Shyam Visweswaran, MD, PhD

Research Interests: 

Dr. Visweswaran's research interests include the application of artificial intelligence and machine learning to problems in the Learning Health System with a specific focus on developing learning electronic medical records (EMRs), precision medicine and personalized modeling, enabling reuse of EMR data for research, and data mining and causal discovery from genomic and biomedical data.

Associate Professor, Department of Biomedical Informatics
Associate Professor of Intelligent Systems, Clinical and Translational Science, and Computational Biology
Director of Clinical and Translational Informatics, Department of Biomedical Informatics
Co-Director, Informatics Component, Clinical and Translational Science Institute
Director, Center for Clinical Research Informatics
Co-Director, Kimball Family Center for Clinical Informatics
Biomedical Informatics Program Director of the Medical Scientist Training Program
Biomedical Informatics Training Program Core Faculty

University of Pittsburgh School of Medicine

Publications: 

King, A. J., Hochheiser, H., Visweswaran, S., Clermont, G., & Cooper, G. F. (2017). Eye-tracking for clinical decision support: A method to capture automatically what physicians are viewing in the EMR. AMIA Summits on Translational Science Proceedings, 2017, 512–521.

Sergio M. Castro, Eugene Tseytlin, Olga Medvedeva, Kevin Mitchell, Shyam Visweswaran, Tanja Bekhuis, Rebecca S. Jacobson, Automated annotation and classification of BI-RADS assessment from radiology reports, Journal of Biomedical Informatics, Volume 69, May 2017, Pages 177-187, ISSN 1532-0464, https://doi.org/10.1016/j.jbi.2017.04.011. (http://www.sciencedirect.com/science/article/pii/S1532046417300813)
 

Lustgarten JL, Balasubramanian JB, Visweswaran S, Gopalakrishnan V, Learning Parsimonious Classification Rules from Gene Expression Data Using Bayesian Networks with Local Structure.  2017 Mar;2(1). pii: 5. doi: 10.3390/data2010005. Epub 2017 Jan 18.    PMID: 28331847  PMCID:  PMC5358670  DOI: 10.3390/data2010005

Approximate Kernel-based Conditional Independence Tests for Fast Non-Parametric Causal Discovery
Eric V. Strobl, Kun Zhang, Shyam Visweswaran, - arXiv preprint arXiv:1702.03877, 2017
Pineda AL, Ogoe HA, Balasubramanian JB, Rangel Escareño C, Visweswaran S, Herman JG, Gopalakrishnan V. On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue.  BMC Cancer. 2016 Mar 4;16:184. doi: 10.1186/s12885-016-2223-3. PMID:  26944944  PMCID:  PMC4778315  DOI: 10.1186/s12885-016-2223-3
 

Hauskrecht M, Batal I, Hong C, Nguyen Q, Cooper GF, Visweswaran S, Clermont G. Outlier-based detection of unusual patient-management actions: An ICU study. Journal of Biomedical Informatics (2016) Oct 5  pii: S1532-0464(16)30135-6. doi: http://dx.doi.org/10.1016/j.jbi.2016.10.002 PMID: 27720983

Ogoe, HA, Visweswaran, S, Lu, X, Gopalakrishnan, V.  (2015) Knowledge transfer via classification rules using functional mapping for integrative modeling of gene expression data.  BMC Bioinformatics 16:226 (designated as a Highly Accessed paper) PMID: 26202217 PMCID: PMC4512094

Visweswaran S, Ferreira A, Cooper GF. Personalized modeling for prediction with decision-path models. PLoS One. 2015 Jun 22;10(6):e0131022 PMID: 26098570 PMCID: PMC4476684

Eric Strobl, Shyam Visweswaran. Markov boundary discovery with ridge regularized linear models. Journal of Causal Inference. ISSN (Online) 2193-3685, ISSN (Print) 2193-3677, DOI: 10.1515/jci-2015-0011, November 2015

Jiang X, Visweswaran S, Neapolitan RE. Mining Epistatic Interactions from High-Dimensional Data Sets Using Bayesian Networks. In: Holmes D, Jain L, editors. Foundations and Intelligent Paradigms-3. Berlin, Heidelberg: Springer-Verlag, 2011.

Stokes ME, Barmada MM, Kamboh MI, Visweswaran S. The application of network label propagation to rank biomarkers in genome-wide Alzheimer's data. BMC Genomics. 2014 Apr 14;15(1):282. PMID: 24731236

Balasubramanian JB, Visweswaran S, Cooper GF, Gopalakrishnan V. Selective model averaging with Bayesian rule learning for predictive biomedicine. In: Proceedings of the 2014 AMIA Summit on Translational Bioinformatics (Apr 2014).

Bhavnani SK, Dang B, Caro M, Bellala G, Visweswaran S, Asuncion M, Divekar R. Heterogeneity within and across pediatric pulmonary infections: From bipartite networks to at-risk subphenotypes. In: Proceedings of the 2014 AMIA Summit on Translational Bioinformatics (Apr 2014).

Aflakparast M, Salimi H, Gerami A, Dubé M-P, Visweswaran S, Masoudi-Nejad A. Cuckoo search epistasis: A new method for exploring significant genetic interactions. Heredity, 2014 Feb 19; doi: 10.1038/hdy.2014.4. PMID: 24549111

Pineda, A. L., Tsui, F.-C., Visweswaran, S., & Cooper, G. F. (2013). Detection of Patients with Influenza Syndrome Using Machine-Learning Models Learned from Emergency Department Reports. Online Journal of Public Health Informatics, 5(1), e41.

Kalamangalam GP, Pestana Knight EM, Visweswaran S, Gupta A. Noninvasive predictors of subdural grid seizure localization in children with nonlesional focal epilepsy. Journal of Clinical Neurophysiology. 2013 Feb;30(1):45-50. PMID: 23377441

Hauskrecht, M, Batal, I, Valko, M, Visweswaran, S, Cooper, GF, Clermont, G. Outlier detection for patient monitoring and alerting. Journal of Biomedical Informatics. 2013 Feb; 46(1):47-55. PMID: 22944172 PMCID: PMC3567774

Strobl EV, Visweswaran S. Deep multiple kernel learning. In: Proceedings of the 12th International Conference on Machine Learning and Applications (ICMLA'13), Miami, FL. (Dec 2013).

Hauskrecht M, Visweswaran S, Cooper GF, Clermont G. Data-driven identification of unusual clinical actions in the ICU. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (Nov 2013).

Sverchkov Y, Visweswaran S, Clermont G, Hauskrecht M, Cooper GF. A multivariate probabilistic method for comparing two clinical datasets. In: Proceedings of the ACM International Health Informatics Symposium (2012) 795-800.

Jiang X, Neapolitan RE, Barmada M, Visweswaran S, Cooper GF.   A fast algorithm for learning epistatic genomic relationships.  In: Proceedings of the Annual Symposium of the American Medical Informatics Association (2010) 341-345. PMID: 21346997 PMC3041370

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