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Acute-Phase Machine Learning Prediction of 12-Month Aphasia and Discourse Recovery.

Manuel Jose Marte, Mathew Chaves, Lindsey Kelly

Medrxiv : the Preprint Server for Health Sciences

|May 25, 2026

Summary

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Machine learning models using acute stroke data can predict 12-month aphasia resolution and connected-speech recovery. Early prediction aids rehabilitation and clinical trial enrichment.

Area of Science:

Neuroscience
Computational Linguistics
Medical Imaging

Background:

Aphasia affects 30-40% of stroke patients at 12 months.
Early forecasting of language recovery is crucial for guiding rehabilitation and clinical trial enrichment.
Current machine learning (ML) prediction models often rely on chronic-phase data, which is unavailable at the acute decision point.

Purpose of the Study:

To investigate whether acute-phase features can predict 12-month language recovery outcomes after ischemic stroke.
To determine if global aphasia severity and connected-speech recovery share common substrates within an ML framework.
To develop and validate ML models for forecasting aphasia resolution and discourse normalization using acute clinical and imaging data.

Main Methods:

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Related Experiment Videos

Last Updated: May 26, 2026

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia

Utilizing Repetitive Transcranial Magnetic Stimulation to Improve Language Function in Stroke Patients with Chronic Non-fluent Aphasia

Published on: July 2, 2013

Neuronavigation-guided Repetitive Transcranial Magnetic Stimulation for Aphasia

Neuronavigation-guided Repetitive Transcranial Magnetic Stimulation for Aphasia

Published on: May 6, 2016

Studied 73 patients with acute left-hemisphere ischemic stroke and aphasia.

Defined 12-month outcomes as aphasia resolution (Western Aphasia Battery-Revised Aphasia Quotient [WAB-AQ] ≥93.8) and discourse normalization (Modern Cookie Theft content units ≥22.1).

Trained four ML algorithms on hierarchical feature sets (clinical, volumetric, anatomical, network-disconnection) using nested cross-validation and SHapley Additive exPlanations (SHAP) stability analysis.

Main Results:

Acute WAB-AQ was the dominant predictor for aphasia resolution (mean |SHAP| = 13.60).
Random forest model achieved high accuracy for aphasia resolution (F1 = 0.874), with clinical features alone yielding F1 = 0.851.
Support vector regression model achieved moderate accuracy for discourse normalization (F1 = 0.725), with shared predictors including acute WAB-AQ, lesion volume, and left pars triangularis.

Conclusions:

Acute-phase ML models can accurately forecast 12-month aphasia resolution and modestly forecast discourse normalization.
Clinical features account for the majority of predictive variance in language recovery.
Acute imaging data reveals shared and outcome-specific neural substrates, supporting early patient stratification for rehabilitation and clinical trials.