate Decision Support

摘要

Medical research produces a vast amount of data everyday through for instance high throughput preclinical and clinical tools. Exploiting such a source of knowledge, as well as discovering patterns and relations buried within, can offer great help to clinical professionals in high quality health care services. There is a growing reliance on advanced computing technologies to help make sense and comprehend such data. In this paper, we describe the application of Word2Vec to facilitate knowledge discovery from very-large public unstructured text corpora (worked with PubMed thus far, but can easily incorporate others). Benefit from unsupervised word embedding, we experiment how new knowledge can stem from peer-reviewed medical publications and cross-reference such knowledge with established one to understand the advantages and disadvantages of popular deep-learning based approaches to knowledge acquisition. We also developed a proof-of-concept computer system to exploit such knowledge in a medical recommendation system.