Machine Learning for Pulmonary and Critical Care Medicine: A Narrative Review
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41030_2020_Article_110.pdf
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293.29 KB
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Adobe PDF
Checksum (MD5)
5395a272d036d7c8d18d73631f1ce45e
Author(s)
Mlodzinski, Eric
Stone, David J
Celi, Leo A
Date Issued
February 5, 2020
Publisher
Springer Healthcare
Version
Final published version
Abstract
Abstract
Machine learning (ML) is a discipline of computer science in which statistical methods are applied to data in order to classify, predict, or optimize, based on previously observed data. Pulmonary and critical care medicine have seen a surge in the application of this methodology, potentially delivering improvements in our ability to diagnose, treat, and better understand a multitude of disease states. Here we review the literature and provide a detailed overview of the recent advances in ML as applied to these areas of medicine. In addition, we discuss both the significant benefits of this work as well as the challenges in the implementation and acceptance of this non-traditional methodology for clinical purposes.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
MIT Critical Data (Laboratory)
Terms of Use
Creative Commons Attribution
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s41030-020-00110-z
