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Research
The Vis Lab is focused on the application of artificial intelligence and machine learning to problems in the Learning Health System (LHS) that include:
- Learning electronic medical record (EMR) system and computerized clinical decision support
- Precision medicine and personalized modeling
- Causal discovery from biomedical data
- EMR data warehousing
- 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.
Learning electronic medical record (EMR) 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 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:
- Calzoni, L, Clermont, G, Cooper, GF, Visweswaran, S, Hochheiser, H. Exploring novel graphical representations of clinical data in a learning EMR. In: AMIA Annual Symposium Proceedings. 2017 Nov 7. pdf
- 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, 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.)
- 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.
- Visweswaran, S, Mezger, J, Clermont, G, Hauskrecht, M, Cooper, GF. Identifying deviations from usual medical care using a statistical approach. In: Proceedings of the Fall Symposium of the American Medical Informatics Association. 2010 Nov 13; 2010:827-31.
- Visweswaran, S, Hanbury, P, Saul, M, Cooper, GF. Detecting adverse drug events in discharge summaries using variations on the simple Bayes model. In: Proceedings of the Fall Symposium of the American Medical Informatics Association. 2003;2003:689-93.
Precision medicine and patient-specific 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 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:
- 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.
- Visweswaran, S, Ferreira, A, Cooper, GF. Personalized modeling for prediction with decision-path models. PLoS One. 2015 Jun 22;10(6):e0131022.
- Ferreira, A, Cooper, GF, Visweswaran, S. Decision path models for patient-specific modeling of patient outcomes. In: Proceedings of the Fall Symposium of the American Medical Informatics Association. 2013 Nov 16; 2013:413-21.
- Visweswaran, S, Cooper, GF. Learning instance-specific predictive models. Journal of Machine Learning Research. 2010 Dec; 11:3369-3405.
- 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. 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, Cooper, GF. Instance-specific Bayesian model averaging for classification. In: Advances in Neural Information Processing Systems (NIPS 2004) (Dec 2004) 1449-56.
- Visweswaran, S. Learning patient-specific models from clinical data. Doctoral Dissertation, University of Pittsburgh, Sep 2007.
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 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 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:
- Strobl, EV, Visweswaran, S. Markov boundary discovery with ridge regularized linear models. Journal of Causal Inference. 2016 Mar; 4(1):31-48.
- Aflakparast, M, Masoudi-Nejad, A, Bozorgmehr, JH, Visweswaran, S. Informative Bayesian Model Selection: A method for identifying interactions in genome-wide data. Molecular BioSystems. 2014 Aug 26; 10(10):2654-62.
- 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 Jun; 112(6):666-74.
- 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. Markov blanket discovery using kernel-based conditional dependence measures. In: Proceedings of the NIPS 2013 Workshop on Causality, Lake Tahoe, NV. (Dec 2013).
- 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.
- 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, Eack, SM, Swaminathan, V, Visweswaran, S. Predicting the risk of psychosis onset: Advances and prospects. Early Intervention in Psychiatry. 2012 Nov;6(4):368-79.
- 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-Aug;18(4):370-5.
- Jiang, X, Neapolitan, RE, Barmada, MM, Visweswaran, S. Learning genetic epistasis using Bayesian network scoring criteria. BMC Bioinformatics. 2011 Mar 31;12:89.
- Jiang, X, Barmada, MM, Visweswaran, S. Identifying genetic interactions in genome-wide data using Bayesian networks. Genetic Epidemiology. 2010 Sep; 34(6):575-81.
EMR 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) NCATS-funded Accrual of patients to Clinical Trials (ACT) network, 2) NIH-funded All of Us Research Program, 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:
- 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.
- 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).
- 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).
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 and for discovery of sub-phenotypes.
Publications:
- Bhavnani SK, Visweswaran S, Divekar R, Brasier A. Towards team-centered informatics: Accelerating innovation in multidisciplinary scientific teams through visual analytics. The Journal of Applied Behavioral Science. 2019 Mar;55(1):50-72.
- Bhavnani SK, Dang B, Kilaru V, Caro M, Visweswaran S, Saade G, Smith AK, Menon R. Methylation differences reveal heterogeneity in preterm pathophysiology: Results from bipartite network analyses. Journal of Perinatal Medicine. 2018 Jul 26;46(5):509-521.
- Bhavnani, SK, Visweswaran, S, Divekar, R, Bellala, G. Where is the science in big data visual analytics? From pretty pictures to transformative biomedical discoveries. In: AMIA Joint Summits Translational Science Proceedings. 2015 Mar 23; 2015.
- Bhavnani, SK, Bryant, D, Visweswaran, S, Divekar, R, Karmarkar, A, Ottenbacher, K. How comorbidities co-occur in readmitted hip fracture patients: From bipartite networks to insights for post-discharge planning. In: AMIA Joint Summits Translational Science Proceedings. 2015 Mar 23; 2015.
- Bhavnani, SK, Dang, B, Bellala, G, Divekar, R, Visweswaran, S, Brasier, A, Kurosky, A. Unlocking proteomic heterogeneity in complex diseases through visual analytics. Proteomics. 2015 Feb 13; 15(8):1405-18.
- 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: AMIA Joint Summits Translational Science Proceedings. 2014 Apr 7; 2014:29-34.