A method for constructing anesthesia complication prediction model based on deep learning

The deep learning-based anesthesia complication prediction model solves the problems of multi-source heterogeneous data processing, real-time and interpretability in anesthesia clinical practice, realizes multimodal feature fusion, dynamic risk prediction and efficient edge deployment, and improves the prediction accuracy and safety in anesthesia scenarios.

CN120452673BActive Publication Date: 2025-09-23XIANYANG CITY SECOND PEOPLES HOSPITAL
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Patent Information

Application Number
CN202510888359.5
Authority / Receiving Office
CN · China
Patent Type
Patents(China)
Current Assignee / Owner
Filing Date
2025-06-30
Publication Date
2025-09-23
Estimated Expiration
2045-06-30

AI Technical Summary

Technical Problem

Existing technologies in anesthesia clinics have limited data processing capabilities, insufficient real-time predictions, weak model interpretability, data imbalance, and low edge deployment efficiency. In particular, they are unable to meet the requirements in terms of multi-source heterogeneous data integration, long-term dependency capture, adaptive adjustment, model generalization capabilities, and edge computing latency.

Method used

A deep learning-based anesthesia complication prediction model is adopted, which realizes real-time prediction and efficient deployment through a multimodal feature fusion module, a dynamic risk trajectory prediction mechanism, an explainable design and edge computing optimization, combined with multi-source heterogeneous data processing, dynamic risk trajectory prediction, edge deployment and clinical decision-making interface.

Benefits of technology

It achieves deep semantic fusion of multi-source data and dynamic risk prediction, improves the prediction accuracy and real-time performance of the model, enhances interpretability, reduces the computing load of edge devices, and builds a closed-loop anesthesia complication early warning and treatment system.

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Abstract

The present invention relates to the field of medical system technology and specifically discloses a method for constructing an anesthesia complication prediction model based on deep learning, comprising the following steps: S1: acquiring multi-source heterogeneous anesthesia medical data; S2: constructing a multimodal feature fusion module; S3: designing a hierarchical deep neural network architecture, comprising subnetworks for parallel processing of data from different modalities and a fully connected prediction layer for fusing multimodal features; S4: employing a dynamic risk trajectory prediction mechanism to continuously output complication probability curves using a sliding time window, rather than a single static prediction result; and S5: deploying a clinical real-time decision interface to map the prediction results to the anesthesia monitoring equipment alarm system in real time. The method employs a bidirectional LSTM+1D-CNN hybrid encoder and a cross-modal attention mechanism to synchronously capture the temporal dependencies of physiological signals and the spatiotemporal characteristics of operational events, achieving deep semantic fusion of multi-source data and improving the model's ability to characterize complication precursor features.
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