用可穿戴设备心率数据，提前1-4小时预测败血症。
Early Prediction of Sepsis using Heart Rate Signals and Genetic Optimized LSTM Algorithm
- 用遗传算法优化LSTM模型，适配可穿戴设备性能。
- 在非病房环境实现1小时至4小时的早期预警，准确率高。
- 适合重症监护外的居家或普通病房患者监测使用。
败血症是由感染引发的免疫系统失调，导致高死亡率、高致残率及高昂医疗成本。及时预测败血症进展对通过早期干预降低不良后果至关重要。尽管已有大量针对重症监护室（ICU）患者的预测模型，但在非病房环境中的早期检测仍存在明显空白。本研究提出并评估了四种新型机器学习算法，通过分析可穿戴设备的心率数据，实现败血症发病的早期预测。模型架构经遗传算法优化，兼顾性能、计算复杂度与内存需求。各模型在1小时预测窗口下表现良好，后通过迁移学习扩展至4小时预测窗口。研究结果表明，该方法具备在非ICU和非病房环境中借助可穿戴技术实现早期败血症检测的潜力。
原文摘要 · Abstract (English)
Sepsis, characterized by a dysregulated immune response to infection, results in significant mortality, morbidity, and healthcare costs. The timely prediction of sepsis progression is crucial for reducing adverse outcomes through early intervention. Despite the development of numerous models for Intensive Care Unit (ICU) patients, there remains a notable gap in approaches for the early detection of sepsis in non-ward settings. This research introduces and evaluates four novel machine learning algorithms designed for predicting the onset of sepsis on wearable devices by analyzing heart rate data. The architecture of these models was refined through a genetic algorithm, optimizing for performance, computational complexity, and memory requirements. Performance metrics were subsequently extracted for each model to evaluate their feasibility for implementation on wearable devices capable of accurate heart rate monitoring. The models were initially tailored for a prediction window of one hour, later extended to four hours through transfer learning. The encouraging outcomes of this study suggest the potential for wearable technology to facilitate early sepsis detection outside ICU and ward environments.
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