Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes

Strategies to Address the Lack of Labeled Data for Supervised Machine Learning Training With Electronic Health Records: Case Study for the Extraction of Symptoms From Clinical Notes

BackgroundAutomated extraction of symptoms from clinical notes is a challenging task owing to the multidimensional nature of symptom description. The availability of labeled training data is extremely limited owing to the nature of the data containing protected health informa...

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Bibliographic Details
Main Authors: Marie Humbert-Droz, Pritam Mukherjee, Olivier Gevaert
Format: Article
Language:English
Published: JMIR Publications 2022-03-01
Series:JMIR Medical Informatics
Online Access:https://medinform.jmir.org/2022/3/e32903

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https://medinform.jmir.org/2022/3/e32903

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