ArticleFrontiers in public health2022
Application of machine learning and natural language processing for predicting stroke-associated pneumonia.
Article in Frontiers in public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 17 citations in OpenAlex.
- European stroke organisation guideline on stroke-associated pneumonia.European stroke journal · 2026Guideline
- Procalcitonin and biomarkers for stroke-associated pneumonia: a systematic review and meta-analysis.BMC pulmonary medicine · 2025Pooled it
- Machine learning prediction of long-term postoperative pneumonia risk: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
- A Decade of Artificial Intelligence in Stroke Care (2015-2025): Trends, Clinical Translation, and the Precision Medicine Frontier-A Narrative Review.Journal of personalized medicine · 2026Review
- Construction and multi-center validation of a model based on blending ensemble learning for predicting stroke-associated pneumonia following acute ischemic stroke.Frontiers in neurology · 2026Article
- Association between thyroid hormone sensitivity and ischemic stroke-associated pneumonia: The role of FT3/FT4 ratio.PloS one · 2025Article
- Predictive modelling of hospital-acquired infection in acute ischemic stroke using machine learning.Scientific reports · 2024Article
- Characteristics and risk of stroke in emergency department patients with acute dizziness.Heliyon · 2024Article
- Ventilator-Associated Pneumonia Prediction Models Based on AI: Scoping Review.JMIR medical informatics · 2024Article
- Machine learning using multimodal and autonomic nervous system parameters predicts clinically apparent stroke-associated pneumonia in a development and testing study.Journal of neurology · 2024Article
- Article
- Building a SHAP interpretable prediction model for stroke-associated pneumonia [stroke-associated pneumonia (SAP)] in patients with aneurysmal subarachnoid hemorrhage (aSAH).Frontiers in neurologyArticle
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Identifying patients at high risk of stroke-associated pneumonia (SAP) may permit targeting potential interventions to reduce its incidence. We aimed to explore the functionality of machine learning (ML) and natural language processing techniques on structured data and unstructured clinical text to predict SAP by comparing it to conventional risk scores. Methods: Linked data between a hospital stroke registry and a deidentified research-based database including electronic health records and administrative claims data was used. Natural language processing was applied to extract textual features from clinical notes. The random forest algorithm was used to build ML models. The predictive performance of ML models was compared with the A Results: Among 5,913 acute stroke patients hospitalized between Oct 2010 and Sep 2021, 450 (7.6%) developed SAP within the first 7 days after stroke onset. The ML model based on both textual features and structured variables had the highest AUC [0.840, 95% confidence interval (CI) 0.806-0.875], significantly higher than those of the ML model based on structured variables alone (0.828, 95% CI 0.793-0.863, Conclusions: The ML model based on both textural features and structured variables performed better than conventional risk scores in predicting SAP. The workflow used to generate ML prediction models can be disseminated for local adaptation by individual healthcare organizations.
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