Poster abstracts from fourth annual public meeting: Mobilizing computable biomedical knowledge (MCBK 2021)

• Published in: Learning health systems Vol. 6; no. 1
• Format: Journal Article
• Language: English
• Published: Boston John Wiley & Sons, Inc 01.01.2022; John Wiley and Sons Inc
• Subjects: Abstracts; Big Data; Chronic illnesses; Cognition & reasoning; Coronaviruses; COVID-19; Emergency medical care; Health education; Health literacy; Infectious diseases; Knowledge representation; Machine learning; Metadata; Ontology; Pandemics; Public health; Semantic web; Semantics; Social networks; Subject specialists; User generated content; United States > US
• ISSN: 2379-6146; 2379-6146
• DOI: 10.1002/lrh2.10300

Technical posters A heterogeneous knowledge environment for cognitive support applications Davide Sottara1, PhD; Jane L. Shellum1, MS; Adam M. Bartscher1; Marc L. Sainvil1, MS; Matthew D. Hegland1; Branden C. Hickey1, MAS 1Mayo Clinic, 200 1st Street SW, Rochester, MN 55905, USA {family.firstname}@mayo.edu Modern cognitive support applications are complex systems that combine background (bio)medical knowledge with knowledge about physiological processes, decision-making criteria, evidence-based guidelines, clinical workflows, human-computer interaction protocols, and semantics of data sources, including electronic health records. Use of CEDR big data for elucidating COVID-19's impact on emergency care Dhruv B. Sharma1, MS, ACEP, Irving, TX; Dr. Pawan Goyal1, MD, MHA, PMP, MS, ACEP, Irving, TX; Dr. Arjun K. Venkatesh1, MD, MBA, MHS 1Yale School of Medicine, New Haven, CT, USA The COVID-19 pandemic generated several crises specific to the emergency department (ED) including large drops in ED patient volumes and increased burden of capturing valid pandemic data to measure nationwide effects in emergent ED conditions. Since 2015, the American College of Emergency Physicians (ACEP) has been developing and maturing its Clinical Emergency Data Registry (CEDR) into a big data resource to facilitate data reporting and democratize nationwide emergency medicine analytics. Decentralized and reproducible geocoding and characterization of community and environmental exposures for multi-site studies Erika Rasnick1, MS; Andrew Vancil1, MS; Cole Brokamp1, PhD 1Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA DeGAUSS is a computable biomedical knowledge system for offline geocoding and assessment of place-based sociodemographic and environmental exposures in health studies. An augmented-intelligence machine learning pipeline to retrieve and recommend videos on chronic and infectious diseases Rema Padman1, PhD; Xiao Liu2, PhD; Anjana Susarla3, PhD 1Carnegie Mellon University, Pittsburgh, PA, USA; 2Arizona State University, Tempe, AZ, USA; 3Michigan State University, Lansing, MI, USA The easy availability of a vast repository of user-generated health information on social networks, blogs, YouTube, and Twitter presents a significant opportunity to investigate how social media channels can inform and communicate healthcare information for patient-centric health promotion and literacy improvement.