ArticleJournal of biomedical informatics2023
A hybrid system to understand the relations between assessments and plans in progress notes.
Article in Journal of biomedical informatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.JMIR medical informatics · 2024Article
- Automated stratification of trauma injury severity across multiple body regions using multi-modal, multi-class machine learning models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Clinical natural language processing for secondary uses.Journal of biomedical informatics · 2024Article
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Authors and funding
4 authors.
Funding
Abstract
objectiveThe paper presents a novel solution to the 2022 National NLP Clinical Challenges (n2c2) Track 3, which aims to predict the relations between assessment and plan subsections in progress notes.
methodsOur approach goes beyond standard transformer models and incorporates external information such as medical ontology and order information to comprehend the semantics of progress notes. We fine-tuned transformers to understand the textual data and incorporated medical ontology concepts and their relationships to enhance the model's accuracy. We also captured order information that regular transformers cannot by taking into account the position of the assessment and plan subsections in progress notes.
resultsOur submission earned third place in the challenge phase with a macro-F1 score of 0.811. After refining our pipeline further, we achieved a macro-F1 of 0.826, outperforming the top-performing system during the challenge phase.
conclusionOur approach, which combines fine-tuned transformers, medical ontology, and order information, outperformed other systems in predicting the relationships between assessment and plan subsections in progress notes. This highlights the importance of incorporating external information beyond textual data in natural language processing (NLP) tasks related to medical documentation. Our work could potentially improve the efficiency and accuracy of progress note analysis.
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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.