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Machine Learning-Based Predictive Model for Functional Independence in Spinal Cord Injury: Protocol for a Predictive

Alberto Isaac Perez-Sanpablo¹, Marlene Alejandra Rodriguez-Barragan², Alicia Meneses-Peñaloza³

¹Motion Analysis and Rehabilitation Engineering Lab, Instituto Nacional de Rehabilitación, Mexico City, Mexico.

JMIR Research Protocols

|July 3, 2026

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This summary is machine-generated.

This study develops machine learning (ML) models to predict functional independence after spinal cord injury (SCI). These models aim to improve rehabilitation planning and patient outcomes by providing accurate predictions of recovery trajectories.

Area of Science:

Develops and validates machine learning (ML) models for predicting functional independence in spinal cord injury (SCI) patients.
Focuses on improving rehabilitation goal setting and resource allocation through accurate prognostic tools.

Background:

Spinal cord injury (SCI) affects millions globally, with functional independence being a key rehabilitation objective.
Existing prognostic tools like the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) have limitations in predicting independence.
Limited access to advanced diagnostics and the inadequacy of current models hinder effective goal setting and planning in SCI care.

Purpose of the Study:

To develop and validate ML-based rules for predicting functional independence (measured by SCIM-III) at 3, 6, and 12 months post-SCI.
To integrate readily available clinical admission predictors to enhance the accuracy and generalizability of the prediction models.

Keywords:

SCIM-III artificial intelligence functional independence machine learning predictive modeling prognosis rehabilitation spinal cord injury trunk control

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To create a clinically applicable tool that overcomes the limitations of existing prognostic models in SCI.

Main Methods:

Utilized retrospective electronic health records (2015-2026) for model development and a prospective cohort for external validation.
Compared six ML architectures: linear regression, classification and regression tree, categorical boosting, light gradient boosting machine, multilayer perceptron, and Gaussian process regression.
Employed 10-fold cross-validation and assessed performance using metrics like RMSE, MAE, R², AUC, calibration plots, and decision curve analysis.

Main Results:

The study is in progress, with retrospective data collection and ML model development planned through March 2027.
Prospective external validation is scheduled from October 2026 to March 2028.
Results are anticipated by January 2028, with performance to be benchmarked against existing SCI prediction rules.

Conclusions:

Aims to deliver a reliable, clinically actionable ML-based prediction tool for estimating SCIM-III trajectories in SCI.
The tool will support enhanced goal setting, rehabilitation planning, and resource allocation.
Clinical utility will be determined by outperforming baseline and existing prediction rules on the prospective validation cohort.