The Vis Lab is focused on the application of artificial intelligence and machine learning to problems in the Learning Health System (LHS) that include:

  • development of a learning Electronic Medical Record (LEMR) system,
  • precision medicine and personalized modeling,
  • data mining and causal discovery from biomedical data,
  • research data warehousing for clinical, translational, and informatics research, and
  • automated visual analytics.

The goal of a LHS is to deliver the best care every time, and to learn and improve with each care experience. A LHS has two arms: 1) an afferent (blue) arm that is focused on assembling data from various sources including EMR systems, mobile health (mHealth) applications and research studies into an integrated research data repository, and 2) an efferent (red) arm that is focused on returning results and findings obtained from analyses of the data repository to inform clinical decision support and patient decision support systems.

The former arm is the focus of the Center for Clinical Research Informatics (CCRI) and the latter arm is the focus of the Center for Clinical Informatics (CCI).

Development of a learning Electronic Medical Record (LEMR) system

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 decision support. My work focuses on the development of intelligent EMRs that contain adaptive and learning components to provide decision support using the right data, at the right time. In addition, I work with a team of collaborators in developing and implementing machine-learning methods for detecting adverse drug events and for identifying anomalies in clinical management of patients. 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 on intelligent electronic medical records and computerized clinical decision support include:

Precision medicine and personalized modeling

This work is funded by a UG3 grant from the Office of the Director, NIH.

The University of Pittsburgh is funded as one of the Healthcare Provider Organizations for the Cohort Program of President Obama’s Precision Medicine Initiative (PMI). The PMI Cohort Program, now called the All of Us Research Program, is a landmark longitudinal research effort that aims to engage 1 million or more U.S. participants 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 PA Cares for Us project that will enroll and collect data and bio specimens for 150,000 individuals for the PMI Cohort 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, personalized modeling focuses on learning models that are tailored to the characteristics of the individual at hand. Personalized 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, personalized 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 personalized models from clinical and genomic data, and
  • application of personalized modeling to risk assessment, diagnosis, prognosis and selection of therapy.

Publications on personalized modeling include:

Data mining and causal discovery from biomedical data

This work is funded by a U54 grant from NHGRI, NIH.

Developing efficient data mining and causal discovery methods for biomarker discovery from high-dimensional genetic variant data is important for genomic medicine. Genomic medicine is driving precision medicine and focuses on the use of information obtained from sequences such as whole exomes and whole genomes. Genomic information, in combination with clinical data, will lead to increased understanding of the biology of human health and disease, improved prediction of disease and effect of therapy, and ultimately the realization of precision medicine. Our work focuses on single nucleotide variants (SNVs) data obtained from genome-wide studies (GAWSs), and more recently whole exomes. This work is in collaboration with Gregory F. Cooper and M. Michael Barmada (at the University of Pittsburgh). The areas of focus include:

  • discovery of interacting SNVs in high-dimensional genomic data using Bayesian and information-theoretic methods,
  • development of efficient multivariate methods to identify disease-associated SNVs in high-dimensional genomic data,
  • development of computationally efficient predictive models from high-dimensional genomic data, and
  • development of algorithms to infer causal relationships from observational data.

Publications on data mining and causal discovery from high-dimensional genomic data include:

  • 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 electronic medical record data for clinical, translational, and informatics research. I lead the development and implementation of a Pitt 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) CTSI-funded i2b2 – based Cohort Discovery Repository, 2) NCATS-funded Accrual of patients to Clinical Trials (ACT) network, and 3) PCORI-funded PaTH clinical data research network (CDRN). This work is in collaboration with Steven E. Reis, Michael J. Becich, and Melissa Saul (at the University of Pittsburgh).

Publications on reuse of EMR data include:

  • 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, Tenenbaum, J, Gouripeddi, R. Secondary use of data for research - EHR, omics and environmental data. In: AMIA Joint Summits Translational Science Proceedings (Mar 2016).

Automated visual analytics

Automated visual analytics combines visual analytics with automated analysis for discovery of patterns in data. Visual analytics is interactive analysis facilitated by interactive visual interfaces where the domain expert interacts with the data visually to identify interesting patterns. However, it is effort-intensive and is readily applicable only to datasets with low dimensionality and small sample sizes. Our work focusses on automated visual analytics that combines visualization and automated analysis methods to take advantage of the rapid search that automated methods provide with the ability to identify novel and rich patterns that visual analytics provides. This work is in collaboration with Suresh K Bhavnani (at the University of Texas Medical Branch at Galveston), Kevin Bassler (University of Houston, Central), and Rohit D Divaker (Mayo Clinic, Rochester). The areas of focus include:

  • development of automated visual analytics methods that combine bipartite network visual analytics with heuristic search methods from machine-learning, and
  • application of automated visual analytics methods to genomic data for discovery of gene-gene interactions, for pathway analysis and for discovery of sub-phenotypes.

Publications on visual analytics include: