ArticleJournal of medical Internet research2025
Stroke Diagnosis and Prediction Tool Using ChatGLM: Development and Validation Study.
Article in Journal of medical Internet research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Fine-Tuning, Retrieval-Augmented Generation, and Hybrid Adaptation of Language Models for Clinical Decision-Making in Health Care: Systematic Review.Journal of medical Internet research · 2026Pooled it
- ShenNongDiet: a medicine diet recommendation system based on graph-semantic hybrid retrieval and evidence constraint generation.Chinese medicine · 2026Article
- Large language models as a screening tool for the qualification of patients with Parkinson's disease for device-aided therapies.Journal of neural transmission (Vienna, Austria : 1996) · 2026Article
- Towards trustworthy brain stroke diagnosis using a lightweight explainable deep learning framework for CT imaging.Scientific reports · 2026Article
- Systematic Review of Large Language Models and Natural Language Processing in Stroke Care: Applications, Challenges, and Future Directions.Stroke (Hoboken, N.J.) · 2026Review
- An interpretable machine learning model for predicting stroke in patients with hypertension: insights from the SPRINT.Scientific reports · 2026Article
- Virtual patients and standardized patients combined training is associated with improved clinical reasoning among medical students.Frontiers in medicine · 2026Article
- Malignant cerebral edema after endovascular thrombectomy: a multimodal prediction model based on post-thrombectomy cerebral hyperdensity and natural language processing.Frontiers in medicine · 2026Article
- Accuracy of Large Language Models to Identify Stroke Subtypes Within Unstructured Electronic Health Record Data.Stroke · 2025Article
- Performance evaluation of large language models in pediatric nephrology clinical decision support: a comprehensive assessment.Pediatric nephrology (Berlin, Germany) · 2025Article
- Application of Large Language Models in Stroke Rehabilitation Health Education: 2-Phase Study.Journal of medical Internet research · 2025Article
- Astragaloside IV is a potential natural neuroprotective agent for stroke: a review.Frontiers in pharmacology · 2025Review
- Large language models for neurology: a mini review.Frontiers in digital health · 2025Review
- Performance and usability of retrieval-augmented large language models for stroke patient and caregiver support.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundStroke is a globally prevalent disease that imposes a significant burden on health care systems and national economies. Accurate and rapid stroke diagnosis can substantially increase reperfusion rates, mitigate disability, and reduce mortality. However, there are considerable discrepancies in the diagnosis and treatment of acute stroke.
objectiveThe aim of this study is to develop and validate a stroke diagnosis and prediction tool using ChatGLM-6B, which uses free-text information from electronic health records in conjunction with noncontrast computed tomography (NCCT) reports to enhance stroke detection and treatment.
methodsA large language model (LLM) using ChatGLM-6B was proposed to facilitate stroke diagnosis by identifying optimal input combinations, using external tools, and applying instruction tuning and low-rank adaptation (LoRA) techniques. A dataset containing details of 1885 patients with and those without stroke from 2016 to 2024 was used for training and internal validation; another 335 patients from two hospitals were used as an external test set, including 230 patients from the training hospital but admitted at different periods, and 105 patients from another hospital.
resultsThe LLM, which is based on clinical notes and NCCT, demonstrates exceptionally high accuracy in stroke diagnosis, achieving 99% in the internal validation dataset and 95.5% and 79.1% in two external test cohorts. It effectively distinguishes between ischemia and hemorrhage, with an accuracy of 100% in the validation dataset and 99.1% and 97.1% in the other test cohorts. In addition, it identifies large vessel occlusions (LVO) with an accuracy of 80% in the validation dataset and 88.6% and 83.3% in the other test cohorts. Furthermore, it screens patients eligible for intravenous thrombolysis (IVT) with an accuracy of 89.4% in the validation dataset and 60% and 80% in the other test cohorts.
conclusionsWe developed an LLM that leverages clinical text and NCCT to identify strokes and guide recanalization therapy. While our results necessitate validation through widespread deployment, they hold the potential to enhance stroke identification and reduce reperfusion time.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.