Literature DB >> 31036284

Machine Learning and Other Emerging Decision Support Tools.

Jason M Baron1, Danielle E Kurant2, Anand S Dighe2.   

Abstract

Emerging applications of machine learning and artificial intelligence offer the opportunity to discover new clinical knowledge through secondary exploration of existing patient medical records. This new knowledge may in turn offer a foundation to build new types of clinical decision support (CDS) that provide patient-specific insights and guidance across a wide range of clinical questions and settings. This article will provide an overview of these emerging approaches to CDS, discussing both existing technologies as well as challenges that health systems and informaticists will need to address to allow these emerging approaches to reach their full potential.
Copyright © 2019 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Artificial intelligence; Clinical decision support; Computational pathology; Knowledge discovery; Machine learning

Mesh:

Year:  2019        PMID: 31036284     DOI: 10.1016/j.cll.2019.01.010

Source DB:  PubMed          Journal:  Clin Lab Med        ISSN: 0272-2712            Impact factor:   1.935


  2 in total

1.  Use of machine learning to predict clinical decision support compliance, reduce alert burden, and evaluate duplicate laboratory test ordering alerts.

Authors:  Jason M Baron; Richard Huang; Dustin McEvoy; Anand S Dighe
Journal:  JAMIA Open       Date:  2021-03-01

2.  Development of a "meta-model" to address missing data, predict patient-specific cancer survival and provide a foundation for clinical decision support.

Authors:  Jason M Baron; Ketan Paranjape; Tara Love; Vishakha Sharma; Denise Heaney; Matthew Prime
Journal:  J Am Med Inform Assoc       Date:  2021-03-01       Impact factor: 4.497

  2 in total

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