ArticleInternational journal of medical informatics2018
Natural language processing of clinical notes for identification of critical limb ischemia.
Article in International journal of medical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 2 of them syntheses that pooled it.
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Who cites it
40 citing papers in PubMed, 2 syntheses or guidelines pooled it, 96 citations in OpenAlex.
- Diversity in Machine Learning: A Systematic Review of Text-Based Diagnostic Applications.Applied clinical informatics · 2022Pooled it
- Findings from the 2019 International Medical Informatics Association Yearbook Section on Health Information Management.Yearbook of medical informatics · 2019Pooled it
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- Integrating Medication Leaflets Utilizing FHIR and an LLM-Based Question-Answer Pipeline in a Mobile Application.Journal of medical systems · 2026Article
- Natural Language Processing framework for identifying abdominal aortic aneurysm repairs using unstructured electronic health records.Scientific reports · 2025Article
- Clinical applications of large language models in medicine and surgery: A scoping review.The Journal of international medical research · 2025Article
- Applying Natural Language Processing to Textual Data From Clinical Data Warehouses: Systematic Review.JMIR medical informatics · 2023Review
- Can Natural Language Processing and Artificial Intelligence Automate The Generation of Billing Codes From Operative Note Dictations?Global spine journal · 2023Article
- Supervised Text Classification System Detects Fontan Patients in Electronic Records With Higher Accuracy ThanJournal of the American Heart Association · 2023Article
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- Near Real-time Natural Language Processing for the Extraction of Abdominal Aortic Aneurysm Diagnoses From Radiology Reports: Algorithm Development and Validation Study.JMIR medical informatics · 2023Article
- Opportunities and challenges in application of artificial intelligence in pharmacology.Pharmacological reports : PR · 2023Review
- Comprehensive Review of Natural Language Processing (NLP) in Vascular Surgery.EJVES vascular forum · 2023Review
- Natural language processing for identification of hypertrophic cardiomyopathy patients from cardiac magnetic resonance reports.BMC medical informatics and decision making · 2022Article
- The Association of Cannabis Use Disorder with Acute Limb Ischemia and Critical Limb Ischemia.Vascular and endovascular surgery · 2022Article
- Evaluation of Natural Language Processing for the Identification of Crohn Disease-Related Variables in Spanish Electronic Health Records: A Validation Study for the PREMONITION-CD Project.JMIR medical informatics · 2022Article
- Artificial intelligence in atherosclerotic disease: Applications and trends.Frontiers in cardiovascular medicine · 2022Review
- Cohort Identification from Free-Text Clinical Notes Using SNOMED CT's Hierarchical Semantic Relations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- Development and Validation of an Automatic System for Intracerebral Hemorrhage Medical Text Recognition and Treatment Plan Output.Frontiers in aging neuroscience · 2022Article
- Improving broad-coverage medical entity linking with semantic type prediction and large-scale datasets.Journal of biomedical informatics · 2021Article
Corrections and comments
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
Abstract
backgroundCritical limb ischemia (CLI) is a complication of advanced peripheral artery disease (PAD) with diagnosis based on the presence of clinical signs and symptoms. However, automated identification of cases from electronic health records (EHRs) is challenging due to absence of a single definitive International Classification of Diseases (ICD-9 or ICD-10) code for CLI. METHODS AND
resultsIn this study, we extend a previously validated natural language processing (NLP) algorithm for PAD identification to develop and validate a subphenotyping NLP algorithm (CLI-NLP) for identification of CLI cases from clinical notes. We compared performance of the CLI-NLP algorithm with CLI-related ICD-9 billing codes. The gold standard for validation was human abstraction of clinical notes from EHRs. Compared to billing codes the CLI-NLP algorithm had higher positive predictive value (PPV) (CLI-NLP 96%, billing codes 67%, p < 0.001), specificity (CLI-NLP 98%, billing codes 74%, p < 0.001) and F1-score (CLI-NLP 90%, billing codes 76%, p < 0.001). The sensitivity of these two methods was similar (CLI-NLP 84%; billing codes 88%; p < 0.12).
conclusionsThe CLI-NLP algorithm for identification of CLI from narrative clinical notes in an EHR had excellent PPV and has potential for translation to patient care as it will enable automated identification of CLI cases for quality projects, clinical decision support tools and support a learning healthcare system.
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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.