A Comprehensive Review on - Machine Learning in Bio-Signal Analysis for Chronic Pain Research

Sabeenian, R.S.; Vinodhini, C.M.

doi:10.1109/icercs57948.2023.10434197

2023 International Conference on Emerging Research in Computational Science (ICERCS) 2023

DOI: 10.1109/icercs57948.2023.10434197

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A Comprehensive Review on - Machine Learning in Bio-Signal Analysis for Chronic Pain Research

R.S. Sabeenian,

C.M. Vinodhini

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2024

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Deep learning models for the prediction of acute postoperative pain in PACU for video‐assisted thoracoscopic surgery

Zhang,

He,

Liang

et al. 2024

BMC Med Res Methodol

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Background Postoperative pain is a prevalent symptom experienced by patients undergoing surgical procedures. This study aims to develop deep learning algorithms for predicting acute postoperative pain using both essential patient details and real-time vital sign data during surgery. Methods Through a retrospective observational approach, we utilized Graph Attention Networks (GAT) and graph Transformer Networks (GTN) deep learning algorithms to construct the DoseFormer model while incorporating an attention mechanism. This model employed patient information and intraoperative vital signs obtained during Video-assisted thoracoscopic surgery (VATS) surgery to anticipate postoperative pain. By categorizing the static and dynamic data, the DoseFormer model performed binary classification to predict the likelihood of postoperative acute pain. Results A total of 1758 patients were initially included, with 1552 patients after data cleaning. These patients were then divided into training set ( n = 931) and testing set ( n = 621). In the testing set, the DoseFormer model exhibited significantly higher AUROC (0.98) compared to classical machine learning algorithms. Furthermore, the DoseFormer model displayed a significantly higher F1 value (0.85) in comparison to other classical machine learning algorithms. Notably, the attending anesthesiologists' F1 values (attending: 0.49, fellow: 0.43, Resident: 0.16) were significantly lower than those of the DoseFormer model in predicting acute postoperative pain. Conclusions Deep learning model can predict postoperative acute pain events based on patients' basic information and intraoperative vital signs. Supplementary Information The online version contains supplementary material available at 10.1186/s12874-024-02357-5.

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Deep learning models for the prediction of acute postoperative pain in PACU for video‐assisted thoracoscopic surgery

Zhang,

He,

Liang

et al. 2024

BMC Med Res Methodol

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show abstract

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A Comprehensive Review on - Machine Learning in Bio-Signal Analysis for Chronic Pain Research

Cited by 1 publication

References 60 publications

Deep learning models for the prediction of acute postoperative pain in PACU for video‐assisted thoracoscopic surgery

Deep learning models for the prediction of acute postoperative pain in PACU for video‐assisted thoracoscopic surgery

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