May 10: Adam Resnick, Children’s Hospital of Philadelphia
May 24: Brad Erickson and Eliot Siegel
June 7: Vivek Navale, National Institutes of Health
June 21: Aviv Regev, MIT, Broad Institute
July 5: Paul Fearn, NCI Surveillance Informatics Branch
BD2K Aztec is a global biomedical resource discovery index that allows users to simultaneously search a diverse array of tools. The resources indexed include web services, standalone software, publications, and large libraries composed of many interrelated functions. Aztec will ensure that software tools remain findable in the long term by issuing persistent DOIs and routinely updating metadata for the entire index. Aztec’s established ontologies and robust API support the programmatic query of its entire database, as well as the construction of indexes for specialized subdomains. Aztec is currently in its alpha-release phase (version 1.1), in which it is being evaluated and tested by internal users at UCLA, as well as invited external users at Sage Bionetworks, TSRI, and EMBL-EBI. Their feedback and comments have been documented and incorporated into Aztec's next release.
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This talk will introduce the Common Workflow Language (CWL) project. In July 2016 the CWL team released standards that enable the portable, interoperable, and executable description of command line data analysis tools and workflow made from those tools. These descriptions are enhanced by CWL's first class (but optional) support for Docker containers. The state of CWL adoption and examples of bioinformatic collaborations across many continents using CWL will be reviewed. Attendees who want to play with CWL prior to attending the presentation are invited to go through the "Gentle Introduction to the Common Workflow Language" tutorial on any OS X or Linux machine on their own time: http://www.commonwl.org/v1.0/UserGuide.html
Prostate cancer is the most common malignancy in men and newly diagnosed men face complex treatment choices, each with different risks of acquired morbidities, including patient-centered outcomes (PCOs). Current government initiatives highlight the need to incorporate PCOs into healthcare quality metric evaluations and the widespread implementation of electronic health records (EHRs) provides opportunities to do so. However, efforts to assess quality metrics in EHRs have been limited because most relevant data are not reliably captured in structured formats. Instead they are buried as non-structured, free text recorded by clinicians. Leveraging the power of computational resources for processing the vast amount of medical information residing in EHRs, we achieve automation and precision in the evaluation of both process and outcome quality metrics, including metrics focused on PCOs.
To develop our approach, we first built a patient cohort using ICD-9/10 diagnosis codes to identify prostate cancer patients. Patients are confirmed in the California Cancer Registry, which returns tumor characteristics and treatment data on all patients with a confirmed cancer diagnosis, including complete historical record of disease pathology. Next we create novel ontological representations of quality metrics, many that are non-prostate specific. Each quality metric determines the target terms and concepts to extract from the EHRs. These terms may include diagnostic procedures and tests and their results, therapeutic procedures, and drugs. Terms are mapped to a standardized medical vocabulary (e.g., SNOMED or RxNorm), enabling us to represent the elements of a metric by a concept domain and its permissible values. The structured representation of the quality metric terms are used to create quality phenotypes, which are rules involving the temporal order of components of the quality metrics. Finally, we use data mining algorithms, including Natural Language Processing (NLP) technologies to parse the clinical narrative text and extract pertinent structured information. While we test our methodology in prostate cancer patients, these approaches are applicable to all cancer patients and are the basis of a learning healthcare system. This presentation will demonstrate the feasibility of using our methods to increase the usability of existing EHRs and enhance the efficiency and accuracy of quality measurement in cancer patients, including PCO measurements.
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