The Data Science in Intensive Care Medicine Knowledge Education Group (KEG) aims to support Society of Critical Care Medicine members in learning how data science can be applied to problems in critical care medicine. Data science is an interdisciplinary field that employs statistical and machine learning (ML) methods to extract knowledge from complex datasets.
This KEG builds on three emerging trends:
- Availability of unprecedented amounts of high-dimensional health data (“big data”) from multiple sources (eg, electronic health records, physiologic recordings, imaging, genomics)
- Rapidly growing use of ML algorithms to support data-intensive tasks
- Recognition that big data/ML can be successfully applied to support core missions in medicine and healthcare delivery (precision health, clinical decision-making, operational efficiency, value-based care, training, and education)
From data characterization to advanced ML techniques and applications, the KEG will explore and share knowledge related to all facets of this rapidly growing field.
Join the Data Science KEG to join the conversation, connect with other participants, and access the library of resources.
Robert D. Stevens, MD, PhD, FCCM, Johns Hopkins University School of Medicine, Baltimore, USA
Piyush Mathur, MD, FCCM, The Cleveland Clinic, Cleveland, OH, USA