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research [2019/03/03 19:35]
shyam [Data mining and causal discovery from biomedical data]
research [2022/05/03 14:35] (current)
shyam
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 ~~NOTOC~~ ~~NOTOC~~
 +
  
 ===== Research ===== ===== 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: +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 dataand research data warehousing.
- +
-  * Learning electronic medical record (EMR) system and computerized clinical decision support +
-  * Precision medicine and personalized modeling +
-  * Data mining and causal discovery from biomedical data +
-  * Reuse of EMR data and research 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. +
- +
-{{ wiki:lhs_decic_transparent.png?0x300 }} +
  
-==== Learning electronic medical record (EMR) system and computerized clinical decision support ====+==== Machine learning-based clinical decision support ====
  
 {{ wiki:lemur_transparent.png?150x0}} {{ wiki:lemur_transparent.png?150x0}}
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 This work is funded by a [[https://projectreporter.nih.gov/project_info_description.cfm?aid=9030245|R01 grant from NLM]], NIH. This work is funded by a [[https://projectreporter.nih.gov/project_info_description.cfm?aid=9030245|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 [[http://www.dbmi.pitt.edu/person/gregory-cooper-md-phd|Gregory F. Cooper]], [[http://www.dbmi.pitt.edu/person/harry-hochheiser-phd|Harry Hochheiser]], [[http://people.cs.pitt.edu/~milos/|Milos Hauskrecht]], [[http://www.ccm.pitt.edu/directory/profile/gilles-clermont|Gilles Clermont]], (at the University of Pittsburgh), and [[https://sbmi.uth.edu/faculty-and-staff/dean-sittig.htm|Dean Sittig]] (at the University of Texas Health Science Center at Houston).+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 [[http://www.dbmi.pitt.edu/person/gregory-cooper-md-phd|Gregory F. Cooper]], [[http://www.dbmi.pitt.edu/person/harry-hochheiser-phd|Harry Hochheiser]], [[http://people.cs.pitt.edu/~milos/|Milos Hauskrecht]], [[http://www.ccm.pitt.edu/directory/profile/gilles-clermont|Gilles Clermont]], (at the University of Pittsburgh), and [[https://sbmi.uth.edu/faculty-and-staff/dean-sittig.htm|Dean Sittig]] (at the University of Texas Health Science Center at Houston).
  
 Publications: Publications:
  
-  * CalzoniLClermont, G, Cooper, GF, **VisweswaranS**, HochheiserH. Exploring novel graphical representations of clinical data in a learning EMR. In: AMIA Annual Symposium Proceedings. 2017 Nov 7. {{papers:2017_exploring_novel_graphical_representations_of_clinical_data_in_a_learning_emr.pdf|pdf}}+  * Hauskrecht MBatal IValko M, **Visweswaran S**, Cooper GFClermont G. {{papers:2013_outlier_detection_for_patient_monitoring_and_alerting.pdf|Outlier detection for patient monitoring and alerting}}. Journal of Biomedical Informatics. 2013 Feb; 46(1):47-55.
  
-  * KingAJ, Hochheiser, H, **VisweswaranS**, Clermont, G, Cooper, GFEye-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 27-302017:512-21. PMID: [[http://www.ncbi.nlm.nih.gov/pubmed/28815151|28815151]] PMCID: PMC5543363 {{papers:2017_eye-tracking_for_clinical_decision_support_a_method_to_capture_automatically_what_physicians_are_viewing_in_the_emr.pdf|pdf}} (//Awarded First Place in the Student Paper Competition at the AMIA Joint Summits Clinical Research Informatics2017//.)+  * King AJ, Cooper GFHochheiser 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 172015:1967-75. PMID: [[http://www.ncbi.nlm.nih.gov/pubmed/26958296|26958296]] PMCID: PMC4765593 {{papers:2015_development_and_preliminary_evaluation_of_a_prototype_of_a_learning_electronic_medical_record_system.pdf|pdf}} (//Awarded First Place in the Student Paper Competition at the AMIA Annual Symposium2015//.)
  
