Fuchiang (Rich) Tsui, PhD, FAMIA, IEEE Senior Member
Associate Professor of Anesthesiology and Critical Care at the Hospital of the University of Pennsylvania and the Children's Hospital of Philadelphia
Director, Tsui Laboratory, Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia
Science Director, Biomedical Informatics Program, Department of Anesthesiology and Critical Care Medicine,, Children's Hospital of Philadelphia
Scientist, Endowed Chair in Biomedical Informatics and Entrepreneurial Science, Children's Hospital of Philadelphia
Department: Anesthesiology and Critical Care
Contact information
Children's Hospital of Philadelphia
Department of Anesthesiology and Critical Care Medicine
Roberts Center for Pediatric Research
2716 South Street
Philadelphia, PA 19146
Department of Anesthesiology and Critical Care Medicine
Roberts Center for Pediatric Research
2716 South Street
Philadelphia, PA 19146
Email:
tsuif@chop.edu
tsuif@chop.edu
Publications
Education:
BS (Electrical Engineering)
Tatung University, Taipei, Taiwan, 1988.
MS (Electrical Engineering)
University of Pittsburgh, Pittsburgh, PA, 1993.
PhD (Electrical Engineering)
University of Pittsburgh, Pittsburgh, PA, 1996.
Permanent linkBS (Electrical Engineering)
Tatung University, Taipei, Taiwan, 1988.
MS (Electrical Engineering)
University of Pittsburgh, Pittsburgh, PA, 1993.
PhD (Electrical Engineering)
University of Pittsburgh, Pittsburgh, PA, 1996.
Description of Research Expertise
Dr. Tsui's research interest includes clinical informatics, natural language processing, artificial intelligence and machine learning, population informatics, data science, data engineering, mobile healthcare, data warehouse, and large real-time clinical production systems. He has published 100+ peer-reviewed papers and has been working in healthcare field for more than 25 years.To translate academic research into clinical practice, Dr. Tsui has directed several projects for real-time patient risk prediction by integrating hospital EHR systems and predictive modeling, e.g., the Cardiac Intensive Care Warning Index (C-WIN), the Infant Mortality Prediction System with Intervention Management (IMPreSIv), and the System for Hospital Adaptive Readmission Prediction and Management (SHARP). He was one of funding members of the Real-time Outbreak and Disease Surveillance (RODS) system and the National Retail Data Monitor (NRDM) system at the University of Pittsburgh.
Dr. Tsui teaches the course of “Introduction to Biomedical and Health Informatics” at the Perelman School of Medicine, University of Pennsylvania. He has served as a dissertation advisor of doctoral students and a mentor of post-doctoral fellows and junior faculty members.
Dr. Tsui’s Laboratory (www.tsuilab.com) at the Children’s Hospital of Philadelphia and the University of Pennsylvania has been hosting international visiting scholars from different countries to work on various biomedical informatics research projects. His laboratory welcome (international) visiting scholars and research collaboration.
Selected Publications
Arcia A, Benda NC, Wu DTY.: Advancing the science of visualization of health data for lay audiences. J Am Med Inform Assoc. 31(2): 283-288, Jan. 2024.Chen CC, Massey SL, Kirschen M, Yuan I, Padiyath A, Simpao A, Tsui FR: Electroencephalogram-based Machine Learning Models to Predict Neurologic Outcome after Cardiac Arrest: A Systematic Review. Resuscitation 194, Jan 2024 Notes: doi: 10.1016/j.resuscitation.2023.110049.
Van de Kamp E, Ma J, Monangi N, Tsui FR, Jani SG, Kim JH, Kahn RS, Wang CJ: Addressing health-related social needs and mental health needs in the neonatal intensive care unit: exploring challenges and the potential of technology. International Journal of Environmental Research and Public Health 20(24), December 2023.
Dysart G, Davis M, Doupnik S, Hamm M, Schwartz K, George-Milford B, Bush M, Ryan N, Tsui R, Melhem N, Stepp S, Brent B, and Young J: Provider, caregiver, and adolescent pre-implementation perceptions of a predictive algorithm to identify adolescents at risk for suicide in pediatric primary care. 16th Annual Conference on the Science of Dissemination and Implementation in Health Dec. 2023.
Yu H, Simpao AF, Ruiz VM, Nelson O, Muhly WT, Sutherland TN, Gálvez JA, Pushkar MB, Stricker PA, Tsui FR,: Predicting pediatric emergence delirium using data-driven machine learning applied to electronic health record dataset at a quaternary care pediatric hospital. Journal of American Medical Informatics Association (JAMIA) Open 6(4), Dec. 2023 Notes: 10.1093/jamiaopen/ooad106.
Shaeffer G, Muthu N, Tsui FR, Shi L, Jenkins J, Grundmeier R: A Novel Software Platform for EHR-Integrated Real-Time Inpatient Applications. Annual Symposium, American Medical Informatics Association (AMIA) Nov. 2023.
Ruiz V, Neal D, Shi L, Nadine M, Young J, Davis M, Brent D, Tsui FR: Deriving a Point Scoring Index for Suicide Attempt Prediction Based on Youth Depression Screenings in a Quaternary Care Children’s Hospital. Annual Symposium, American Medical Informatics Association (AMIA) Nov. 2023.
Shi L, Mi F, Tsui FR: Pediatric PhysioWarehouse: A Data Warehouse for Multi-modal Machine Learning and Deep Learning with High-Speed Physiological Data, EHR, and PC4 Data. Annual Symposium, American Medical Informatics Association (AMIA) Nov. 2023.
Tsui FR, Ruiz VM, Ryan ND, Melhem N, Brent DA, McKibben J, Malecky A, Gharani P, Marroquin O, Ackerman K, Schlesinger A, Wolfson D, Brodine D: Predicting Population-Level Youth Suicide Attempts from Electronic Health Records in a Large Healthcare System. Annual Symposium, American Medical Informatics Association (AMIA) Nov. 2023.
Han S, Richie R, Shi L, Tsui FR: Developing Deep Neural Networks for Personalized Matching of Researcher Biosketches to Funder Requests for Proposals. Annual Symposium, American Medical Informatics Association (AMIA) Nov. 2023.