South China Normal University, China
Dr. Tianyong Hao is a full Professor at School of Computer Science. He received his Ph.D. degree at City University of Hong Kong in 2010. He studied at York University, Canada, in 2008 and Emory University, USA, in 2009. After that, he worked at University of New South Wales, Australia, in 2012 and at Columbia University until 2014. Dr. Hao is a senior member of IEEE/CCF and committee member of ISO TC37, SAC TC52 (associate secretary), CCF Chinese Information Technology, CCF YOCSEF GZ (chair), CIPS Health Informatics (associate secretary), etc. He is the lead guest editor of several SCI journals including JMIR Medical Informatics and also serves for IEEE TII, ACM TIST, KAIS, JBI, as well as top-tier conferences such as AAAI, IJCAI, COLING, etc. He has published more than 90 SCI/EI indexed papers including IEEE TKDE, ACM TALIP, JBI, JAIST, KBS, C&E, etc. He is the PI of 2 grants from National Science Foundation of China and the leader of provincial level research team on NLP for big data. He owns 6 best paper awards, 1 ISO issued international standard (DIS), 4 granted patents, 15 national software copyrights. His research interests include natural language processing, health text mining, and question answering.
Large Scale Clinical Trial Text Processing and Mining
Clinical trials generate highly relevant evidences for effective disease treatments. The extraction of necessary information from a large scale clinical trial text through natural language processing for patient characteristic aggregation remains a research problem due to the complex of the investigator-authored free-text. By collaborating with a research group at Columbia University Medical Center, our research group has made some progresses on several research topics. This talk will introduce the recent research on: an extensible approach for automated semantic tag mining, clinical trial clustering by similar eligibility criteria, disease named entity recognition, temporal expression extraction and normalization, transgender identification for enhancing clinical trial recruitment, and measurable quantitative information extraction and normalization.