-  * KingAJ, Cooper, GF, HochheiserH, Clermont, G, **VisweswaranS**. Development and preliminary evaluation of a prototype of a learning electronic medical record system. In: AMIA Annual Symposium Proceedings. 2015 Nov 172015:1967-75. PMID: [[http://www.ncbi.nlm.nih.gov/pubmed/26958296|26958296]] PMCID: PMC4765593 {{papers:2015_development_and_preliminary_evaluation_of_a_prototype_of_a_learning_electronic_medical_record_system.pdf|pdf}} (//Awarded First Place in the Student Paper Competition at the AMIA Annual Symposium2015//.)+  * King AJ, Hochheiser H, **Visweswaran S**, Clermont G, Cooper GFEye-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 27-302017:512-21. PMID: [[http://www.ncbi.nlm.nih.gov/pubmed/28815151|28815151]] PMCID: PMC5543363 {{papers:2017_eye-tracking_for_clinical_decision_support_a_method_to_capture_automatically_what_physicians_are_viewing_in_the_emr.pdf|pdf}} (//Awarded First Place in the Student Paper Competition at the AMIA Joint Summits Clinical Research Informatics2017//.)
  
-  * HauskrechtMBatalI, Valko, M, **Visweswaran, S**, Cooper, GF, Clermont, G{{papers:2013_outlier_detection_for_patient_monitoring_and_alerting.pdf|Outlier detection for patient monitoring and alerting}}. Journal of Biomedical Informatics. 2013 Feb; 46(1):47-55.+  * King AJCooper GFClermont GHochheiser HHauskrecht M, Sittig DF, **Visweswaran, S**. Using machine learning to selectively highlight patient information. Journal of Biomedical Informatics. 2019 Oct 29:103327. ([[https://pubmed.ncbi.nlm.nih.gov/31676461/|abstract]])
  
-  * **VisweswaranS**, Mezger, J, Clermont, G, HauskrechtM, CooperGF{{papers:2010_identifying_deviations_from_usual_medical_care_using_a_statistical_approach.pdf|Identifying deviations from usual medical care using a statistical approach}}. In: Proceedings of the Fall Symposium of the American Medical Informatics Association2010 Nov 132010:827-31.+  * King AJCooper GF, Clermont GHochheiser H, Hauskrecht M, Sittig DF**Visweswaran, S**Leveraging eye tracking to prioritize relevant medical record dataComparative machine learning studyJournal of Medical Internet Research2020;22(4):e15876 ([[https://www.jmir.org/2020/4/e15876/|paper]])
  
-  * **Visweswaran, S**, Hanbury, P, Saul, M, Cooper, GF. {{papers:2003_detecting_adverse_drug_events_in_discharge_summaries_using_variations_on_the_simple_bayes_model.pdf|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 personalized modeling ====
- +
- +
-==== Precision medicine and patient-specific modeling ====+
  
 {{ wiki:allofus.png?200x0}} {{ wiki:allofus.png?200x0}}
 {{ wiki:allofuspa.png?200x0}} {{ wiki:allofuspa.png?200x0}}
  
-This work is funded by a [[https://projectreporter.nih.gov/project_info_description.cfm?aid=9228293|UG3 grant from the Office of the Director]], NIH.+This work is funded by a [[https://projectreporter.nih.gov/project_info_details.cfm?aid=10113038|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 [[http://www.health.pitt.edu/people/steven-e-reis|Steven E. Reis]] and [[http://www.upmc.com/media/experts/Pages/oscar-c-marroquin.aspx|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. 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 [[http://www.health.pitt.edu/people/steven-e-reis|Steven E. Reis]] and [[http://www.upmc.com/media/experts/Pages/oscar-c-marroquin.aspx|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.
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 Publications: Publications:
  
-  * **VisweswaranS**, Ferreira, A, Cooper, GF. {{papers:2015_personalized_modeling_for_prediction_with_decision-path_models.pdf|Personalized modeling for prediction with decision-path models}}. PLoS One. 2015 Jun 22;10(6):e0131022.+  * **Visweswaran S**. {{papers:2007_dissertation_learning_patient_specific_models_from_clinical_data.pdf|Learning patient-specific models from clinical data}}. Doctoral Dissertation, University of Pittsburgh, Sep 2007.
  
