Jung-wei received his Ph.D. in Biomedical Informatics from Columbia University. His dissertation was on reciprocal solutions to make biomedical terminology/ontology and natural language process (NLP) benefit each other. In one direction, he developed NLP methods that used context- and content-based features to audit semantic classification of the Unified Medical Language System (UMLS) concepts. In the other direction, he developed methods of using the UMLS semantic information to improve NLP tasks such as word sense disambiguation (WSD) and syntactic parsing. Two of the derived journal articles were selected as best papers in Yearbook of Medical Informatics by the International Medical Informatics Association.
After completing Ph.D. in 2010, Jung-wei joined Kaiser Permanente Southern California as an informatics scientist to work with real world data and requirements in healthcare industry. He spent six years with hands-on architecting and developing a full-fledged, scalable NLP pipeline based on the Unstructured Information Management Architecture (UIMA). The pipeline generated significant value through applications in computer-assisted medical coding and preventive medicine. In addition to operational duties, he continued research along several tracks: 1) applied NLP for preventive medicine of cardiovascular diseases, 2) investigated technical benefits and issues in cross-institution NLP collaboration, 3) developed syntactic annotation guidelines with emphasis on handling ungrammatical clinical sentences, and 4) used NLP and big data technology to identify health issues in massive consumer product reviews.
In 2016, Jung-wei joined the Center for Biomedical Informatics & Biostatistics at the University of Arizona to contribute his clinical informatics skills for precision medicine and health disparity research. His current endeavor is on innovating solutions that use electronic medical records and genomic data to facilitate clinical research. The active projects involve investigation into comorbidities and methods of case-based reasoning.