ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2019
Comparison of Natural Language Processing Techniques in Analysis of Sparse Clinical Data: Insulin Decline by Patients.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Development of a natural language processing algorithm to extract social determinants of health from clinician notes.American journal of transplantation : official journal of the American Society of Transplantation and the American Society of Transplant Surgeons · 2025Article
- Phenotyping severity of patient-centered outcomes using clinical notes: A prostate cancer use case.Learning health systems · 2020Article
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4 authors.
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Abstract
We present a comparative evaluation of a range of popular Natural Language Processing (NLP) approaches for Information Extraction (IE) in clinical documents to detect cases of patients declining medication that has been recommended by their providers. More specifically, we tackle the task of identifying diabetics who decline insulin, using a training set of 51k randomly selected provider notes. Analysis shows that decline of insulin by patients is a rare phenomenon, with a document-level prevalence of approx. 0.1%. We examine the effectiveness of some of the most popular IE approaches, including sentence-level support vector machines (SVM)-based classification, token- level sequence labelling using conditional random fields (CRFs), and rule-based detection based on encoding human knowledge. Our results on a held-out test set show that the generalization of rule-based approach (F1=0.97) outperforms the SVM (F1=0.61) and CRF models (F1=0.40).
Identifiers
31259016PMC6568116What OpenQuestion holds
Registered trials
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