ArticleJournal of biomedical informatics2012
Building an automated SOAP classifier for emergency department reports.
Article in Journal of biomedical informatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 41 citations in OpenAlex.
- Current approaches to identify sections within clinical narratives from electronic health records: a systematic review.BMC medical research methodology · 2019Pooled it
- RETRACTED: Artificial intelligence for emergency medical care.Health care science · 2025Review
- Improving model transferability for clinical note section classification models using continued pretraining.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Improving Model Transferability for Clinical Note Section Classification Models Using Continued Pretraining.medRxiv : the preprint server for health sciences · 2023Article
- Hierarchical Annotation for Building A Suite of Clinical Natural Language Processing Tasks: Progress Note Understanding.LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation · 2022Article
- Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish.BMC medical informatics and decision making · 2021Article
- A Preliminary Characterization of Canonicalized and Non-Canonicalized Section Headers Across Variable Clinical Note Types.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- A Natural Language Processing System That Links Medical Terms in Electronic Health Record Notes to Lay Definitions: System Development Using Physician Reviews.Journal of medical Internet research · 2018Article
- Ranking Medical Terms to Support Expansion of Lay Language Resources for Patient Comprehension of Electronic Health Record Notes: Adapted Distant Supervision Approach.JMIR medical informatics · 2017Article
- Evaluation of Clinical Text Segmentation to Facilitate Cohort Retrieval.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Determinants of a successful problem list to support the implementation of the problem-oriented medical record according to recent literature.BMC medical informatics and decision making · 2016Review
- Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry.Journal of digital imaging · 2015Article
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Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Information extraction applications that extract structured event and entity information from unstructured text can leverage knowledge of clinical report structure to improve performance. The Subjective, Objective, Assessment, Plan (SOAP) framework, used to structure progress notes to facilitate problem-specific, clinical decision making by physicians, is one example of a well-known, canonical structure in the medical domain. Although its applicability to structuring data is understood, its contribution to information extraction tasks has not yet been determined. The first step to evaluating the SOAP framework's usefulness for clinical information extraction is to apply the model to clinical narratives and develop an automated SOAP classifier that classifies sentences from clinical reports. In this quantitative study, we applied the SOAP framework to sentences from emergency department reports, and trained and evaluated SOAP classifiers built with various linguistic features. We found the SOAP framework can be applied manually to emergency department reports with high agreement (Cohen's kappa coefficients over 0.70). Using a variety of features, we found classifiers for each SOAP class can be created with moderate to outstanding performance with F(1) scores of 93.9 (subjective), 94.5 (objective), 75.7 (assessment), and 77.0 (plan). We look forward to expanding the framework and applying the SOAP classification to clinical information extraction tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.