ArticleJournal of the American Heart Association2021
Natural Language Processing Enhances Prediction of Functional Outcome After Acute Ischemic Stroke.
Article in Journal of the American Heart Association, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning Applications in Imaging of Acute Ischemic Stroke: A Systematic Review and Narrative Summary.Radiology · 2025Pooled it
- Trial
- Multilingual Evidence-Based Question-Answering for Stroke Discharge Summaries: Study of Cross-Lingual Heterogeneity in Clinical Reports.Journal of medical Internet research · 2026Article
- Systematic Review of Large Language Models and Natural Language Processing in Stroke Care: Applications, Challenges, and Future Directions.Stroke (Hoboken, N.J.) · 2026Review
- Speech analysis for differentiating bipolar disorder and major depressive disorder during euthymic states.Annals of general psychiatry · 2026Article
- RehabMate: an explainable framework for action detection and corrective feedback in pediatric stroke rehabilitation.Scientific reports · 2026Article
- Natural language processing for triage of cerebral large-vessel occlusion.Arquivos de neuro-psiquiatria · 2025Article
- The incremental value of unstructured data via natural language processing in machine learning-based COVID-19 mortality prediction: a comparative study.BMC medical informatics and decision making · 2025Article
- Disambiguating Clinical Abbreviations by One-to-All Classification: Algorithm Development and Validation Study.JMIR medical informatics · 2024Article
- Natural language processing systems for extracting information from electronic health records about activities of daily living. A systematic review.JAMIA open · 2024Review
- Direct Clinical Applications of Natural Language Processing in Common Neurological Disorders: Scoping Review.JMIR neurotechnology · 2024Review
- Optimization of Radiology Diagnostic Services for Patients with Stroke in Multidisciplinary Hospitals.Materia socio-medica · 2024Review
- Fusion Modeling: Combining Clinical and Imaging Data to Advance Cardiac Care.Circulation. Cardiovascular imaging · 2023Review
- From Admission to Discharge: Predicting National Institutes of Health Stroke Scale Progression in Stroke Patients Using Biomarkers and Explainable Machine Learning.Journal of personalized medicine · 2023Article
- Applications of Natural Language Processing for the Management of Stroke Disorders: Scoping Review.JMIR medical informatics · 2023Article
- Multimodal deep learning for COVID-19 prognosis prediction in the emergency department: a bi-centric study.Scientific reports · 2023Article
- Evaluation of Blood Biomarkers and Parameters for the Prediction of Stroke Survivors' Functional Outcome upon Discharge Utilizing Explainable Machine Learning.Diagnostics (Basel, Switzerland) · 2023Article
- Automated risk assessment of newly detected atrial fibrillation poststroke from electronic health record data using machine learning and natural language processing.Frontiers in cardiovascular medicine · 2022Article
- Application of machine learning and natural language processing for predicting stroke-associated pneumonia.Frontiers in public health · 2022Article
- Natural language processing (NLP)-assisted assessment of neurologic severity and functional outcomes in factor Xa inhibitor-related intracranial hemorrhages.Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background Conventional prognostic scores usually require predefined clinical variables to predict outcome. The advancement of natural language processing has made it feasible to derive meaning from unstructured data. We aimed to test whether using unstructured text in electronic health records can improve the prediction of functional outcome after acute ischemic stroke. Methods and Results Patients hospitalized for acute ischemic stroke were identified from 2 hospital stroke registries (3847 and 2668 patients, respectively). Prediction models developed using the first cohort were externally validated using the second cohort, and vice versa. Free text in the history of present illness and computed tomography reports was used to build machine learning models using natural language processing to predict poor functional outcome at 90 days poststroke. Four conventional prognostic models were used as baseline models. The area under the receiver operating characteristic curves of the model using history of present illness in the internal and external validation sets were 0.820 and 0.792, respectively, which were comparable to the National Institutes of Health Stroke Scale score (0.811 and 0.807). The model using computed tomography reports achieved area under the receiver operating characteristic curves of 0.758 and 0.658. Adding information from clinical text significantly improved the predictive performance of each baseline model in terms of area under the receiver operating characteristic curves, net reclassification improvement, and integrated discrimination improvement indices (all
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