Yong-gang Cao

TL;DR: A clinical question answering system named AskHERMES is built to perform robust semantic analysis on complex clinical questions and output question-focused extractive summaries as answers and demonstrates the potential to outperform both Google and UpToDate systems.

TL;DR: This paper explored supervised machine learning approaches to automatically classify an ad hoc clinical question into general topics and then evaluated different methods for automatically extracting keywords from an ad-hoc clinical question, achieving an average F-score of 56% on the 4,654 clinical questions maintained by the National Library of Medicine.

TL;DR: Lancet, a supervised machine-learning system that automatically extracts medication events consisting of medication names and information pertaining to their prescribed use from lists or narrative text in medical discharge summaries, can achieve a high precision with a competitive overall F1 score.

TL;DR: Two natural language processing models, namely, automatic topic assignment and keyword identification, that together automatically and effectively extract information needs from ad hoc clinical questions are reported on.

TL;DR: The supervised machine-learning algorithms Conditional Random Fields (CRFs) are applied to automatically parse a citation into its fields (e.g., Author, Title, Journal, and Year) with an overall 97.95% F1-score.