Article

DISCO: A Delta-NIHSS-Based Machine Learning Model for Predicting Recurrence, Disability, and Mortality Following Acute Ischemic Stroke

2025-05-13

Abstract excerpt

<h4>Background</h4> An accurate, robust, clinically accessible, and explainable predictive model for post-stroke composite outcomes could identify high-risk patients for targeted interventions. However, such a model is currently lacking. This study leverages artificial intelligence to develop and validate a predictive model for post-stroke outcomes at three months and over five years, leveraging comprehensive data...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 9fba89f8-cc3e-5021-8cea-b42160d9b853
DOI: 10.1101/2025.05.11.25326629

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

DISCO: A Delta-NIHSS-Based Machine Learning Model for Predicting Recurrence, Disability, and Mortality Following Acute Ischemic StrokeDOI 10.1101/2025.05.11.25326629Read

Development and validation of a machine learning-based risk prediction model for post-stroke cognitive impairmentSemantic proximity Read

Machine Learning-Based Prediction of 28-Day Mortality in Stroke Using the Nutrition-Immunity-Renal Index (NIRI): A Multicenter, Dual-Database Study of MIMIC-IV and eICUSemantic proximity Read

Stroke-SCORE: Personalizing Acute Ischemic Stroke Treatment to Improve Patient OutcomesSemantic proximity Read

AI-based Prediction of Imminent Primary Stroke on Claims Data Enables Accurate Patient StratificationSemantic proximity Read

Integrating Vascular, Neurological, and Metabolic Factors into a Validated Nomogram to Predict 90‑day Outcomes after Mechanical Thrombectomy for Acute Ischemic StrokeSemantic proximity Read

A novel clinical nomogram for predicting 3-month unfavorable outcomes of stroke patients treated with mechanical thrombectomySemantic proximity Read

Guideline-Guided Prognostic Models of Mortality Following First-Ever Ischemic StrokeSemantic proximity Read

Benchmarking Ensemble Machine Learning Algorithms for the Early Prediction of Stroke in Imbalanced Clinical Cohorts: A Comparative Analysis and Decision Curve AssessmentSemantic proximity Read

A Neutrophil-Incorporated Nomogram for Predicting 3-Month Disability after Intravenous Thrombolysis in Acute Ischemic StrokeSemantic proximity Read

iSPAN: Improved prediction of outcomes post thrombectomy with Machine LearningSemantic proximity Read

Dynamic Stroke Risk Stratification via Machine Learning: A Multi-Level Single-Center StudySemantic proximity Read

Predicting functional outcome in ischemic stroke patients using genetic, environmental, and clinical factors: a machine learning analysis of population-based prospective cohort study.Semantic proximity Read