An In-Depth Study of Personalized Anesthesia Management Models in Gastrointestinal Endoscopy Based on Multimodal Deep Learning

In response to the annual occurrence of over 10 million gastrointestinal endoscopic examinations in China, this study proposes a personalized anesthesia management model based on multimodal deep learning. This model was designed to enhance anesthesia management efficiency and disease detection rates...

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Veröffentlicht in:	IEEE access 2025, Vol.13, p.15410-15429
Hauptverfasser:	Shi, Hanqi, Wang, Hongyu, Ding, Xibing, Dang, Zheng
Format:	Artikel
Sprache:	eng
Schlagworte:	Anesthesia Anesthesiology Artificial neural networks Body size Customization Data models Deep learning Drugs Endoscopes Endoscopy Gastrointestinal endoscopy Gastrointestinal tract hyperparameter optimization Lead time Long short term memory LSTM networks Machine learning Parameters Physiology Predictive models Real time real-time prediction Sparse matrices sparse matrix classifier Sparsity Support vector machines Surgery
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