Predicting relations between SOAP note sections: The value of incorporating a clinical information model.
Electronic health record
Entailment
Intensive care unit
Language modeling
Natural language processing
SOAP notes
Journal
Journal of biomedical informatics
ISSN: 1532-0480
Titre abrégé: J Biomed Inform
Pays: United States
ID NLM: 100970413
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
received:
06
01
2023
revised:
27
03
2023
accepted:
05
04
2023
pmc-release:
01
05
2024
medline:
8
5
2023
pubmed:
16
4
2023
entrez:
15
4
2023
Statut:
ppublish
Résumé
Physician progress notes are frequently organized into Subjective, Objective, Assessment, and Plan (SOAP) sections. The Assessment section synthesizes information recorded in the Subjective and Objective sections, and the Plan section documents tests and treatments to narrow the differential diagnosis and manage symptoms. Classifying the relationship between the Assessment and Plan sections has been suggested to provide valuable insight into clinical reasoning. In this work, we use a novel human-in-the-loop pipeline to classify the relationships between the Assessment and Plan sections of SOAP notes as a part of the n2c2 2022 Track 3 Challenge. In particular, we use a clinical information model constructed from both the entailment logic expected from the aforementioned Challenge and the problem-oriented medical record. This information model is used to label named entities as primary and secondary problems/symptoms, events and complications in all four SOAP sections. We iteratively train separate Named Entity Recognition models and use them to annotate entities in all notes/sections. We fine-tune a downstream RoBERTa-large model to classify the Assessment-Plan relationship. We evaluate multiple language model architectures, preprocessing parameters, and methods of knowledge integration, achieving a maximum macro-F1 score of 82.31%. Our initial model achieves top-2 performance during the challenge (macro-F1: 81.52%, competitors' macro-F1 range: 74.54%-82.12%). We improved our model by incorporating post-challenge annotations (S&O sections), outperforming the top model from the Challenge. We also used Shapley additive explanations to investigate the extent of language model clinical logic, under the lens of our clinical information model. We find that the model often uses shallow heuristics and nonspecific attention when making predictions, suggesting language model knowledge integration requires further research.
Identifiants
pubmed: 37061014
pii: S1532-0464(23)00081-3
doi: 10.1016/j.jbi.2023.104360
pmc: PMC10197152
mid: NIHMS1892003
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
104360Subventions
Organisme : NLM NIH HHS
ID : T15 LM007056
Pays : United States
Organisme : NIDDK NIH HHS
ID : T35 DK104689
Pays : United States
Informations de copyright
Copyright © 2023 Elsevier Inc. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Vimig Socrates reports financial support was provided by National Institutes of Health. Aidan Gilson reports financial support was provided by National Institute of Diabetes and Digestive and Kidney Diseases. Aidan Gilson reports financial support was provided by Yale School of Medicine.
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