Fuchiang (Rich) Tsui, PhD

Room 437L
5607 Baum Blvd.
Phone: 
412-648-7182
Fax: 
412-648-9118
Admin Support: 

Primary Appointment: 

Associate Professor, Department of Biomedical Informatics
Associate Professor of Intelligent Systems
Associate Professor of Bioengineering
Associate Professor of Clinical and Translational Science
Associate Director, RODS Laboratory
Biomedical Informatics Training Program Core Faculty, University of Pittsburgh School of Medicine
Peer-reviewed Publications: 
100
Biography
Dr. Tsui's research interest includes Clinical informatics, public health informatics, biosurveillance, machine learning, (big) data mining, artificial intelligence, natural language processing, mobile healthcare, precision medicine, data warehouse, time series analysis, signal processing, and large real-time production systems. He has been working in medical informatics for more than 18 years. Dr. Tsui has produced over 100 peer-reviewed publications and his publications have been actively cited (Google h-index: 39). Dr. Tsui has extensive knowledge and experience building large informatics production systems. At the University of Pittsburgh, he received his PhD in Electrical Engineering, premed training, and postdoctoral training in medical informatics. He has built several large real-time informatics production systems: (1) Clinical Event Monitor System (CLEM) that serves as a decision support system and reports notifiable diseases at University of Pittsburgh Medical Center (UPMC) in real-time, (2) Realtime Outbreak and Disease Surveillance System (RODS) that collects and monitors emergency department visits at UPMC, and (3) National Retail Data Monitor (NRDM) that collects and monitors over-the-counter medication sales from over 32,000 retail stores across the nation. Recently, he has been working with Children's Hospital of Pittsburgh of UPMC to build his 4th production system SHARP, a realtime decision support system that helps clinicians and patient care teams identify and manage patients at high-risk for 30-day readmission.
Research/Scholarship Interests: 
Public Health Informatics; clinical informatics; Machine Learning; biosurveillance; artificial intelligence; natural language processing; (big) data mining; mobile healthcare; precision medicine; data warehouse; time series analysis; signal processing; large real-time production systems; Commerce; Thermometers
(Legend: Current Major/ Current Minor, Non-current Major/ Non-current Minor)
Honors & Awards: 
2016
Innovation Award, University of Pittsburgh
2014
Distinguished Poster Award in 2014 American Medical Informatics Association annual conference, Washington, DC (among 400+ posters)
2013
Interviewed by Pittsburgh Business Times about my latest research work on hospital readmission reduction that could have strong impact to patient care and cost reduction (http://www.bizjournals.com/pittsburgh/print-edition/2013/11/22/analytic-tool-helps-docs-make-better.html)
2012
Scientific achievement Award for Outstanding Research Articles in Biosurveillance, 2012 Annual International Society for Disease Surveillance: Expanding Collaborations to Chart a New Course in Public Health Surveillance (December 4-5, 2012, San Diego, California), International Society for Disease Surveillance
2010
Invited meeting with the President of Taiwan (President Ma) at the Presidential Building; selected elites in public health surveillance field
2002
Nomination for best paper, 2002 Annual Symposium for Computer Applications in Medical Care: Biomedical Informatics: One Discipline (November 9 - 13, San Antonio, TX), AMIA
1998
Nomination for best paper, 1998 Annual Symp. for Computer Applications in Medical Care: A Paradigm Shift in Health Information Systems: Clinical Infrastructures for the 21st Century (Nov. 7-11), AMIA
1997
Nomination for best paper, 1997Annual Symposium for Computer Applications in Medical Care: The Emergence of 'Internetable' Health Care. Systems That Really Work (October 25-29, Nashville, TN), AMIA
Grants & Contracts: 
09/2016 - 02/2018
Children's Hospital of Pittsburgh of UPMC Under agreement
C-WIN: Cardiac Intensive Care Unit Warning System
Role: PI (sole)
08/2016 - 07/2018
Richard King Mellon Foundation
Infant mortality prediction and intervention in Allegheny County
Role: PI (sole)
05/2015 - 04/2018
Local Foundation
System for Hospital Adaptive Readmission Prevention and Reduction (SHARP)
Role: PI (sole)
01/2015 - 01/2018
Coulter Foundation
Readmission technology development for Industrial applications
Role: PI (sole)
09/2014 - 08/2015
Pennsylvania Innovation Works
Post discharge monitoring through mobile application
Role: PI (sole)
07/2014 - 06/2016
NIH 5UL1 TR000005
University of Pittsburgh Clinical and Translational Science Institute
Role: Co-Investigator
02/2014 - 01/2015
First Gear, University of Pittsburgh N/A
Hospital Readmission Prediction
Role: PI (sole)
01/2014 - 06/2016
PCORI
PaTH towards a Learning Health System for the Mid-Atlantic Region
Role: Co-Investigator
07/2013 - 06/2016
NIH/NLM 1 R01 LM011370
Probabilistic Disease Surveillance
Role: Co-Investigator
09/2008 - 09/2010
Centers for Disease Control and Prevention U38HK000063
PA-OH BIG: Building a Regional Biosurveillance Grid for Pennsylvania and Ohio
Role: Principal Investigator (sole)
05/2008 - 08/2009
Houston Department of Health and Human Services 460000897 (08-0672)
The Houston RODS System Expansion of Functionality
Role: Principal Investigator (sole)
01/2008 - 06/2011
Pennsylvania Department of Health SAP #40000012020
Pennsylvania (PA) Biosurveillance
Role: Co-Investigator
2008
1U38HK000063-01
PA-OH BIG: BUILDING A REGIONAL BIOSURVEILLANCE GRID FOR PENNSYLVANIA AND OHIO
 
