Open AccessApplications of machine learning in acute care research
Author(s) -
Ohu Ikechukwu,
Benny Paul Kummannoor,
Rodrigues Steven,
Carlson Jestin N.
Publication year - 2020
Publication title -
journal of the american college of emergency physicians open
Language(s) - English
Resource type - Journals
ISSN - 2688-1152
DOI - 10.1002/emp2.12156
Subject(s) - artificial intelligence , machine learning , computer science , field (mathematics) , health care , artificial neural network , deep learning , mathematics , pure mathematics , economics , economic growth
Abstract Artificial intelligence has been successfully applied to numerous health care and non‐health care‐related applications and its use in emergency medicine has been expanding. Among its advantages are its speed in decision making and the opportunity for rapid, actionable deduction from unstructured data with that increases with access to larger volumes of data. Artificial intelligence algorithms are currently being applied to enable faster prognosis and diagnosis of diseases and to improve patient outcomes. 1,2 Despite the successful application of artificial intelligence, it is still fraught with limitations and “unknowns” pertaining to the fact that a model's accuracy is dependent on the amount of information available for training the model, and the understanding of the complexity presented by current artificial intelligence and machine learning algorithms is often limited in many individuals outside of those involved in the field. This paper reviews the applications of artificial intelligence and machine learning to acute care research and highlights commonly used machine learning techniques, limitations, and potential future applications.