Dingcheng Li

TL;DR: An effective Knowledge Embedding based Question Answering (KEQA) framework that focuses on answering the most common types of questions, i.e., simple questions, in which each question could be answered by the machine straightforwardly if its single head entity and single predicate are correctly identified.

TL;DR: A data-normalization platform that ensures data security, end-to-end connectivity, and reliable data flow within and across institutions is developed and demonstrated by executing a QDM-based MU quality measure that determines the percentage of patients between 18 and 75 years with diabetes whose most recent low-density cholesterol test result during the measurement year was <100 mg/dL.

TL;DR: A comparative study between two machine learning methods, Conditional Random Fields and Support Vector Machines for clinical named entity recognition and their applicability to clinical domain is presented.

TL;DR: The corpus statistics presented here are instructive for building lexicons from the UMLS, and feature intrinsic to Metathesaurus terms (well formedness, length and language) generalise easily across clinical institutions, but term frequencies should be adapted with caution.

TL;DR: This study detected among 668 drugs of the most frequent 20 treatment regimens in the United States, 307 drugs have sex differences in ADEs and identified 736 unique drug-event combinations with significant sex differences.