-  * Ferreira, A, Cooper, GF, **VisweswaranS**. {{papers:2013_decision_path_models_for_patient-specific_modeling_of_patient_outcomes.pdf|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. {{papers:2004_instance-specific_bayesian_model_averaging_for_classification.pdf|Instance-specific Bayesian model averaging for classification}}. In: Advances in Neural Information Processing Systems (NIPS 2004) (Dec 2004) 1449-56.
  
-  * **VisweswaranS**, CooperGF. {{papers:2010_learning_instance-specific_predictive_models.pdf|Learning instance-specific predictive models}}. Journal of Machine Learning Research2010 Dec11:3369-3405.+  * **Visweswaran S**, Cooper GF. {{papers:2005_patient-specific_models_for_predicting_the_outcomes_of_patients_with_community_acquired_pneumonia.pdf|Patient-specific models for predicting the outcomes of patients with community acquired pneumonia}}. In: Proceedings of the Fall Symposium of the American Medical Informatics Association20052005:759-63.
  
-  * **VisweswaranS**, AngusDC, HsiehM, WeissfeldL, YealyD, CooperGF. {{papers:2010_learning_patient-specific_predictive_models_from_clinical_data.pdf|Learning patient-specific predictive models from clinical data}}. Journal of Biomedical Informatics. 2010 Oct;43(5):669-85.+  * **Visweswaran S**, Angus DC, Hsieh M, Weissfeld L, Yealy D, Cooper GF. {{papers:2010_learning_patient-specific_predictive_models_from_clinical_data.pdf|Learning patient-specific predictive models from clinical data}}. Journal of Biomedical Informatics. 2010 Oct;43(5):669-85.
  
-  * **VisweswaranS**, CooperGF. {{papers:2005_patient-specific_models_for_predicting_the_outcomes_of_patients_with_community_acquired_pneumonia.pdf|Patient-specific models for predicting the outcomes of patients with community acquired pneumonia}}. In: Proceedings of the Fall Symposium of the American Medical Informatics Association20052005:759-63.+  * **Visweswaran S**, Cooper GF. {{papers:2010_learning_instance-specific_predictive_models.pdf|Learning instance-specific predictive models}}. Journal of Machine Learning Research2010 Dec11:3369-3405.
  
-  * **VisweswaranS**, Cooper, GF. {{papers:2004_instance-specific_bayesian_model_averaging_for_classification.pdf|Instance-specific Bayesian model averaging for classification}}. In: Advances in Neural Information Processing Systems (NIPS 2004(Dec 2004) 1449-56.+  * **Visweswaran S**, Ferreira ACooper GF. {{papers:2015_personalized_modeling_for_prediction_with_decision-path_models.pdf|Personalized modeling for prediction with decision-path models}}. PLoS One. 2015 Jun 22;10(6):e0131022.
  
-  * **VisweswaranS**. {{papers:2007_dissertation_learning_patient_specific_models_from_clinical_data.pdf|Learning patient-specific models from clinical data}}. Doctoral Dissertation, University of Pittsburgh, Sep 2007.+  * Jabbari F, **Visweswaran S**, Cooper GF. {{papers:2018_instance-specific_bayesian_network_structure_learning.pdf|Instance-specific Bayesian network structure learning}}. In: The 9th International Conference on Probabilistic Graphical Models. 2018 Sep 11 – 14.
  
