ArticleJournal of the American Medical Informatics Association : JAMIA
Learning regular expressions for clinical text classification.
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 41 papers, 2 of them syntheses that pooled it.
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Who cites it
41 citing papers in PubMed, 2 syntheses or guidelines pooled it.
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- Natural Language Processing for Identification of Hospitalized People Who Use Drugs: Cohort Study.JMIR AI · 2025Article
- Leveraging deep neural network and language models for predicting long-term hospitalization risk in schizophrenia.Schizophrenia (Heidelberg, Germany) · 2025Article
- Implementation of a rule-based algorithm to find patients eligible for cancer clinical trials.JAMIA open · 2024Article
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- Automated, High-Throughput Platform to Generate a High-Reliability, Comprehensive Rectal Cancer Database.JCO clinical cancer informatics · 2024Article
- Detection of Patient-Level Immunotherapy-Related Adverse Events (irAEs) from Clinical Narratives of Electronic Health Records: A High-Sensitivity Artificial Intelligence Model.Pragmatic and observational research · 2024Article
- The Application of Computer Technology to Clinical Practice Guideline Implementation: A Scoping Review.Journal of medical systems · 2023Article
- RegEMR: a natural language processing system to automatically identify premature ovarian decline from Chinese electronic medical records.BMC medical informatics and decision making · 2023Article
- CytofIn enables integrated analysis of public mass cytometry datasets using generalized anchors.Nature communications · 2022Article
- A method to advance adolescent sexual health research: Automated algorithm finds sexual history documentation.Frontiers in digital health · 2022Article
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Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
objectivesNatural language processing (NLP) applications typically use regular expressions that have been developed manually by human experts. Our goal is to automate both the creation and utilization of regular expressions in text classification.
methodsWe designed a novel regular expression discovery (RED) algorithm and implemented two text classifiers based on RED. The RED+ALIGN classifier combines RED with an alignment algorithm, and RED+SVM combines RED with a support vector machine (SVM) classifier. Two clinical datasets were used for testing and evaluation: the SMOKE dataset, containing 1091 text snippets describing smoking status; and the PAIN dataset, containing 702 snippets describing pain status. We performed 10-fold cross-validation to calculate accuracy, precision, recall, and F-measure metrics. In the evaluation, an SVM classifier was trained as the control.
resultsThe two RED classifiers achieved 80.9-83.0% in overall accuracy on the two datasets, which is 1.3-3% higher than SVM's accuracy (p<0.001). Similarly, small but consistent improvements have been observed in precision, recall, and F-measure when RED classifiers are compared with SVM alone. More significantly, RED+ALIGN correctly classified many instances that were misclassified by the SVM classifier (8.1-10.3% of the total instances and 43.8-53.0% of SVM's misclassifications).
conclusionsMachine-generated regular expressions can be effectively used in clinical text classification. The regular expression-based classifier can be combined with other classifiers, like SVM, to improve classification performance.
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