The Vis Lab is focused on on the application of artificial intelligence and machine learning to problems in biomedicine with a specific focus on developing intelligent electronic medical record systems, precision medicine and personalized modeling, data mining and causal discovery from biomedical data, and research data warehousing.

This work is funded by a R01 grant from NLM, NIH.

Electronic medical records (EMRs) are capturing increasing amounts of patient data that can be leveraged by machine learning methods for computerized clinical decision support. My work focuses on developing a learning EMR system that uses machine learning to identify and highlight relevant patient data, at the right time, to the right person. In addition, I work with a team of collaborators in developing and implementing machine learning methods for identifying anomalies in clinical management of patients and raising alerts. This work is in collaboration with Gregory F. Cooper, Harry Hochheiser, Milos Hauskrecht, Gilles Clermont, (at the University of Pittsburgh), and Dean Sittig (at the University of Texas Health Science Center at Houston).

Publications:

• 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.

• King AJ, Cooper GF, Hochheiser H, Clermont G, Visweswaran S. Development and preliminary evaluation of a prototype of a learning electronic medical record system. In: AMIA Annual Symposium Proceedings. 2015 Nov 17; 2015:1967-75. PMID: 26958296 PMCID: PMC4765593 pdf (Awarded First Place in the Student Paper Competition at the AMIA Annual Symposium, 2015.)

• King AJ, Hochheiser H, Visweswaran S, Clermont G, Cooper GF. Eye-tracking for clinical decision support: A method to capture automatically what physicians are viewing in the EMR. In: AMIA Joint Summits Translational Science Proceedings. 2017 Mar 27-30; 2017:512-21. PMID: 28815151 PMCID: PMC5543363 pdf (Awarded First Place in the Student Paper Competition at the AMIA Joint Summits Clinical Research Informatics, 2017.)

• King AJ, Cooper GF, Clermont G, Hochheiser H, Hauskrecht M, Sittig DF, Visweswaran, S. Using machine learning to selectively highlight patient information. Journal of Biomedical Informatics. 2019 Oct 29:103327. (abstract)

• King AJ, Cooper GF, Clermont G, Hochheiser H, Hauskrecht M, Sittig DF, Visweswaran, S. Leveraging eye tracking to prioritize relevant medical record data: Comparative machine learning study. Journal of Medical Internet Research. 2020;22(4):e15876 (paper)

This work is funded by a OT2 grant from the Office of the Director, NIH.

The University of Pittsburgh is funded as one of the Healthcare Provider Organizations for the national All of Us Research Program, which is a historic effort to gather data from 1 million people living in the United States. The goal of the program is to revolutionize how disease is prevented and treated based on individual differences in lifestyle, environment and genetics. Along with Steven E. Reis and Oscar Marroquin, I am a PD/PI on the All of Us Pennsylvania project that will enroll and collect data and bio specimens for 120,000 individuals for the All of Us Research Program.

In predictive modeling in medicine, the typical paradigm consists of learning a single model from a database of individuals, which is then applied to predict outcomes for any future individual. Such a model is called a population-wide model because it is intended to be applied to an entire population of future individuals. In contrast, patient-specific modeling focuses on learning models that are tailored to the characteristics of the individual at hand. Patient-specific models that are optimized to perform well for a specific individual are likely to have better predictive performance than the typical population-wide models that are optimized to have good predictive performance on average on all future individuals. Moreover, patient-specific models can identify features such as genomic factors that are specific for an individual thus enabling precision medicine. This work is in collaboration with Gregory F. Cooper (at the University of Pittsburgh). The areas of focus include:

• development of Bayesian and information-theoretic methods for learning patient-specific models from clinical and genomic data, and
• application of personalized modeling to risk assessment, diagnosis, prognosis and selection of therapy.

Publications:

• Visweswaran S. Learning patient-specific models from clinical data. Doctoral Dissertation, University of Pittsburgh, Sep 2007.

• Visweswaran S, Cooper GF. Instance-specific Bayesian model averaging for classification. In: Advances in Neural Information Processing Systems (NIPS 2004) (Dec 2004) 1449-56.