 +==== Causal discovery from biomedical data ====
  
-==== Data mining and 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 [[http://www.dbmi.pitt.edu/person/gregory-cooper-md-phd|Gregory F. Cooper]] and [[https://profiles.dom.pitt.edu/faculty_info.aspx/Whitcomb5013|David C. Whitcomb]] (at the University of Pittsburgh). 
  
-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 [[http://www.dbmi.pitt.edu/person/gregory-cooper-md-phd|Gregory F. Cooper]] and [[https://www.dept-med.pitt.edu/gi/faculty_info.aspx/Whitcomb5013/|David CWhitcomb]] (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 [[http://www.dbmi.pitt.edu/person/gregory-cooper-md-phd|Gregory F. Cooper]] and [[https://github.com/ericstrobl/|Eric VStrobl]] and is funded by a [[https://projectreporter.nih.gov/project_info_description.cfm?aid=8935874|U54 grant from NHGRI]], NIH.
  
-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 [[http://www.dbmi.pitt.edu/person/gregory-cooper-md-phd|Gregory F. Cooper]] and [[http://www.mdphd.pitt.edu/students/eric-strobl|Eric Strobl]] and is funded by a [[https://projectreporter.nih.gov/project_info_description.cfm?aid=8935874|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 [[https://coehp.uark.edu/directories/leadership-directory/uid/bprimack/name/Brian+Primack/|Brian Primack]] (at the University of Arkansas) and is funded by a [[https://projectreporter.nih.gov/project_info_description.cfm?aid=9656981|R01 grant from NCI]], 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 [[https://www.dom.pitt.edu/dgim/faculty_info.aspx/Primack4945|Brian Primack]] and is funded by a [[https://projectreporter.nih.gov/project_info_details.cfm?aid=950346|R01 grant from NCI]], NIH.+
  
 The areas of focus include: The areas of focus include:
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 Publications: Publications:
  
-  * Strobl, EV, **VisweswaranS**. {{papers:2016_markov_boundary_discovery_with_ridge_regularized_linear_models.pdf|Markov boundary discovery with ridge regularized linear models}}. Journal of Causal Inference2016 Mar4(1):31-48.+  * Stokes ME, **Visweswaran S**. {{papers:2012_application_of_a_spatially-weighted_relief_algorithm_for_ranking_genetic_predictors_of_disease.pdf|Application of a spatially-weighed Relief algorithm for ranking genetic predictors of disease}}. BioData Mining2012 Dec 35(1):20.
  
-  * Aflakparast, M, Masoudi-Nejad, A, Bozorgmehr, JH, **VisweswaranS**. {{papers:2014_informative_bayesian_model_selection_a_method_for_identifying_interactions_in_genome-wide_data.pdf|Informative Bayesian Model Selection: A method for identifying interactions in genome-wide data}}. Molecular BioSystems2014 Aug 2610(10):2654-62.+  * Strobl EV, **Visweswaran S**. {{papers:2013_deep_multiple_kernel_learning.pdf|Deep multiple kernel learning}}. In: Proceedings of the 12th International Conference on Machine Learning and Applications (ICMLA'13)2013 Dec 42013:414-17.
  
-  * Aflakparast, M, Salimi, H, Gerami, A, Dubé, M-P, **VisweswaranS**, Masoudi-Nejad, A. {{papers:2014_cuckoo_search_epistasis_a_new_method_for_exploring_significant_genetic_interactions.pdf|Cuckoo search epistasis: A new method for exploring significant genetic interactions}}. Heredity2014 Jun; 112(6):666-74.+  * Strobl EV, **Visweswaran S**. {{papers:2013_markov_blanket_ranking_using_kernel-based_conditional_dependence_measures.pdf|Markov blanket discovery using kernel-based conditional dependence measures}}. In: Proceedings of the NIPS 2013 Workshop on Causality, Lake Tahoe, NV. (Dec 2013).
  
