Is repeat blood-testing necessary? AI could help decide
You want and expect your doctor to be thorough, but over-testing can raise concerns as well, can you be too thorough? Jonathan Chen, MD, Ph. D., Assistant Professor of Medicine, believes that the answer is yes, especially when looking at blood diagnostic testing. Blood tests are an important diagnostic tool, but often repeated tests yield diminished results. Repeated tests not only often show no change, but they are uncomfortable for the patient. Couple this with concerns about rising healthcare expenses and it is clear that a solution needs to be found.
Machine learning algorithms can synthesize volumes of electronic medical record data to systematically identify low yield tests, quantifying the predictability of results to encourage high-value care. Chen and a team of researchers and physicians have taken steps to try and address the problem and have created an algorithm that can predict whether a given blood test will come back "normal." Their work was recently published in the JAMA Network Open. This algorithm can tell doctors if a repeat test will be likely to produce a result that is different from the original test. These algorithms can often predict >95% chance that a test will yield normal results, but it is a separate issue for laboratorians, clinicians, and patients to decide in individual cases whether it is *worth* checking for something when you were already 95% sure of the answer.
Multi-omics data fusion with AMARETTO fosters a Community of Cancer Informatics Researchers
The Gevaert lab developed the initial AMARETTO algorithm in 2013 and has since then expanded the team by working together with Dr. Nathalie Pochet at Brigham & Women/Harvard University and with Dr. Mikel Hernaez at University of Illinois Urbana-Champaign. Together these teams have taken a tool that required data scientists to operate, to create the more user friendly GenePattern environment supported by the Informatics Technology for Cancer Research program at the National Cancer Institute. In addition AMARETTO has been expanded to include the ability to link patient data to model systems for improved drug target discovery, linking with perturbation data from the LINCS project to validate predictions, and integration of images and image phenotypes including radiography and histopathology imaging data. Read more about how AMARETTO was expanded here.
The Shah Lab Green Button Team Awarded The 2019 ISP Star Award
The Shah Lab Green Button team (center, from left to right: Ken Jung, Vladimir Polony, Alison Callahan, Saurabh Gombar) received a 2019 ISP Star Award, which recognizes individuals from the Stanford Medicine community who, through their extraordinary efforts, embody the strategic priorities of our Integrated Strategic Plan (ISP): Value Focused, Digitally Driven, and Uniquely Stanford. By developing a technology that amplifies the clinical impact of our world-class faculty and creates outcome-driven recommendations at the point care, the Green Button exemplifies the transformative power of collaboration at Stanford Medicine. They were presented the award by David Entwistle (President and CEO of Stanford Health Care; left), Lloyd Minor (Dean of the School of Medicine; second from right) and Paul A. King (President and CEO of Stanford Children's Health; right).
The CEDAR project has been chosen as a key participant in the GO FAIR Metadata for Machines (M4M) workshops and the FAIR Funder pilot program. The GO FAIR initiative, an international program to advance FAIR (findable, accessible, interoperable, reusable) data and services, launched the M4M workshop series to stimulate the creation and re-use of FAIR metadata standards and machine-ready metadata templates. The M4M workshops have adopted CEDAR to provide the metadata capabilities for the initial workshops.
The M4M workshops are agile events that bring together domain experts, metadata specialists, and technical developers to define metadata elements and standards, create machine-actionable templates for collecting metadata according to those standards, and register the templates for open access, discovery, and re-use. CEDAR has worked with the Leiden-based GO FAIR team for over a year to demonstrate and evaluate the CEDAR technologies for these tasks, and participated in two recent M4M workshops. At the second of these, two national science funders—the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW)—took the first steps toward a complete life cycle of FAIR metadata for their communities by creating CEDAR templates to capture metadata of interest to research funding agencies.
In coming months, CEDAR expects to participate in several more M4M workshops, and advance the FAIR Funder pilot program (depicted in the diagram) through many more of the 7 stages shown in the diagram. CEDAR initially provided services to define metadata elements (1), create machine-actionable templates (2), and register the templates in the CEDAR repository for later re-use (3), and CEDAR enhancements already under way will provide advanced search and open publication for the CEDAR metadatda resources that have been developed, and integrated metadata authoring in coordination with the DSL Data Wizard (4). Also important to GO FAIR: CEDAR already provides rigorous semantic capabilities that let CEDAR metadata be published as JSON-LD or simple RDF triples, two highly interoperable formats for sharing research metadata.
The CEDAR technologies adopted by GO FAIR's M4M workshops and the FAIR Funder pilot program are helping the GO FAIR community move metadata management from ad-hoc, individually developed solutions to a more rigorous, structured, and user-friendly approach to metadata.
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