Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Field |
|---|---|---|
2016 | PKAW | Health care,Data mining,Computer science,Decision support system,Knowledge management,Exploit,Knowledge extraction,R-CAST,Word2vec,Clinical decision support system,Knowledge acquisition |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
12 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bo Hu | 1 | 161 | 27.21 |
| Boris Villazón-terrazas | 2 | 190 | 21.23 |