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.

Learning electronic medical record system and computerized clinical decision support

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).


  • 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)

Precision medicine and patient-specific modeling

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.


Causal discovery from biomedical data

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.


  • 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.

Research data warehousing

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).