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McDuffie, Michael T.

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McDuffie

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Michael T.

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McDuffie, Michael T.
Author's Publications: search.filters.isAuthorOfPublication.0f237a22-9ad0-401a-8993-64c219193da5

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    A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey
    (Nature Publishing Group, 2016) Patel, Chirag; Pho, Nam; McDuffie, Michael T.; Easton-Marks, Jeremy; Kothari, Cartik; Kohane, Isaac; Avillach, Paul
    The National Health and Nutrition Examination Survey (NHANES) is a population survey implemented by the Centers for Disease Control and Prevention (CDC) to monitor the health of the United States whose data is publicly available in hundreds of files. This Data Descriptor describes a single unified and universally accessible data file, merging across 255 separate files and stitching data across 4 surveys, encompassing 41,474 individuals and 1,191 variables. The variables consist of phenotype and environmental exposure information on each individual, specifically (1) demographic information, physical exam results (e.g., height, body mass index), laboratory results (e.g., cholesterol, glucose, and environmental exposures), and (4) questionnaire items. Second, the data descriptor describes a dictionary to enable analysts find variables by category and human-readable description. The datasets are available on DataDryad and a hands-on analytics tutorial is available on GitHub. Through a new big data platform, BD2K Patient Centered Information Commons (http://pic-sure.org), we provide a new way to browse the dataset via a web browser (https://nhanes.hms.harvard.edu) and provide application programming interface for programmatic access.
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    Combining clinical and genomics queries using i2b2 – Three methods
    (Public Library of Science, 2017) Murphy, Shawn; Avillach, Paul; Bellazzi, Riccardo; Phillips, Lori; Gabetta, Matteo; Eran, Ally; McDuffie, Michael T.; Kohane, Isaac
    We are fortunate to be living in an era of twin biomedical data surges: a burgeoning representation of human phenotypes in the medical records of our healthcare systems, and high-throughput sequencing making rapid technological advances. The difficulty representing genomic data and its annotations has almost by itself led to the recognition of a biomedical “Big Data” challenge, and the complexity of healthcare data only compounds the problem to the point that coherent representation of both systems on the same platform seems insuperably difficult. We investigated the capability for complex, integrative genomic and clinical queries to be supported in the Informatics for Integrating Biology and the Bedside (i2b2) translational software package. Three different data integration approaches were developed: The first is based on Sequence Ontology, the second is based on the tranSMART engine, and the third on CouchDB. These novel methods for representing and querying complex genomic and clinical data on the i2b2 platform are available today for advancing precision medicine.