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research [2018/06/02 08:31]
shyam [Precision medicine and personalized modeling] 		research [2019/07/21 10:48]
shyam
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   * Learning electronic medical record (EMR) system and computerized clinical decision support   * Learning electronic medical record (EMR) system and computerized clinical decision support
   * Precision medicine and personalized modeling   * Precision medicine and personalized modeling
-  * Data mining and causal discovery from biomedical data +  * Causal discovery from biomedical data 
-  * Reuse of EMR data and research data warehousing+  * EMR data warehousing
   * Automated visual analytics   * Automated visual analytics
  
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 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 [[http://www.dbmi.pitt.edu/person/gregory-cooper-md-phd|Gregory F. Cooper]] (at the University of Pittsburgh). The areas of focus include: 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 [[http://www.dbmi.pitt.edu/person/gregory-cooper-md-phd|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+  * 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.   * application of personalized modeling to risk assessment, diagnosis, prognosis and selection of therapy.
  
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-==== Data mining and causal discovery from biomedical data ====+==== 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://www.dept-med.pitt.edu/gi/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 C. Whitcomb]] (at the University of Pittsburgh). 
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 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. 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 media1 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.+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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-==== Reuse of EMR data and research data warehousing ====+==== EMR 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]]. 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]].