Teaching Activities: 
08/31/2015 - 12/19/2015 BIOINF - 2011 - 14178 FDS CLIN & PH INFORMATICS
08/31/2015 - 12/19/2015 BIOINF - 2120 - 19867 SYMBLC MTHS IN ARTFCL INTELGNC

Patent:

1. SYSTEM FOR HOSPITAL ADAPTIVE READMISSION PREDICTION AND MANAGEMENT (SHARP), Pub. No.: US 2015/0081328 A1, Pub. Date: Mar. 19, 2015

 

Publications

Gesteland PH, Gardner RM, Tsui FC, Espino JU, Rolfs RT, James BC, Chapman WW, Moore AW, Wagner MM. Automated syndromic surveillance for the 2002 Winter Olympics. J Amer Med Inform Assoc. 2003, 10(6): 547-54. PMID: 12925547. PMCID: PMC264432

Panackal AA, M’ikanatha NM, Tsui F-C, McMahon J, Wagner MM, et al.  Automatic electronic laboratory-based reporting of notifiable infectious diseases at a large health system. Emerg Infect Dis. 2002 Jul;8(7):685-91. PMID: 12095435 PMCID: PMC2730325

Gesteland PH, Wagner MM, Chapman WW, Espino JU, Tsui FC, Gardner RM, Rolfs RT, Dato V, James BC, Haug PJ. (2002). Rapid deployment of an electronic disease surveillance system in the state of Utah for the 2002 Olympic Winter Games. Proceedings of the AMIA Symposium, 285–289. PMID: 12463832 PMCID: PMC2244330

Tsui FC, Espino JU, Wagner MM, Gesteland P, Ivanov O, Olszewski RT, Liu Z, Zeng X, Chapman W, Wong WK, Moore A. (2002). Data, network, and application: technical description of the Utah RODS Winter Olympic Biosurveillance System. Proceedings of the AMIA Symposium, 815–819. PMID: 12463938 PMCID: PMC2244477

Tsui FC, Wagner MM, Dato V, Chang CC. Value of ICD-9 coded chief complaints for detection of epidemics. Proceedings / AMIA ... Annual Symposium. AMIA Symposium. 2001;711-5. PMID: 11825278 PMCID: PMC2243339

Wagner MM, Tsui FC, Espino JU, Dato VM, Sittig DF, Caruana RA, McGinnis LF, Deerfield DW, Druzdzel MJ, Fridsma DB. The emerging science of very early detection of disease outbreaks. J Public Health Manag Pract. 2001 Nov;7(6):51-9. PMID: 11710168

