Knowledge Discovery in Clinical and Biomedical Data: Case Studies in Pediatrics and Mental Health

With the widespread adoption of electronic health records and increasing discoveries reported in biomedical literature, computational approaches are needed for further knowledge discovery and hypothesis generation. Challenges include the capture of key information within text and standardization issues, requiring use of natural language processing and data integration techniques. Clinical data mining and biomedical literature mining have been used in a range of contexts to discover disease knowledge such as comorbidities and patterns related to social, behavioral, and familial (SBF) factors. In this lecture, a series of case studies will be presented on representing, extracting, integrating, mining, and visualizing SBF factors and comorbidities for pediatric and mental health conditions. Collectively, these studies demonstrate use of systematic processes and development of open-source tools for transforming clinical and biomedical data into knowledge. Elizabeth Chen is the Founding Associate Director of the Brown Center for Biomedical Informatics (BCBI), Associate Professor of Medical Science, and Associate Professor of Health Services, Policy& Practice at Brown University. She received a BS in Computer Science from Tufts University and PhD in Biomedical Informatics from Columbia University. Within BCBI, Dr. Chen leads the Clinical Informatics Innovation and Implementation (CI3) Laboratory that is focused on leveraging EHR technology and data to improve healthcare delivery an...
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