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Computational Health Distinguished Lecture Series

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https://hdl.handle.net/1853/70860
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Now showing 1 - 3 of 3
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    Physics of the Medical Record
    ( 2017-03-16) Hripcsak, George
    Dr. George Hripcsak will discuss the national push for electronic health records that will make an unprecedented amount of clinical information available for research, with approximately one billion patient visits documented per year in the US. These data may lead to discoveries that improve understanding of biology, aid the diagnosis and treatment of disease, and permit the inclusion of diverse populations and rare diseases. Health record data also bring challenges, with missing and inaccurate data and substantial bias. Some of the bias comes about because the data are generated as part of the health care process. For example blood tests done at night select for patients who are sicker. We employ nonlinear time series methods borrowed from other fields to address the challenges, and we attempt to model and correct for biases due to the health care process. We incorporate mechanistic models to constrain the search space to create accurate predictions despite limited training sets and missing values.
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    Inferring Host-Pathogen Interactions from Diverse Data Sources
    ( 2017-03-02) Craven, Mark
    Dr. Craven discusses work in several studies that involve developing and applying predictive methods in order to characterize host-pathogen interactions. In the first study, we are focused on inferring host subnetworks that are involved in viral replication from genome-wide loss-of-function experiments. Although these experiments can identify the host factors that directly or indirectly facilitate or inhibit the replication of a virus in a host cell, they do not elucidate how these genes are organized into the biological pathways that mediate host-virus interactions. We are developing novel computational methods that use a wide array of secondary data sources, including the scientific literature, to transform the measurements from these assays into hypotheses that predict the pathways in the cell that relate implicated genes to viral replication. In the second study, we are applying machine-learning methods to understand how variation in the genome of the HSV-1 virus influences multiple ocular disease phenotypes in a host. In the third study, we are investigating the extent to which risk for various infectious disease phenotypes can be predicted from electronic health records by using machine-learning methods.
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    Use Cases of Big Data for Pharma
    ( 2017-02-16) Dilley, Cynthia