Tsui FC, Wagner MM, Wilbright W, Tse A, Hogan WR. A feasibility study of two methods for end-user configuration of a clinical event monitor. Proceedings / AMIA ... Annual Symposium. AMIA Symposium. 1999;975-8.  PMID: 10566506 PMCID: PMC2232807

Wagner, M. M., Pankaskie, M., Hogan, W., Tsui, F. C., Eisenstadt, S. A., Rodriguez, E., & Vries, J. K. (1997). Clinical event monitoring at the University of Pittsburgh. Proceedings of the AMIA Annual Fall Symposium, 188–192. PMID: 9357614 PMCID: PMC2233316

Tsui FC, Li C, Sun M, Sclabassi RJ. "Acquiring, Modeling and Predicting Intracranial Pressure in the Intensive Care Unit,". Biomedical Engineering, Applications, Basis, and Communications. 1996;8(6):566-578.

Michael J. Becich MD, PhD, professor and chair of the Department of Biomedical Informatics, focuses on developing datawarehouses and data mining strategies for genomic and proteomic data derived from high throughput methodologies such as expression microarrays and tissue microarrays. His interests also include tissue bank information systems, clinical information systems and imaging repositories that are currently operating in the Pathology Department at University of Pittsburgh. He is also the leader for the University of Pittsburgh’s Cancer Biomedical Informatics Grid (caBIG) projects and the Informatics Codirector of Pitt’s Clinical and Translational Science Institute. Becich currently serves as PI or Co-PI on eight funded grants, including grants from the NCI, the Department of Defense, Agency for Healthcare Research and Quality, and the PA Commonwealth Department of Health.

Sample of Related Publications:

Patel AA, Kajdacsy-Balla A, Berman JJ, Bosland M, Datta MW, Dhir R, Gilbertson J, Melamed J, Orenstein J, Tai KF, Becich MJ. The development of common data elements for a multi-institute prostate cancer tissue bank: The Cooperative Prostate Cancer Tissue Resource (CPCTR) experience. BMC Cancer. 2005 Aug 21;5:108.

Several faculty members, including  Rich Tsui, PhD and Greg Cooper, MD, PhD, investigate methods for real-time detection and assessment of disease outbreaks within the Realtime Outbreak and Disease Surveillance (RODS) Laboratory. Founded in part by Michael Wagner, MD, PhD (funded by a R01 grant) and Rich Tsui PhD, and Jeremy Espino, MD, the RODS Laboratory is a biosurveillance research laboratory that is home to three large projects that work with health departments to create surveillance systems: the RODS Open Source Project, Pennsylvania RODS, and the National Retail Data Monitor (NRDM).

These projects benefit the public and also benefit the research by grounding our work in actual public health practice and by collecting surveillance data for algorithm validation and investigations into the value of different types of novel data for outbreak detection. Current research interests of the faculty include algorithm development, assessment of novel types of surveillance data, grid computing, natural language processing, and analyses of detectability. Current funding sources include the Centers for Disease Control, the National Library of Medicine, and the Houston Health Department.

Sample of Related Publications

Cooper GF, Villamarín R, Tsui F-CMillett N, Espino JUWagner MM, A method for detecting and characterizing outbreaks of infectious disease from clinical reports, J Biomed Inform. 2014 Aug 30. pii: S1532-0464(14)00192-0. doi: 10.1016/j.jbi.2014.08.011

Ye Y, Tsui F.-C., Wagner M, Espino JU, Li Q, Influenza detection from emergency department reports using natural language processing and Bayesian network classifiers, Journal of American Medical Informatics Association, Jan. 2014, PMID: 24406261

Rexit R, Tsui F.-C., Espino J, et. al., An analytics appliance for identifying (near) optimal over-the-counter medicine products as health indicators for influenza surveillance, Journal Manager of Information Systems, 2014

Liu TY, Sanders JL, Tsui F.-C., Espino JU, Dato VM, and Suyama J, Association of Over-The-Counter pharmaceutical sales with Influenza-Like-Illnesses to patient volume in an urgent care setting, PLOS One, 8(3), Mar. 2013

Wagner MM, Moore A, Aryel R, editors. Handbook of Biosurveillance. New York: Elsevier; 2006.