-  * StokesME, BarmadaMM, KambohMI, **VisweswaranS**. {{papers:2014_the_application_of_network_label_propagation_to_rank_biomarkers_in_genome-wide_alzheimers_data.pdf|The application of network label propagation to rank biomarkers in genome-wide Alzheimer's data}}. BMC Genomics. 2014 Apr 14;15(1):282.+  * Stokes ME, Barmada MM, Kamboh MI, **Visweswaran S**. {{papers:2014_the_application_of_network_label_propagation_to_rank_biomarkers_in_genome-wide_alzheimers_data.pdf|The application of network label propagation to rank biomarkers in genome-wide Alzheimer's data}}. BMC Genomics. 2014 Apr 14;15(1):282.
  
-  * StroblEV, **VisweswaranS**. {{papers:2013_markov_blanket_ranking_using_kernel-based_conditional_dependence_measures.pdf|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**. [[https://arxiv.org/abs/1702.03877|Approximate kernel-based conditional independence tests for fast non-parametric causal discovery]]Journal of Causal Inference2019 Mar; 4(1):31-48.
  
-  * StroblEV, **VisweswaranS**. {{papers:2013_deep_multiple_kernel_learning.pdf|Deep multiple kernel learning}}. InProceedings of the 12th International Conference on Machine Learning and Applications (ICMLA'13)2013 Dec 4; 2013:414-17. +  * Strobl EV, **Visweswaran S**. [[https://arxiv.org/abs/1905.10330|Dirac delta regressionConditional density estimation with clinical trials]]arXiv preprint arXiv:1905.103302019.
-   +
-  * StokesME, **Visweswaran, S**. {{papers:2012_application_of_a_spatially-weighted_relief_algorithm_for_ranking_genetic_predictors_of_disease.pdf|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**. {{papers:2012_predicting_the_risk_of_psychosis_onset_advances_and_prospects.pdf|Predicting the risk of psychosis onset: Advances and prospects. Early Intervention in Psychiatry}}. 2012 Nov;6(4):368-79.+==== Research data warehousing ====
  
-  * Wei W, **Visweswaran, S**, Cooper, GF. {{papers:2011_the_application_of_naive_bayes_model_averaging_to_predict_alzheimers_disease_from_genome-wide_data.pdf|The application of naive Bayes model averaging to predict Alzheimer's disease from genome-wide data}}Journal of the American Medical Informatics Association2011 Jul-Aug;18(4):370-5.+This work is funded by a [[https://projectreporter.nih.gov/project_info_description.cfm?aid=9260460|UL1 grant from NCATS]], NIH and a [[https://www.pcori.org/research-results/2015/path-towards-learning-health-system-path/|CDRN grant from PCORI]].
  
-  * Jiang, X, Neapolitan, RE, Barmada, MM, **Visweswaran, S**. {{papers:2011_learning_genetic_epistasis_using_bayesian_network_scoring_criteria.pdf|Learning genetic epistasis using Bayesian network scoring criteria}}. BMC Bioinformatics. 2011 Mar 31;12:89. +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 [[http://www.health.pitt.edu/people/steven-e-reis|Steven E. Reis]], [[http://www.dbmi.pitt.edu/person/michael-j-becich-md-phd|Michael J. Becich]], and [[https://www.dbmi.pitt.edu/node/53786|Jonathan C. Silverstein]] (at the University of Pittsburgh).
- +
-  * Jiang, X, Barmada, MM, **Visweswaran, S**. {{papers:2010_identifying_genetic_interactions_in_genome-wide_data_using_bayesian_networks.pdf|Identifying genetic interactions in genome-wide data using Bayesian networks}}. Genetic Epidemiology. 2010 Sep; 34(6):575-81. +
- +
- +
-==== Reuse of EMR data and research data warehousing ==== +
- +
-This work is funded by a [[https://projectreporter.nih.gov/project_info_description.cfm?aid=9260460|UL1 grant from NCATS]], NIH and a [[http://www.pcornet.org/clinical-data-research-networks/cdrn11-university-of-pittsburgh/|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 [[http://www.health.pitt.edu/people/steven-e-reis|Steven E. Reis]], [[http://www.dbmi.pitt.edu/person/michael-j-becich-md-phd|Michael J. Becich]], and [[http://www.dbmi.pitt.edu/person/melissa-saul-ms-1|Melissa Saul]] (at the University of Pittsburgh).+
  
