ArticleJournal of the American Medical Informatics Association : JAMIA
Use of a support vector machine for categorizing free-text notes: assessment of accuracy across two institutions.
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 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Using Natural Language Processing to Measure and Improve Quality of Diabetes Care: A Systematic Review.Journal of diabetes science and technology · 2021Pooled it
- Automated Type 2 Diabetes Case and Control Identification from the MIMIC-IV Database.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- K-nearest-neighbor algorithm to predict the survival time and classification of various stages of oral cancer: a machine learning approach.F1000Research · 2022Article
- Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.JMIR medical informatics · 2019Review
- Natural language processing and machine learning algorithm to identify brain MRI reports with acute ischemic stroke.PloS one · 2019Article
- Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse.Journal of medical Internet research · 2018Article
- Automatic classification of RDoC positive valence severity with a neural network.Journal of biomedical informatics · 2017Article
- Uncovering Capgras delusion using a large-scale medical records database.BJPsych open · 2017Article
- Natural Language Processing for Cohort Discovery in a Discharge Prediction Model for the Neonatal ICU.Applied clinical informatics · 2016Article
- A systematic comparison of feature space effects on disease classifier performance for phenotype identification of five diseases.Journal of biomedical informatics · 2015Article
- Recent Advances in Clinical Natural Language Processing in Support of Semantic Analysis.Yearbook of medical informatics · 2015Review
- A novel method of adverse event detection can accurately identify venous thromboembolisms (VTEs) from narrative electronic health record data.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- Predicting Health Care Utilization After Behavioral Health Referral Using Natural Language Processing and Machine Learning.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2015Article
- Text classification for assisting moderators in online health communities.Journal of biomedical informatics · 2013Article
- N-gram support vector machines for scalable procedure and diagnosis classification, with applications to clinical free text data from the intensive care unit.Journal of the American Medical Informatics Association : JAMIAArticle
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElectronic health record (EHR) users must regularly review large amounts of data in order to make informed clinical decisions, and such review is time-consuming and often overwhelming. Technologies like automated summarization tools, EHR search engines and natural language processing have been shown to help clinicians manage this information.
objectiveTo develop a support vector machine (SVM)-based system for identifying EHR progress notes pertaining to diabetes, and to validate it at two institutions. MATERIALS AND
methodsWe retrieved 2000 EHR progress notes from patients with diabetes at the Brigham and Women's Hospital (1000 for training and 1000 for testing) and another 1000 notes from the University of Texas Physicians (for validation). We manually annotated all notes and trained a SVM using a bag of words approach. We then used the SVM on the testing and validation sets and evaluated its performance with the area under the curve (AUC) and F statistics.
resultsThe model accurately identified diabetes-related notes in both the Brigham and Women's Hospital testing set (AUC=0.956, F=0.934) and the external University of Texas Faculty Physicians validation set (AUC=0.947, F=0.935). DISCUSSION: Overall, the model we developed was quite accurate. Furthermore, it generalized, without loss of accuracy, to another institution with a different EHR and a distinct patient and provider population.
conclusionsIt is possible to use a SVM-based classifier to identify EHR progress notes pertaining to diabetes, and the model generalizes well.
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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.