ArticleJournal of the American Medical Informatics Association : JAMIA
Word sense disambiguation in the clinical domain: a comparison of knowledge-rich and knowledge-poor unsupervised methods.
Article in Journal of the American Medical Informatics Association : JAMIA. 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.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Clinical Natural Language Processing in 2014: Foundational Methods Supporting Efficient Healthcare.Yearbook of medical informatics · 2015Pooled it
- Application of artificial intelligence to corelate food formulations to disease risk prediction: a comprehensive review.Journal of food science and technology · 2023Review
- Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Complexities, variations, and errors of numbering within clinical notes: the potential impact on information extraction and cohort-identification.BMC medical informatics and decision making · 2019Article
- A novel framework for biomedical entity sense induction.Journal of biomedical informatics · 2018Article
- A bibliometric analysis of natural language processing in medical research.BMC medical informatics and decision making · 2018Article
- Knowledge-Based Biomedical Word Sense Disambiguation with Neural Concept EmbeddingsProceedings. IEEE International Symposium on Bioinformatics and Bioengineering · 2017Article
- Semantic annotation in biomedicine: the current landscape.Journal of biomedical semantics · 2017Review
- Distinction between medical and non-medical usages of short forms in clinical narratives.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Trends in biomedical informatics: automated topic analysis of JAMIA articles.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Challenges in clinical natural language processing for automated disorder normalization.Journal of biomedical informatics · 2015Article
- KnowLife: a versatile approach for constructing a large knowledge graph for biomedical sciences.BMC bioinformatics · 2015Article
- Concept Modeling-based Drug Repositioning.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2015Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
objectiveTo evaluate state-of-the-art unsupervised methods on the word sense disambiguation (WSD) task in the clinical domain. In particular, to compare graph-based approaches relying on a clinical knowledge base with bottom-up topic-modeling-based approaches. We investigate several enhancements to the topic-modeling techniques that use domain-specific knowledge sources. MATERIALS AND
methodsThe graph-based methods use variations of PageRank and distance-based similarity metrics, operating over the Unified Medical Language System (UMLS). Topic-modeling methods use unlabeled data from the Multiparameter Intelligent Monitoring in Intensive Care (MIMIC II) database to derive models for each ambiguous word. We investigate the impact of using different linguistic features for topic models, including UMLS-based and syntactic features. We use a sense-tagged clinical dataset from the Mayo Clinic for evaluation.
resultsThe topic-modeling methods achieve 66.9% accuracy on a subset of the Mayo Clinic's data, while the graph-based methods only reach the 40-50% range, with a most-frequent-sense baseline of 56.5%. Features derived from the UMLS semantic type and concept hierarchies do not produce a gain over bag-of-words features in the topic models, but identifying phrases from UMLS and using syntax does help. DISCUSSION: Although topic models outperform graph-based methods, semantic features derived from the UMLS prove too noisy to improve performance beyond bag-of-words.
conclusionsTopic modeling for WSD provides superior results in the clinical domain; however, integration of knowledge remains to be effectively exploited.
Indexed as
Identifiers
What 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.