Differences

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--- projects [2018/05/14 22:25]
shyam [Accrual of patients to Clinical Trials (ACT) network]
+++ projects [2020/07/03 11:02]
shyam [Biomedical Informatics Core, CTSI]
@@ Line 3: / Line 3: @@
 ===== 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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-==== 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.