Stefano Falini1, Giovanni Angelotti2, Maurizio Cecconi1,3. 1. Department of Anesthesia and Intensive Care. 2. Data Science Core Facility, Humanitas Clinical and Research Center, Rozzano. 3. Humanitas University, Milano, Italy.
Abstract
PURPOSE OF REVIEW: The availability of large datasets and computational power has prompted a revolution in Intensive Care. Data represent a great opportunity for clinical practice, benchmarking, and research. Machine learning algorithms can help predict events in a way the human brain can simply not process. This possibility comes with benefits and risks for the clinician, as finding associations does not mean proving causality. RECENT FINDINGS: Current applications of Data Science still focus on data documentation and visualization, and on basic rules to identify critical lab values. Recently, algorithms have been put in place for prediction of outcomes such as length of stay, mortality, and development of complications. These results have begun being implemented for more efficient allocation of resources and in benchmarking processes, to allow identification of successful practices and margins for improvement. In parallel, machine learning models are increasingly being applied in research to expand medical knowledge. SUMMARY: Data have always been part of the work of intensivists, but the current availability has not been completely exploited. The intensive care community has to embrace and guide the data science revolution in order to decline it in favor of patients' care.
PURPOSE OF REVIEW: The availability of large datasets and computational power has prompted a revolution in Intensive Care. Data represent a great opportunity for clinical practice, benchmarking, and research. Machine learning algorithms can help predict events in a way the human brain can simply not process. This possibility comes with benefits and risks for the clinician, as finding associations does not mean proving causality. RECENT FINDINGS: Current applications of Data Science still focus on data documentation and visualization, and on basic rules to identify critical lab values. Recently, algorithms have been put in place for prediction of outcomes such as length of stay, mortality, and development of complications. These results have begun being implemented for more efficient allocation of resources and in benchmarking processes, to allow identification of successful practices and margins for improvement. In parallel, machine learning models are increasingly being applied in research to expand medical knowledge. SUMMARY: Data have always been part of the work of intensivists, but the current availability has not been completely exploited. The intensive care community has to embrace and guide the data science revolution in order to decline it in favor of patients' care.
Authors: Maria Elena Laino; Elena Generali; Tobia Tommasini; Giovanni Angelotti; Alessio Aghemo; Antonio Desai; Pierandrea Morandini; Giulio G Stefanini; Ana Lleo; Antonio Voza; Victor Savevski Journal: Arch Med Sci Date: 2022-01-14 Impact factor: 3.707