Hogan WR, Cooper GF, Wallstrom GL, Wagner MM, Depinay JM. The Bayesian aerosol release detector: an algorithm for detecting and characterizing outbreaks caused by an atmospheric release of Bacillus anthracis. Stat Med. 2007 Dec 20;26(29):5225-52. PMID: 17948918

Espino J., Hall K., White P., Washington D., Grant A., Hume A., Antonioletti M., Krause A., Jackson M., Tsui F.-C. and Heinbaugh W. Open-source Collaboration in Practice between RODS, NCPHI Research Lab, University of Edinburgh and Tarrant County Public Health, 2008 PHIN Conference.

The Tsui lab is affiliated with the Department of Biomedical Informatics at the University of Pittsburgh. Under the direction of Profssor Tsui, the lab works closely with hospitals and frontline clinicians to collect and analyze big clinical data that leads to new hypotheses, patient outcome prediction and management, and risk reduction and intervention. We focus on four key research areas: Clinical data science, Natural language processing, Mobile health, and Real-time production systems.

Steven Handler, MD, PhD, Assistant Professor with a primary appointment in the Department of Biomedical Informatics and secondary appointments in Geriatric Medicine, and Clinical and Translational Research focuses on clinical and translational informatics in the long-term care setting.  His interests include developing and testing active medication monitoring systems to enhance the detection and response to potential adverse drug events, improving medication safety and adherence during care transitions, and the application of information technology to improve the quality, efficiency, and cost of nursing home care.

Clinical care involves making many predictions under uncertainty, including risk assessment, diagnosis, prognosis and therapeutic management. The better those predictions can be made, the better clinical care is likely to be. The increasing availability and richness of electronic health records (EHRs) are increasing the opportunities for developing and deploying computer-based clinical prediction methods. Such methods can serve as key components of computer-based decision support systems. The data in EHRs can be used to construct prediction models using machine learning methods. Individual patient data from EHRs can also serve as input to the predictions models.

Gregory F. Cooper, M.D., Ph.D. and Shyam Visweswaran, M.D., Ph.D. are leading projects to apply artificial intelligence, machine learning, and Bayesian modeling to develop clinical prediction models from data. These projects are currently developing predictive models from both clinical and genome-wide data using Bayesian statistics and machine learning methods. Bayesian methods are especially well suited for combining prior knowledge (e.g., from the literature) with current data (e.g., from high throughput experiments) to derive predictive models.

These projects are also developing patient-specific prediction models. In contrast to population-based models that are constructed to perform well on average on all future patient cases, patient-specific models are optimized to predict well for a particular patient case under consideration.

Sample of Related Publications:

Visweswaran S, Cooper GF. Instance-specific Bayesian model averaging for classification. In: Advances in Neural Information Processing Systems (NIPS) (2004) 1449-1456.

Cooper GF, Abraham V, Aliferis CF, Aronis JM, Buchanan BG, Caruana R, Fine MJ, Janosky JE, Livingston G, Mitchell T, Monti S, Spirtes P. Predicting dire outcomes of patients with community acquired pneumonia. Journal of Biomedical Informatics (2005) 347-366.

Visweswaran S, Cooper GF. Patient-specific models for predicting the outcomes of patients with community acquired pneumonia. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (2005) 759-763.

Visweswaran S, Angus DC, Hsieh M, Weissfeld L, Yealy D, Cooper, GF. Learning patient-specific predictive models from clinical data. Journal of Biomedical Informatics (2010) 669-685.

Cooper GF, Hennings-Yeomans P, Visweswaran S, Barmada M. An efficient Bayesian method for predicting clinical outcomes from genome-wide data. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (November 2010).

Visweswaran S, Cooper GF. Learning instance-specific predictive models. Journal of Machine Learning Research (to appear).

Posada J, Tsui FR. Inpatient 30-day readmission prediction using cTAKES.  Science2015 - Unleashed; Pittsburgh, PA 2015.

Pages

^