A novel intelligent hybrid reinforcement learning framework for autonomous decision making in complex health cognitive systems.

Overview

1. Center for Computing Research, Instituto Politécnico Nacional, Av. Juan de Dios Batiz, s/n, 07320 Mexico City, Mexico
2. Department of Computer Science, Bahria University Lahore Campus, Lahore, 54600 Pakistan
3. Department of Allied Health Science, Superior University, Lahore, 54000 Pakistan

Abstract

Existing reinforcement learning (RL) approaches struggle to balance real-time decision-making with adaptive learning in dynamic healthcare environments. We propose a brain-inspired hybrid RL framework that integrates model-based (MB) planning and model-free (MF) reflexes via a dynamic meta-controller, neuro-symbolic clinical knowledge, counterfactual reasoning, and ethical safeguards. The framework is validated on a multimodal cerebral palsy (CP) dataset (86 patients) using NetLogo multi-agent simulations and Weka classifiers. A combined reward mechanism achieves 99% total reward accumulation, with 98% optimal reward in 95% of training episodes. Component analysis shows a 60% MB / 40% MF contribution, yielding a 15% improvement over standalone methods. Optimal weighting (0.7 MB, 0.3 MF) further enhances performance. External zero-shot validation on three public datasets (NTNU-HARChildren, EEG-EMG exoskeleton, D4RL) confirms generalizability (macro F1 84.3%, accuracy 81.7%, D4RL scores 68.5 and 62.3). Regression methods achieve correlation coefficients up to 0.94, and classification models (multinomial Naïve Bayes, logistic regression) attain 100% precision, recall, and F-measure. The framework provides a reliable, explainable, and simulation-validated solution for patient-centric autonomous decision-making.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

Code availability

The code implementing the proposed framework is available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• doi:10.17632/8x4vkhy753.1, at the source; found in the references

Data availability

The dataset is not publicly available but can be accessed for research purposes by requesting data access from the University of Lahore Teaching Hospital. The hospital’s research committee approved the use of the dataset, which meets ethical standards. The dataset is fully anonymized.

Reproduced under the paper's license (CC BY), from the paper cited above.

Data Availability Statement

The dataset is not publicly available but can be accessed for research purposes by requesting data access from the University of Lahore Teaching Hospital. The hospital’s research committee approved the use of the dataset, which meets ethical standards. The dataset is fully anonymized.

All materials used in this study are available from the corresponding author upon reasonable request.

The code implementing the proposed framework is available from the corresponding author upon reasonable request.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

• Funding: added Instituto Politécnico Nacional

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 14 keywords, 5 MeSH terms, 42 references.

Cite

This paper

Abdullah, Fatima, Z., Ather, M. A., & Rodríguez, J. L. O. (2026). A novel intelligent hybrid reinforcement learning framework for autonomous decision making in complex health cognitive systems. Scientific reports, 16(1), 14721. https://doi.org/10.1038/s41598-026-50418-0

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