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.
Significant research efforts and resources are being directed towards the development of methodologies and data to support the Precision Medicine and the Cancer Moonshot Initiatives and to utilize these as the basis for translational medicine. Most of these focus on the development and implementation of technology that integrates genomics into clinical practice and/or the development of new diagnostics and therapeutics. The success of these efforts may be hindered by a lack of appreciation of the complexities present in real world clinical practice, e.g., quality and adherence to clinical guidelines, and real world patients, e.g., co-morbidities and poly-pharmacy. We have been developing and implementing system-based modeling approaches to facilitate the evaluation of these critical factors to enhance the goal of delivering the right care to the right patient.
The approach that we have developed, in collaboration with the Epidemiology and Health Research Department at the National Research Council of Italy (CNR-Pisa) involves the development of a Disease Process Model and its instantiation as an ontology implemented in a web-based platform. This disease-agnostic model has been successfully applied in drug and diagnostic development, clinical trial design (and evaluation), risk evaluation and clinical decision support applications. An additional critical component of this modeling approach incorporates the patient’s underlying physiological development and the reality that risk, particularly to lifestyle and environmental factors, will vary throughout a patient’s lifetime and stage of development. This presentation will address the gap between unmet clinical need and unstated, unmet clinical and provide examples from our work in breast cancer, pediatric Acute Respiratory Distress Syndrome (pARDS) and heart failure.
Human-associated microbial communities (microbiota) play an essential role in immunity, health, and disease. Understanding human microbiota and its genomes (microbiome) will increase the possibility of various applications including personalized medicine and cancer immune therapy. Dr. Yu will talk about her research in microbiota, exposures, and lung cancer. She will also discuss the challenges of microbiota research.
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