Differences

This shows you the differences between two versions of the page.

--- projects [2018/05/14 22:25]
shyam [Accrual of patients to Clinical Trials (ACT) network]
+++ projects [2020/07/03 11:16]
shyam [PaTH Clinical Data Research Network]
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 ===== Projects =====
-==== Biomedical Informatics Core, CTSI ====
+==== Informatics Core, CTSI ====
 {{ wiki:ctsi.png?300x0}}
-The Biomedical Informatics Core of the [[http://www.ctsi.pitt.edu/|Clinical and Translational Science Institute (CTSI)]] will establish a research data warehouse; develop and deploy user-friendly, web-based informatics tools such as a Cohort Discovery Tool, a Computable Phenotype Library, and a Data Transfer Tool; and develop and deploy a secure data analytic environment.
+The Informatics Core of the [[http://www.ctsi.pitt.edu/|Clinical and Translational Science Institute (CTSI)]] has established a research data warehouse; developed and deployed user-friendly, web-based informatics tools such as a Cohort Discovery Tool and a Computable Phenotype Library Tool; and is developing a secure data analytic environment.
 This work is funded by grant [[https://projectreporter.nih.gov/project_info_description.cfm?aid=9260460|UL1 TR001857]] from NCATS, NIH.
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-The University of Pittsburgh is [[https://projectreporter.nih.gov/project_info_details.cfm?aid=9228293|funded]] as one of the [[https://www.nih.gov/precision-medicine-initiative-cohort-program/healthcare-provider-organizations|Healthcare Provider Organizations]] for the national [[https://allofus.nih.gov/|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. The University of Pittsburgh's program, called the [[https://pacaresforusresearch.org/|All of Us Pennsylvania Research Program]], will enroll 120,000 participants.
+The University of Pittsburgh is [[https://projectreporter.nih.gov/project_info_details.cfm?aid=9228293|funded]] as one of the [[https://www.nih.gov/precision-medicine-initiative-cohort-program/healthcare-provider-organizations|Healthcare Provider Organizations]] for the national [[https://allofus.nih.gov/|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. The University of Pittsburgh's program, called the [[https://pacaresforusresearch.org/|All of Us Pennsylvania]] Research Program, will enroll 120,000 participants.
 This work is funded by grant [[https://projectreporter.nih.gov/project_info_description.cfm?aid=9228293|UG3 OD023153]] from the Office of the Director, NIH.
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-[[http://www.pathnetwork.org/|PaTH]] is a Patient Centered Outcomes Research Institute (PCORI) Clinical Data Research Network project which is focused on building a Learning Health System (LHS) for the Mid-Atlantic region. It is comprised of Geisinger Health System, Johns Hopkins University, Johns Hopkins Health System, Penn State College of Medicine, Penn State Milton S. Hershey Medical Center, Temple Health System, Lewis Katz School of Medicine at Temple University, the University of Pittsburgh, UPMC, the University of Utah, and University of Utah Health Care. the University of Pittsburgh leads the informatics component of PaTH.
+[[http://www.pathnetwork.org/|PaTH]] is a Patient Centered Outcomes Research Institute (PCORI) Clinical Data Research Network project which is focused on building a Learning Health System (LHS) for the Mid-Atlantic region. It is comprised of University of Pittsburgh / UPMC, Penn State, Temple University, John Hopkins University, the Ohio State University and University of Michigan. The University of Pittsburgh leads the informatics component of PaTH.
 This work is funded by grant [[http://www.pcori.org/research-results/2015/path-towards-learning-health-system-path|CDRN 1306-04912]] from PCORI.
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-==== Learning Electronic Medical Record (LEMR) system ====
+==== Genomics Research and Innovation Network (GRIN) ====
-{{ wiki:lemur_transparent.png?150x0}}
+Genomics Research and Innovation Network (GRIN) is a federated network that is developing two portals for researchers: (A) Prep-to- research portal. Investigators can execute genotype, phenotype, or combined genotype/phenotype queries, and receive aggregate results in real time; and (B) Study portal. With proper approvals, patient-level data are readily transferred to a cloud-hosted environment.
-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 funded by a [[https://projectreporter.nih.gov/project_info_description.cfm?aid=9818382|U01 grant]] from the NCATS, NIH.
-This work is funded by a [[https://projectreporter.nih.gov/project_info_description.cfm?aid=9030245|R01 grant]] from the NLM, NIH.
-==== Personalized modeling for precision medicine ====
-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.