ArticleProceedings of the ACM ... International Workshop on Data and Text Mining in Biomedical Informatics. ACM International Workshop on Data and Text Mining in Biomedical Informatics
Document Sublanguage Clustering to Detect Medical Specialty in Cross-institutional Clinical Texts.
Article in Proceedings of the ACM ... International Workshop on Data and Text Mining in Biomedical Informatics. ACM International Workshop on Data and Text Mining in Biomedical Informatics. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Dynamic few-shot prompting for clinical note section classification using lightweight, open-source large language models.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Contextual Variation of Clinical Notes induced by EHR Migration.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2023Article
- Collecting specialty-related medical terms: Development and evaluation of a resource for Spanish.BMC medical informatics and decision making · 2021Article
- Ensembles of natural language processing systems for portable phenotyping solutions.Journal of biomedical informatics · 2019Article
- Detecting Secular Trends in Clinical Treatment through Temporal Analysis.Journal of medical systems · 2019Article
- Trie-based rule processing for clinical NLP: A use-case study of n-trie, making the ConText algorithm more efficient and scalable.Journal of biomedical informatics · 2018Article
- Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach.BMC medical informatics and decision making · 2017Article
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4 authors.
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Abstract
This paper reports on a set of studies designed to identify sublanguages in documents for domain-specific processing across institutions. Psychological evidence indicates that humans use context-specific linguistic information when they read. Natural Language Processing (NLP) pipelines are successful within specific domains (i.e., contexts). To limit the number of domain-specific NLP systems, a natural focus would be on sublanguages. Sublanguages are identified by shared lexical and semantic features.[1] Patterson and Hurdle[2] developed a sublanguage identification system that functioned well for 12 clinical specialties at the University of Utah. The current work compares sublanguages across institutions. Using a clinical NLP pipeline augmented by a new document corpus from the University of Pittsburg (UPitt), new documents were assigned to clusters based on the minimum cosine-distance to a Utah cluster centroid. The UPitt documents were divided into a nine-group specialty corpus. Across institutions, five of the specialty groups fell within the expected clusters. We find that clustering encounters difficulty due to documents with mixed sublanguages; naming convention differences across institutions; and document types used across specialties. The findings indicate that clinical specialty sublanguages can be identified across institutions.
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Registered trials
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