Making Machine Learning Models Clinically Useful
Item
Title
Making Machine Learning Models Clinically Useful
Abstract/Description
Recent advances in supervised machine learning have improved diagnostic accuracy and prediction of treatment outcomes, in some cases surpassing the performance of clinicians. In supervised machine learning, a mathematical function is constructed via automated analysis of training data, which consists of input features (such as retinal images) and output labels (such as the grade of macular edema). With large training data sets and minimal human guidance, a computer learns to generalize from the information contained in the training data. The result is a mathematical function, a model, that can be used to map a new record to the corresponding diagnosis, such as an image to grade macular edema. Although machine learning–based models for classification or for predicting a future health state are being developed for diverse clinical applications, evidence is lacking that deployment of these models has improved care and patient outcomes.
Author/creator
Date
In publication
Volume
322
Issue
14
Pages
1351-1352
Resource type
Background/Context
Medium
Print
Background/context type
Conceptual
Open access/free-text available
No
Peer reviewed
No
ISSN
0098-7484
Citation
Shah, N. H., Milstein, A., & Bagley, P., Steven C. (2019). Making Machine Learning Models Clinically Useful. JAMA, 322(14), 1351–1352. https://doi.org/10.1001/jama.2019.10306
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