• 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; 2005:759-63.

• 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 Oct;43(5):669-85.

• Visweswaran S, Cooper GF. Learning instance-specific predictive models. Journal of Machine Learning Research. 2010 Dec; 11:3369-3405.

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

• Jabbari F, Visweswaran S, Cooper GF. Instance-specific Bayesian network structure learning. In: The 9th International Conference on Probabilistic Graphical Models. 2018 Sep 11 – 14.

Development of efficient data mining for biomarker discovery from high-dimensional genomic variant data is important for precision medicine and focuses on the use of information obtained from sequences such as whole exomes and whole genomes. Our work focuses on single nucleotide variants (SNVs) data obtained from genome-wide and whole exome data. This work is in collaboration with Gregory F. Cooper and David C. Whitcomb (at the University of Pittsburgh).

Development and application of causal discovery machine learning methods to biomedical data including large-scale electronic medical record data is important for uncovering casual relations in these data. This work is in collaboration with Gregory F. Cooper and Eric V. Strobl and is funded by a U54 grant from NHGRI, NIH.

Surging use of social media has resulted in unprecedented opportunities for examination of complex social and behavioral phenomena. We are developing machine learning infoveillance tools to study cancer communication in Twitter data. This work is in collaboration with This work is in collaboration with Brian Primack (at the University of Arkansas) and is funded by a R01 grant from NCI, NIH.

The areas of focus include:

• development of predictive models from high-dimensional genomic data,
• development of algorithms to infer causal relationships from observational data, and
• application of machine learning to build an infoveillance tool to study cancer communication in Twitter data.

Publications:

• Stokes ME, Visweswaran S. Application of a spatially-weighed Relief algorithm for ranking genetic predictors of disease. BioData Mining. 2012 Dec 3; 5(1):20.

• Strobl EV, Visweswaran S. Deep multiple kernel learning. In: Proceedings of the 12th International Conference on Machine Learning and Applications (ICMLA'13). 2013 Dec 4; 2013:414-17.

• Strobl EV, Visweswaran S. Markov blanket discovery using kernel-based conditional dependence measures. In: Proceedings of the NIPS 2013 Workshop on Causality, Lake Tahoe, NV. (Dec 2013).

• 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.

• Strobl EV, Visweswaran S. Approximate kernel-based conditional independence tests for fast non-parametric causal discovery. Journal of Causal Inference. 2019 Mar; 4(1):31-48.

• Strobl EV, Visweswaran S. Dirac delta regression: Conditional density estimation with clinical trials. arXiv preprint arXiv:1905.10330, 2019.

This work is funded by a UL1 grant from NCATS, NIH and a CDRN grant from PCORI.

Several local, regional and national efforts are ongoing that are creating clinical data repositories for reuse of EMR data for clinical, translational, and informatics research. I lead the development and implementation of a research data warehouse called Neptune. I also lead the efforts for data harmonization, translation to standard terminologies and mapping to standard value sets for several projects that include: 1) NCATS-funded Accrual of patients to Clinical Trials (ACT) network, 2) NIH-funded All of Us Pennsylvania Research Program, and 3) PCORI-funded PaTH clinical data research network. This work is in collaboration with Steven E. Reis, Michael J. Becich, and Jonathan C. Silverstein (at the University of Pittsburgh).

Publications:

• Amin W, Borromeo C, Saul M, Becich MJ, Visweswaran S. Informatics synergies between PaTH and ACT networks. In: Proceedings of the 2015 Summit on Clinical Research Informatics (Mar 2015).

• Visweswaran S, Becich MJ, D’Itri VS, Sendro ER, MacFadden D, Anderson NR, Allen KA, Ranganathan D, Murphy SN, Morrato EH, Pincus HA, Toto R, Firestein GS, Nadler LM, Reis SE. Accrual to Clinical Trials (ACT): A Clinical and Translational Science Award Consortium network. JAMIA Open. 2018 Aug 21.