ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2015
A Hybrid Approach to Extracting Disorder Mentions from Clinical Notes.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- From Conversation to Standardized Terminology: An LLM-RAG Approach for Automated Health Problem Identification in Home Healthcare.Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing · 2025Article
- Clinical concept extraction: A methodology review.Journal of biomedical informatics · 2020Review
- Clinical Text Data in Machine Learning: Systematic Review.JMIR medical informatics · 2020Review
- Clinical concept annotation with contextual word embedding in active transfer learning environment.Digital healthArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Crucial information on a patient's physical or mental conditions is provided by mentions of disorders, such as disease, syndrome, injury, and abnormality. Identifying disorder mentions is one of the most significant steps in clinical text analysis. However, there are many surface forms of the same concept documented in clinical notes. Some are even recorded disjointedly, briefly, or intuitively. Such difficulties have challenged the information extraction systems that focus on identifying explicit mentions. In this study, we proposed a hybrid approach to disorder extraction, which leverages supervised machine learning, rule-based annotation, and an unsupervised NLP system. To identify different surface forms, we exploited rich features, especially the semantic, syntactic, and sequential features, for better capturing implicit relationships among words. We evaluated our method on the CLEF 2013 eHealth dataset. The experiments showed that our hybrid approach achieves a 0.776 F-score under strict evaluation standards, outperforming any participating systems in the Challenge.
Identifiers
26306265PMC4525272What OpenQuestion holds
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.