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research [2019/09/05 12:05]
shyam [Causal discovery from biomedical data] 		research [2019/11/23 19:47]
shyam
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 ===== 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 the application of artificial intelligence and machine learning to computerized clinical decision support, precision medicine and personalized modeling, causal discovery from genomic and biomedical data, and clinical data warehousing and data harmonization.
  
-  * 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. +==== Machine learning for computerized clinical decision support ====
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-{{ wiki:lhs_decic_transparent.png?0x300 }} +
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-==== Learning electronic medical record (EMR) system and computerized 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:
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   * **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.   * **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.
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   * **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.   * **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.
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-==== EMR data warehousing ====+==== Research data warehousing and data harmonization ====
  
 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]]. 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]].