Gregory Cooper, MD, PhD

Room 524
5607 Baum Boulevard
Pittsburgh, PA 15206
Phone Number: 
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Research Interests

  • Application of decision theory, probability theory, Bayesian statistics, and artificial intelligence to biomedical informatics research problems 
  • Causal modeling and discovery from clinical and high-throughput molecular data
  • Computer-aided medical diagnosis and prediction
  • Machine-learning approaches to improving patient safety
  • Biosurveillance of disease outbreaks

Appointments and Positions

Professor of Biomedical Informatics
Secondary faculty appointments in Intelligent Systems,
   Computational Biology, Computer Science and
   Information Sciences
Vice Chair of Department of Biomedical Informatics

Current Research Projects and Collaborations

Dr. Cooper’s past and current research involves the application of decision theory, probability theory, machine learning, Bayesian statistics, and artificial intelligence to biomedical informatics research problems. He has been investigating those topic areas for the past 25 years and has published over 110 peer-reviewed papers. He is currently involved in the following research projects:

Predicting Patients’ Outcomes from Clinical and Genome-Wide Data (PI of an R01 grant from the National Library of Medicine, NIH): The primary goals of this project are to develop, implement, and evaluate computer-based models that predict patient outcomes based on clinical and genome-wide data.

Bayesian Detection and Characterization of Disease Outbreaks (PI of a research project within a P01 grant from the CDC): The goals of this project are to develop, implement, deploy, and evaluate a Bayesian system for detecting and characterizing the outbreak of diseases based on input from a disease case detection system, which is a companion P01 project. 

Discovering Complex Anomalous Patterns in Data (PI of a Pitt subcontract of an NSF grant to CMU):  The goals of this project are to develop, implement, and evaluate a general and widely applicable framework for detecting potentially complex statistical patterns from data about entities in some set of interest, such as patterns of maintenance of jet aircraft in a fleet.

Detecting Deviations in Clinical Care in ICU Data Streams (Co-investigator of an R01 from NIGMS, NIH):  The goals of this project are to develop, implement, and evaluate computer-based methods that model usual clinical care and then apply those models to detect individual patient care that is anomalous. Within the ICU domain this project will investigate how often such anomalies correspond to medical management errors.

Recent Publications

Batal I, Valizadegan H, Cooper GF, Hauskrecht M. A temporal pattern mining approach for classifying electronic health record data. ACM Transactions on Intelligent Systems and Technology (to appear).

Hauskrecht M, Batal I, Valko M, Visweswaran S, Cooper GF, Clermont G. Outlier-detection for patient monitoring and alerting. Journal of Biomedical Informatics (to appear).

Hennings-Yeomans PH, Cooper GF. Improving the prediction of clinical outcomes from genomic data using multiresolution analysis. IEEE/ACM Transactions on Computational Biology and Bioinformatics (to appear).

Batal I, Cooper G, Hauskrecht M. A Bayesian scoring technique for mining predictive and non-spurious rules. Proceedings of the European Conference on Machine Learning (to appear).

Sverchkov Y, Jiang X, Cooper GF.  Spatial cluster detection using dynamic programming. BMC Medical Informatics & Decision Making12:22(2012).  Doi:10.1186/1472-6947-12-22

Sverchkov Y, Visweswaran S, Clermont G, Hauskrecht M, Cooper GF. A multivariate probabilistic method for comparing two clinical datasets. In: Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium January (2012).

Valko M, Kveton B, Valizadegan H,  Cooper GF, Hauskrecht M. Conditional anomaly detection with soft harmonic functions. In:Proceedings of the International Conference on Data Mining (2011).

Batal I, Hauskrecht M, Valizadegan H, Cooper G. A pattern mining approach for classifying multivariate temporal data. In: Proceedings of the BIBM Conference (2011).

Wagner M, Tsui F, Cooper G, Espino J, Harkema H, Levander J, Villamarin R, Voorhees R, Millett N, Keane C, Dey A, Razdan M, Hu Y, Tsai M, Brown S, Lee BY, Gallagher A, Potter M. Probabilistic, Decision-theoretic Disease Surveillance and Control. Online Journal of Public Health Informatics 3 (2011)

Tsui F, Wagner M, Cooper G, Que J, Harkema H, Dowling J, Sriburadej T, Li Q, Espino J, Voorhees R. Probabilistic Case Detection for Disease Surveillance Using Data in Electronic Medical Records. Online Journal of Public Health Informatics 3 (2011).

Lustgarten JL, Visweswaran S, Gopalakrishnan V, Cooper GF. Application of an efficient Bayesian  discretization method to biomedical data. BMC Bioinformatics (2011) July; 12:309 http://www.biomedcentral.com/1471-2105/12/309.   PMID: 21798039 PMC3162539

Jiang X, Barmada MM, Cooper GF, Becich MJ. A new Bayesian network method for evaluating and discovering disease loci associations. PLoS ONE (2011) Aug; http://dx.plos.org/10.1371/journal.pone.0022075.  PMID: 21853025 PMC3154195

Wei W, Visweswaran S, Cooper GF. The application of naive Bayes model averaging to predict Alzheimer’s disease from genome-wide data.Journal of the American Medical Informatics Association (2011) Jul 1; 18(4): 370-5.  PMID: 21672907 PMC3128400.

Shen Y, Cooper GF. Multivariate Bayesian modeling of known and unknown causes of events – An application to biosurveillance.  Journal of Computer Methods and Programs in Biomedicine (Dec 30, 2010). doi:10.1016/j.cmpb.2010.11.015  PMID: 21195503