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
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

104360

Subventions

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.

Références

LREC Int Conf Lang Resour Eval. 2022 Jun;2022:5484-5493
pubmed: 35939277
N Engl J Med. 1968 Mar 14;278(11):593-600
pubmed: 5637758
J Biomed Inform. 2013 Oct;46(5):914-20
pubmed: 23906817
J Am Med Inform Assoc. 2020 Jan 1;27(1):3-12
pubmed: 31584655
Ann Intern Med. 2018 Jul 3;169(1):50-51
pubmed: 29801050
BMC Bioinformatics. 2015 Feb 21;16:55
pubmed: 25886734
Am J Hematol. 2016 May;91(5):E280-3
pubmed: 26875020
Patterns (N Y). 2023 Apr 14;4(4):100729
pubmed: 37123444
J Biomed Inform. 2023 Feb;138:104286
pubmed: 36706848
Sci Data. 2016 May 24;3:160035
pubmed: 27219127
Sci Data. 2021 Jun 2;8(1):149
pubmed: 34078918
J Am Med Inform Assoc. 2021 Sep 18;28(10):2108-2115
pubmed: 34333635
J Biomed Inform. 2023 Apr 13;142:104346
pubmed: 37061012
J Am Med Inform Assoc. 2011 Sep-Oct;18(5):552-6
pubmed: 21685143
J Am Med Inform Assoc. 2005 May-Jun;12(3):296-8
pubmed: 15684123
AMIA Annu Symp Proc. 2020 Mar 04;2019:1236-1245
pubmed: 32308921

Auteurs

Vimig Socrates (V)

Section for Biomedical Informatics and Data Science, Yale University School of Medicine, 300 George St, 06511, New Haven, USA; Department of Emergency Medicine, Yale University School of Medicine, 464 Congress Ave #260, New Haven, 06519, USA; Program of Computational Biology and Bioinformatics, Yale University, 300 George St, New Haven, 06511, USA. Electronic address: vimig.socrates@yale.edu.

Aidan Gilson (A)

Department of Emergency Medicine, Yale University School of Medicine, 464 Congress Ave #260, New Haven, 06519, USA. Electronic address: aidan.gilson@yale.edu.

Kevin Lopez (K)

Section for Biomedical Informatics and Data Science, Yale University School of Medicine, 300 George St, 06511, New Haven, USA; Department of Emergency Medicine, Yale University School of Medicine, 464 Congress Ave #260, New Haven, 06519, USA. Electronic address: kevin.lopez@yale.edu.

Ling Chi (L)

Department of Emergency Medicine, Yale University School of Medicine, 464 Congress Ave #260, New Haven, 06519, USA. Electronic address: ling.chi@yale.edu.

Richard Andrew Taylor (RA)

Section for Biomedical Informatics and Data Science, Yale University School of Medicine, 300 George St, 06511, New Haven, USA; Department of Emergency Medicine, Yale University School of Medicine, 464 Congress Ave #260, New Haven, 06519, USA. Electronic address: richard.taylor@yale.edu.

David Chartash (D)

Section for Biomedical Informatics and Data Science, Yale University School of Medicine, 300 George St, 06511, New Haven, USA; School of Medicine, University College Dublin - National University of Ireland, Dublin, Health Sciences Centre, Belfield, Dublin 4, Ireland. Electronic address: david.chartash@yale.edu.

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