Assistant Professor, Health Outcomes & Biomedical Informatics
Faculty, Institute for Child Health Policy
Phone: (352) 294-8436
Clinical and Translational Research Building
2004 Mowry Road
PO Box 100177
Gainesville, FL 32610-0177
- Ph.D., Computer Application Technology Harbin Institute of Technology, Harbin, China
- M.S., Computer Science and Technology Harbin Institute of Technology, Harbin, China
- B.S., Computer Science and Technology Harbin University of Science and Technology, Harbin China
Dr. Wu is an Assistant Professor in the College of Medicine, Department of Health Outcomes & Biomedical Informatics at the University of Florida. Dr. Wu’s research interests include Natural Language Processing (NLP) and Machine Learning. He has published over 60 peer-reviewed papers and has been the principal investigator for a number of grants, including an NLP grant from Patient-Centered Outcomes Research Institute. His research has contributed substantially to clinical and biomedical NLP – including information extraction from clinical notes and biomedical literature, Word Sense Disambiguation (WSD) for ambiguous biomedical terms; predictive modeling for drug adverse reactions and drug new indications (known as drug repurposing); various applications to apply NLP and machine learning to solve clinical and translational problems.
Dr. Wu received his Ph.D. from the Harbin Institute of Technology, School of Computer Science with a major in natural language processing. Then, he entered medical informatics research with a motivation to help improve the quality of healthcare delivery and the safety of patients. He has Biomedical informatics training at Vanderbilt University (2010-2012) and then University of Texas Health Science Center at Houston (2012-2014)
- UF BMI Team Ranks Third in Clinical Natural Language Processing Challenge
- HOBI Faculty Member’s Paper Awarded ‘Best Natural Language Processing Article of the Year’ by IMIA
- BMI Team Welcomes New Faculty Member Yonghui Wu, Ph.D.
- Clinical Natural Language Processing
- Machine Learning
- Yang X, Bian J, Fang R, Bjarnadottir RI, Hogan WR1, Yonghui Wu † Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting. J Am Med Inform Assoc. August 2019.
- Xiang Y, Xu J, Si Y, Li Z, Rasmy L, Zhou Y, Tiryaki F, Li F, Zhang Y, Yonghui Wu, Jiang X, Zheng W, Zhi D, Tao C, Xu H. Time-sensitive clinical concept embeddings learned from large electronic health records. BMC medical informatics and decision making. 2019;19(2).
- Yonghui Wu, Warner JL, Wang L, Jiang M, Xu J, Chen QX, Nian H, Dai Q, Du X, Yang P, Denny JC, Liu H, Xu H. Discovery of non-Cancer Drug Effects on Survival in Electronic Health Records of Cancer Patients: a New Paradigm for Drug Repurposing. JCO Clin Cancer Inform. 2019;3:1-9. doi:10.1200/CCI.19.00001.
- Lo-Ciganic W, Huang JL, Zhang HH, Weiss JC, Yonghui Wu, Kwoh CK, Donohue JM, Cochran G, Gordon AJ, Malone DC, Kuza CC, Gellad WF. Evaluation of machine-learning algorithms for predicting opioid overdose risk among Medicare beneficiaries with opioid prescription. JAMA Netw Open. 2019;2(3):e190968. DOI:10.1001/jamanetworkopen.2019.0968.
- Yang X, Bian J, Gong Y, Hogan WR, Yonghui Wu.†. MADEx: A System for Detecting Medications, Adverse Drug Events, and Their Relations from Clinical Notes. Drug Safety. 2019:1-11. doi:10.1007/s40264-018-0761-0
- Bekhet L, Yonghui Wu, Wang N, Geng X, Zheng W, Wang F, Wu H, Xu H, Zhi D. A study of Generalizability of Recurrent Neural Network-Based Predictive Models for Heart Failure Onset Risk using a Large and Heterogeneous EHR Data set. J Biomed Inform. 2018;84:11-16. doi:10.1016/j.jbi.2018.06.011.
- Soysal E, Wang J, Jiang M, Yonghui Wu, Pakhomov S, Liu H, and Xu H. CLAMP – a toolkit for efficiently building customized clinical natural language processing pipelines. J Am Med Inform Assoc. November 2017. doi:10.1093/jamia/ocx132.
- Yonghui Wu, Joshua C Denny, S Trent Rosenbloom, Randolph A Miller, Dario A Giuse, Lulu Wang, Carmelo Blanquicett, Ergin Soysal, Jun Xu, Hua Xu. A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD). J Am Med Inform Assoc 2016 August. doi: 10.1093/jamia/ocw109.
- Jun Xu, Hee-Jin Lee, Jia Zeng, Yonghui Wu, Yaoyun Zhang, Liang-Chin Huang, Amber Johnson, Vijaykumar Holla, Ann M. Bailey, Trevor Cohen, Funda Meric-Bernstam, Elmer Bernstam, Hua Xu. Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov. J Am Med Inform Assoc 2016 Jul;23(4):750-7.
- Yonghui Wu, Joshua C. Denny, S. Trent Rosenbloom, Randolph A. Miller, Dario A. Giuse, Min Song, Hua Xu. A Preliminary Study of Clinical Abbreviation Disambiguation in Real Time. Appl Clin Inform 2015, 6(2), 364-374.
- Yonghui Wu, Mia A Levy, Christine M Micheel, Paul Yeh, Buzhou Tang, Michael J Cantrell, Stacy M Cooreman and Hua Xu. Identifying the status of genetic lesions in cancer clinical trial documents using machine learning. BMC Genomics 2012;13(Suppl 8):S21.
- Mei Liu,Yonghui Wu, Yukun Chen, Jingchun Sun, Zhongming Zhao, Xue-wen Chen, Michael Edwin Matheny, Hua Xu. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. J Am Med Inform Assoc 2012 Jun 1;19(e1):e28-e35
- Top-ranked (#1) Chemical-induced Disease Relations extraction system 2015 BioCreative Challenge, CDR task
- Top-ranked (#1) disorder mention recognition system 2014 Semantic Evaluation (SemEval) challenge, Task 7 – Analysis of Clinical Text
- Top-ranked (#1) clinical abbreviation disambiguation system 2013 ShARe/CLEF eHealth Shared Tasks in Clinical NLP
- Top-ranked (#1) “temporal relation extraction” system 2012 i2b2 Clinical NLP challenge
- GMS6856: Introduction to Biomedical Natural Language Processing
- GMS 6803: Data Science for Clinical Research