Current

Center for Causal Modeling and Discovery of Biomedical Knowledge from Big Data (CCD) - Phase II

NIH BD2K Admin Supplement 3U54HG008540-03S1

Co-investigator (PI: Cooper Greg, University of Pittsburgh)

The Center for Causal Modeling and Discovery of Biomedical Knowledge from Big Data (CCD) and the Patient-centered Information Commons (PIC-SURE) will collaborate to develop a system that shares data, and computational accessibility in an externally hosted environment (i.e., cloud). We will start by developing a standardized RESTful Application-Programming-Interface (API) that will enable our organizations to share data and the analysis results. This will serve as...

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i2b2/tranSMART pilot study on the cloud using Redshift - Phase II

Amazon Web Services Research

Grant EDU_R_FY2016

PI $55K

To handle increasing data volumes and potential new sources of data, and to promote patient engagement, we intend to conduct a pilot study that involves hosting this infrastructure on a paradigm which provides high storage, computing power and availability, AWS.

Our project plan comprises 2 phases:

- Phase 1 will involve transforming the open access NDAR (National Database for Autism Research) database to the i2b2/tranSMART model. The transformed NDAR and the existing Oracle...

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NIH/NCATS Global Rare Diseases Patient Registry i2b2/tranSMART Data Repository

NIH/NCATS

Co-PI  Total $2.4M  (PI: Isaac Kohane)

The integration of clinical and biomedical data hosted in multiple distributed repositories is confonted by two significant challenges: 1) correctly linking information pertaining to the same patient across repositories, for example, linking lab results data with bedside observations data; and 2) making data available for analysis at different locations across a collaboration network. These problems are exacerbated in the case of rare diseases research, given the very limited availability of data sets and...

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BCH Precision Link - Genomics Research and Innovation Network (GRIN)

Boston Children's Hospital (BCH), Cincinnati Children's Hospital Medical Center (CCHMC), Children's Hospital of Philadelphia (CHOP)

Co-Investigator BCH  Total: $6M  PI BCH: Ken Mandl

To accelerate genomic discovery, collaboration, and improved clinical outcomes, research leaders at Boston Children's Hospital (BCH), Cincinnati Children's Hospital Medical Center (CCHMC), and Children's Hospital of Philadelphia (CHOP) have come together to propose the creation of the Genomics Research and Innovation Network (GRIN). 

Four major potential uses...

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Scalable Collaborative Infrastructure for a Learning Healthcare System (SCILHS) - Phase II

PCOR - CDRN

Co-Investigator (PI: Ken Mandl)

Leveraging an informatics infrastructure that our investigators have developed over the past 15 years, the Department of Biomedical Informatics (DBMI) at Harvard Medical School will work across 10 healthcare centers throughout the U.S. to develop the Scalable Collaborative Infrastructure for a Learning Health System (SCILHS, pronounced "skills"). An open-source platform, the system will cover more than 8 million patients and enable clinician and patient participation in research. 

Phelan-McDermid Syndrome Data Network (PMS_DN) - Phase II

PCORI - PPRN

Co-PI ($1.2 million) Total 1.6K (PI: Megan O'Boyle)

To collect all available patient data from Phelan-McDermid Syndrome (PMS) patients to make meaningful, well-annotated clinical data available to researchers and to share insights with members of the PCORI network.

TranSMART platform based on i2b2 is being used to integrate Patient Reported Outcomes and Knowledge extracted from Clinical Notes using cTAKES

Neuropsychiatric Genome-Scale and RDoC Individualized Domains (N-GRID)

NIH P50 MH106933

Co-Investigator (PI: Isaac Kohane)

As a result of the accelerated pace of development of technologies for characterizing the human genome, the rate-limiting step for large scale genomic investigation in clinical populations is now phenotyping. This is particularly the case for neuropsychiatric (NP) illness, where phenotypes are complex, biomarkers are lacking, and the primary cell types of interest are difficult to access directly. It has become apparent that both rare and common genetic variation contributes to disease risk and that this risk...

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Patient-Centered Information Commons

NIH/BD2K U54 HG007963

Co-Investigator (PI: Isaac Kohane)

We propose to create a massively scalable toolkit to enable large, multi-center Patient-centered Informatic Commons (PIC) at loval, regional, and national scale, where the focus is the alignment of all available biomedical data per individual. Such a Commons is a prerequisite for conducting the large-N, Big Data, longitudinal studies essential for understanding causation in the Precision Medicine framework while simultaneously addressing key complexities of Patient Centric Outcome Research studies required...

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