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start [2018/02/15 09:54]
127.0.0.1 external edit
start [2018/05/02 17:58]
shyam [News]
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 ~~NOTOC~~ ~~NOTOC~~
  
-===== Data Enabled Clinical Informatics Center (DECIC) =====+===== Center for Clinical Research Informatics (CCRI) =====
  
 {{ wiki:pitt3.png?400x0}} {{ wiki:pitt3.png?400x0}}
  
-The Data Enabled Clinical Informatics Center (DECIC) is in the Department of Biomedical Informatics at the University of Pittsburgh. DECIC is directed by [[http://www.thevislab.com|Shyam Visweswaran, MD, PhD]]. The mission of DECIC is to do **innovative research and healthcare delivery that is enabled by clinical, mobile health, molecular and research data**. The DECIC is focused on:+The Center for Clinical Research Informatics (CCRI) is in the Department of Biomedical Informatics at the University of Pittsburgh. CCRI is directed by [[http://www.thevislab.com|Shyam Visweswaran, MD, PhD]]. The mission of CCRI is to do **innovative research and healthcare delivery that is enabled by clinical, mobile health, molecular and research data**. The CCRI is focused on:
  
   * developing a research data warehouse called [[neptune|Neptune]]   * developing a research data warehouse called [[neptune|Neptune]]
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 {{ wiki:lhs_decic_transparent.png?0x300 }} {{ wiki:lhs_decic_transparent.png?0x300 }}
  
-The DECIC is the home of the Biomedical Informatics Core of the University of Pittsburgh [[http://www.ctsi.pitt.edu/|Clinical and Translational Science Institute]]. The DECIC is an integral component of several national research networks that include: 1) the [[http://act-network.org/|Accrual to Clinical Trials (ACT) network]] that is funded by the National Clinical and Translational Science Award (NCATS), 2) the [[http://pathnetwork.org/|PaTH network]] (University of Pittsburgh, Penn State, Temple University and John Hopkins University) that is funded by the Patient Centered Outcomes Research Institute (PCORI), 3) the [[http://mesotissue.org/|National Mesothelioma Virtual Bank (NMVB)]] is a virtual biospecimen registry designed to support and facilitate research that in mesothelioma, and 4) the [[https://allofus.nih.gov/|All of Us Research Program]] that is a landmark longitudinal research effort that aims to engage 1 million or more U.S. participants to revolutionize how disease is prevented and treated based on individual differences in lifestyle, environment and genetics.+The CCRI is the home of the Biomedical Informatics Core of the University of Pittsburgh [[http://www.ctsi.pitt.edu/|Clinical and Translational Science Institute]]. The CCRI is an integral component of several national research networks that include: 1) the [[http://act-network.org/|Accrual to Clinical Trials (ACT) network]] that is funded by the National Clinical and Translational Science Award (NCATS), 2) the [[http://pathnetwork.org/|PaTH network]] (University of Pittsburgh, Penn State, Temple University and John Hopkins University) that is funded by the Patient Centered Outcomes Research Institute (PCORI), 3) the [[http://mesotissue.org/|National Mesothelioma Virtual Bank (NMVB)]] is a virtual biospecimen registry designed to support and facilitate research that in mesothelioma, and 4) the [[https://allofus.nih.gov/|All of Us Research Program]] that is a landmark longitudinal research effort that aims to engage 1 million or more U.S. participants to revolutionize how disease is prevented and treated based on individual differences in lifestyle, environment and genetics.
  
  
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 {{ wiki:pacaresforus.png?300x0}} {{ wiki:pacaresforus.png?300x0}}
  
-**2016 July** 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 program, called the [[https://pacaresforusresearch.org/|All of Us Pennsylvania Research Program]], will enroll 150,000 participants. See the {{wiki:university_times_pmi_highlighted.pdf|University Times}} and the  [[http://www.upmc.com/media/NewsReleases/2016/Pages/pitt-pmi.aspx|press release by the University of Pittsburgh]].+**2016 July** 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 program, called the [[https://pacaresforusresearch.org/|All of Us Pennsylvania Research Program]], will enroll 120,000 participants. See the {{wiki:university_times_pmi_highlighted.pdf|University Times}} and the  [[http://www.upmc.com/media/NewsReleases/2016/Pages/pitt-pmi.aspx|press release by the University of Pittsburgh]].
  
 {{ wiki:lemur_transparent.png?150x0}} {{ wiki:lemur_transparent.png?150x0}}
  
 **2015 September** NLM R01 funded for [[https://projectreporter.nih.gov/project_info_details.cfm?aid=9030245|Development and evaluation of a learning electronic medical record system]]. Electronic medical records (EMRs) are capturing increasing amounts of patient data that can be leveraged by machine learning methods for computerized clinical decision support. This project focuses on developing a learning EMR system that uses machine learning to provide decision support using the right data, at the right time. **2015 September** NLM R01 funded for [[https://projectreporter.nih.gov/project_info_details.cfm?aid=9030245|Development and evaluation of a learning electronic medical record system]]. Electronic medical records (EMRs) are capturing increasing amounts of patient data that can be leveraged by machine learning methods for computerized clinical decision support. This project focuses on developing a learning EMR system that uses machine learning to provide decision support using the right data, at the right time.