Evolutionary-based deep learning network model using adaptive mixing differential evolution and application in acute pulmonary embolism

Mingjing Wang; Hao ShangGuan; Yang Yang; Yeqi Shou; Lizhi Shao; Yazhou Ji; Ali Asghar Heidari; Huiling Chen; Peiliang Wu

doi:10.1016/j.jare.2026.03.009

Evolutionary-based deep learning network model using adaptive mixing differential evolution and application in acute pulmonary embolism

J Adv Res. 2026 Mar 12:S2090-1232(26)00228-6. doi: 10.1016/j.jare.2026.03.009. Online ahead of print.

Authors

Mingjing Wang¹, Hao ShangGuan², Yang Yang³, Yeqi Shou⁴, Lizhi Shao⁵, Yazhou Ji⁶, Ali Asghar Heidari⁷, Huiling Chen⁸, Peiliang Wu³

Affiliations

¹ School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Wenzhou 325000, China.
² Wenzhou Medical University Second Clinical Medical College, Wenzhou 325000, China.
³ Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, China.
⁴ Department of Endocrinology, The Affiliated Hospital of Shaoxing University, Shaoxing 312099, China.
⁵ School of Internet Anhui University, Anhui 230039, China.
⁶ Department of Hematology, The Affiliated Huai'an No. 1 People's Hospital of Nanjing Medical University, Huai'an, China.
⁷ School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran 1417935840, Iran.
⁸ College of Computer Science and Artificial Intelligence, Wenzhou University, Wenzhou, Zhejiang 325035, China.

PMID: 41831678
DOI: 10.1016/j.jare.2026.03.009

Abstract

Introduction: Acute pulmonary embolism (APE) is characterized by high incidence and mortality, along with non-specific clinical manifestations. Its common symptoms such as dyspnea, chest pain, cough, and hemoptysis can also appear in other diseases, frequently resulting in the oversight of APE patients and raising the risk of misdiagnosis and mortality. Current clinical risk stratification for pulmonary embolism usually depends on hemodynamic evaluation, the pulmonary embolism severity index, echocardiography, and myocardial injury markers. However, these assessment methods tend to be complex, time-consuming, invasive, and lack repeatability. Therefore, developing a more efficient and accurate tool for APE prediction and analysis is crucial.

Objectives: To achieve precise prediction and analysis of APE patients using accessible clinical data, we developed an evolutionary-based deep learning network AlexNet model (EDLAlexNet) that leverages blood biochemical indices, vital signs, clinical parameters, and clinical characteristics. The goal is to provide a reliable clinical tool for the assessment and management of APE with high accuracy, specificity, sensitivity, and a favorable AUC.

Methods: We developed the EDLAlexNet model, which incorporates a novel evolutionary computation method called adaptive mixing differential evolution (MIXDE) integrating Q-learning and opposition-based learning. The performance of the MIXDE algorithm was statistically validated on standard test datasets. Subsequently, the MIXDE-based EDLAlexNet was used to analyze data from intermediate-low-risk and high-risk pulmonary embolism patients.

Result: The results for APE using EDLAlexNet showed promising performance, achieving an accuracy of 93.76%, specificity of 89.46%, sensitivity of 95.74%, and an AUC of 0.9527. These outcomes demonstrate the model's effectiveness in precisely predicting and analyzing APE patients.

Conclusion: Overall, EDLAlexNet, which integrates the MIXDE algorithm, exhibits excellent performance in APE prediction and analysis. It shows potential as a valuable clinical tool for the assessment and management of APE, addressing the limitations of current assessment methods.

Keywords: Acute pulmonary embolism; Evolutionary-based deep learning network; Mixing differential evolution; Opposition-based Learning; Q-Learning.