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...

Szczegółowa specyfikacja

Opis bibliograficzny
Główni autorzy: Marie Humbert-Droz, Pritam Mukherjee, Olivier Gevaert
Format: Artykuł
Język:English
Wydane: JMIR Publications 2022-03-01
Seria:JMIR Medical Informatics
Dostęp online:https://medinform.jmir.org/2022/3/e32903

Internet

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

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