Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios:

مرکز و کتابخانه مطالعات اسلامی به زبان های اروپایی

منو

" Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios: "
Lee, Sangil; Mohr, Nicholas M.; Street, W. Nicholas; Nadkarni, Prakash

Document Type	:	AL
Record Number	:	913227
Doc. No	:	LA71f2f15r
Title & Author	:	Machine Learning in Relation to Emergency Medicine Clinical and Operational Scenarios:. An Overview [Article]\ Lee, Sangil; Mohr, Nicholas M.; Street, W. Nicholas; Nadkarni, Prakash
Date	:	2019
Title of Periodical	:	UC Irvine
Abstract	:	Health informatics is a vital technology that holds great promise in the healthcare setting. We describe two prominent health informatics tools relevant to emergency care, as well as the historical background and the current state of informatics. We also identify recent research findings and practice changes. The recent advances in machine learning and natural language processing (NLP) are a prominent development in health informatics overall and relevant in emergency medicine (EM). A basic comprehension of machine-learning algorithms is the key to understand the recent usage of artificial intelligence in healthcare. We are using NLP more in clinical use for documentation. NLP has started to be used in research to identify clinically important diseases and conditions. Health informatics has the potential to benefit both healthcare providers and patients. We cover two powerful tools from health informatics for EM clinicians and researchers by describing the previous successes and challenges and conclude with their implications to emergency care.