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

ABSTRACT:

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 classi

SUBMITTER: Zhang C 

PROVIDER: S-EPMC11457357 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature