Ontology-driven weak supervision for clinical entity classification in electronic health records.
Nat Commun
; 12(1): 2017, 2021 04 01.
Article
in English
| MEDLINE | ID: covidwho-1164850
ABSTRACT
In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity classification using medical ontologies and expert-generated rules. Our approach, unlike hand-labeled notes, is easy to share and modify, while offering performance comparable to learning from manually labeled training data. In this work, we validate our framework on six benchmark tasks and demonstrate Trove's ability to analyze the records of patients visiting the emergency department at Stanford Health Care for COVID-19 presenting symptoms and risk factors.
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Expert Systems
/
Data Curation
/
Machine Learning
/
COVID-19
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Nat Commun
Journal subject:
Biology
/
Science
Year:
2021
Document Type:
Article
Affiliation country:
S41467-021-22328-4
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