 Publications: Publications:
  
-  * AminW, BorromeoC, SaulM, BecichMJ, **VisweswaranS**. {{papers:2015_informatics_synergies_between_path_and_act_networks.pdf|Informatics synergies between PaTH and ACT networks}}. In: Proceedings of the 2015 Summit on Clinical Research Informatics (Mar 2015)+  * Amin W, Borromeo C, Saul M, Becich MJ, **Visweswaran S**. {{papers:2015_informatics_synergies_between_path_and_act_networks.pdf|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 [[http://www.skbhavnani.com/DIVA/|Suresh K Bhavnani]] (at the University of Texas Medical Branch at Galveston), [[http://www.tcsuh.com/people/faculty/bassler_kevin/|Kevin Bassler]] (University of Houston, Central), and [[http://www.mayoclinic.org/biographies/divekar-rohit-d-m-b-b-s-ph-d/bio-20055738|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, Bellala, G. {{papers:2015_where_is_the_science_in_big_data_visual_analytics_from_pretty_pictures_to_transformative_biomedical_discoveries.pdf|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. {{papers:2015_how_comorbidities_co-occur_in_readmitted_hip_fracture_patients_from_bipartite_networks_to_insights_for_post-discharge_planning.pdf|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. +
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-  * Bhavnani, SK, Dang, B, Bellala, G, Divekar, R, **Visweswaran, S**, Brasier, A, Kurosky, A. {{papers:2015_unlocking_proteomic_heterogeneity_in_complex_diseases_through_visual_analytics.pdf|Unlocking proteomic heterogeneity in complex diseases through visual analytics}}. Proteomics. 2015 Feb 13; 15(8):1405-18.  +
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-  * Bhavnani, SK, Dang, B, Caro, M, Bellala, G, **Visweswaran, S**, Asuncion, M, Divekar, R. {{papers:2014_heterogeneity_within_and_across_pediatric_pulmonary_infections_from_bipartite_networks_to_at-risk_subphenotypes.pdf|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. +
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-  * Bhavnani, SK, Drake, J, Bellala, G, Dang, B, Peng, B, Oteo, JA, Santibañez-Saenz, P, **Visweswaran, S**, Olano, JP. {{papers:2013_how_cytokines_co-occur_across_rickettsioses_patients_from_bipartite_visual_analytics_to_mechanistic_inferences_of_a_cytokine_storm.pdf|How cytokines co-occur across rickettsioses patients: From bipartite visual analytics to mechanistic inferences of a cytokine storm}}. In: AMIA Joint Summits Translational Science Proceedings. 2013 Mar 18; 2013:15-9.+
  
-  * Bhavnani, SK, Bassler, K, **VisweswaranS**. [[http://www.google.com/patents/US20130245959|Computer-Implementable Algorithm for Biomarker Discovery Using Bipartite Networks]]. US Patent Application.+  * **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. [[https://academic.oup.com/jamiaopen/article/1/2/147/5077449|Accrual to Clinical Trials (ACT): A Clinical and Translational Science Award Consortium network]]. JAMIA Open. 2018 Aug 21.
  
-  * Bhavnani, SK, Bellala, G, Victor, S, Bassler, K, **Visweswaran, S**. {{papers:2012_the_role_of_complementary_bipartite_visual_analytical_representations_in_the_analysis_of_snps.pdf|The role of complementary bipartite visual analytical representations in the analysis of SNPs: A case study in ancestral informative markers}}. Journal of the American Medical Informatics Association. 2012 Jun 1; 19(e1):e5-e12.