Multi-label auxiliary decision-making model for pharmacotherapy of ischemic stroke based on BR-SA-LightGBM using real-world electronic medical records
4 affiliations
- 1School of Public Health, Hebei University, Baoding, Hebei, China
- 2School of Economics and Management, Hebei University of Technology, Tianjin, China
- 3Institute of Hospital Management, Cangzhou Central Hospital, Cangzhou, Hebei, China
- 4National Population Health Data Center, Institute of Medical Information, Chinese Academy of Medical Sciences, Beijing, China
Abstract
BackgroundIschemic stroke poses a substantial global health burden owing to its high prevalence, recurrence, disability, and mortality rates. Pharmacotherapy for ischemic stroke is highly heterogeneous across patients, with complex polypharmacy regimens and intricate interdependencies between drug classes. Precise, personalised medication recommendations are therefore indispensable for optimising therapeutic outcomes and improving patient prognosis.MethodsThis study framed pharmacotherapeutic decision-making for ischemic stroke as a multi-label learning task. Using retrospective electronic medical records from 7,335 ischemic stroke patients admitted to a single tertiary hospital (Cangzhou Central Hospital), we developed and evaluated an integrated model, termed BR-SA-LightGBM, which combines Binary Relevance (BR), Simulated Annealing (SA), and the Light Gradient Boosting Machine (LightGBM) to provide personalised recommendations across eight first-line drug categories.ResultsIn an independent test cohort (n = 2,200), the BR-SA-LightGBM model achieved an accuracy of 0.90, an F1-score of 0.87, and a markedly low Hamming loss of 0.09. It significantly outperformed ML-KNN, Power-Set, and the unoptimized BR-LightGBM model across all evaluation metrics (P < 0.05). Across drug classes, age, the number of comorbidities, atrial fibrillation, carotid plaque, and fibrinogen were among the most influential predictors of medication selection.ConclusionsThe BR-SA-LightGBM model established herein enables accurate, personalised, auxiliary decision support for drug prescribing in ischemic stroke. It is intended as an auxiliary decision-support tool that has the potential to reduce medication errors and omissions and may assist clinicians in improving clinical efficiency; it also provides a novel methodological framework for precision medicine in stroke care.
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BibTeX
@article{chen2026multilabel,
title = {{Multi-label auxiliary decision-making model for pharmacotherapy of ischemic stroke based on BR-SA-LightGBM using real-world electronic medical records}},
author = {Yueqi Chen and Chen Rui and Chen Rui and Shuaiyu Mu and Shengfa Zhang and Haoran Liu},
journal = {Frontiers in Medicine},
year = {2026},
month = oct,
doi = {10.3389/fmed.2026.1902875},
url = {https://doi.org/10.3389/fmed.2026.1902875},
note = {CC BY},
}
RIS
TY - JOUR
TI - Multi-label auxiliary decision-making model for pharmacotherapy of ischemic stroke based on BR-SA-LightGBM using real-world electronic medical records
AU - Yueqi Chen
AU - Chen Rui
AU - Chen Rui
AU - Shuaiyu Mu
AU - Shengfa Zhang
AU - Haoran Liu
PY - 2026
DA - 2026/10/27
JO - Frontiers in Medicine
DO - 10.3389/fmed.2026.1902875
UR - https://doi.org/10.3389/fmed.2026.1902875
AB - BackgroundIschemic stroke poses a substantial global health burden owing to its high prevalence, recurrence, disability, and mortality rates. Pharmacotherapy for ischemic stroke is highly heterogeneous across patients, with complex polypharmacy regimens and intricate interdependencies between drug classes. Precise, personalised medication recommendations are therefore indispensable for optimising therapeutic outcomes and improving patient prognosis.MethodsThis study framed pharmacotherapeutic decision-making for ischemic stroke as a multi-label learning task. Using retrospective electronic medical records from 7,335 ischemic stroke patients admitted to a single tertiary hospital (Cangzhou Central Hospital), we developed and evaluated an integrated model, termed BR-SA-LightGBM, which combines Binary Relevance (BR), Simulated Annealing (SA), and the Light Gradient Boosting Machine (LightGBM) to provide personalised recommendations across eight first-line drug categories.ResultsIn an independent test cohort (n = 2,200), the BR-SA-LightGBM model achieved an accuracy of 0.90, an F1-score of 0.87, and a markedly low Hamming loss of 0.09. It significantly outperformed ML-KNN, Power-Set, and the unoptimized BR-LightGBM model across all evaluation metrics (P < 0.05). Across drug classes, age, the number of comorbidities, atrial fibrillation, carotid plaque, and fibrinogen were among the most influential predictors of medication selection.ConclusionsThe BR-SA-LightGBM model established herein enables accurate, personalised, auxiliary decision support for drug prescribing in ischemic stroke. It is intended as an auxiliary decision-support tool that has the potential to reduce medication errors and omissions and may assist clinicians in improving clinical efficiency; it also provides a novel methodological framework for precision medicine in stroke care.
SN - 2296-858X
ER -