Strategies to address the lack of labelled data for supervised machine learning training with electronic health records: a case study for extraction of symptoms from clinical notes (Preprint)-Reference-Cited by-同舟云学术

Strategies to address the lack of labelled data for supervised machine learning training with electronic health records: a case study for extraction of symptoms from clinical notes (Preprint)

Published:2021-08-13 Issue: Volume: Page:
ISSN:2291-9694
Container-title:JMIR Medical Informatics
language:en
Short-container-title:

Author:

Humbert-Droz Marie,Mukherjee Pritam,Gevaert Olivier

Publisher

JMIR Publications Inc.

Subject

Health Information Management,Health Informatics

Reference43 articles.

1. Natural language processing of symptoms documented in free-text narratives of electronic health records: a systematic review

2. Role and Challenges of Unstructured Big Data in Healthcare

3. Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions

4. Natural language processing: State of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine

5. Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances

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