用呼吸机数据实时预测慢阻肺急性加重，提升预警速度。
Time-Aware Tranformer-Based Prediction Model for AECOPD
- 基于时间感知Transformer建模呼吸数据时序特征。
- 在多分类任务中优于传统方法，提升预测准确率。
- 适合居家监测场景，减少临床数据延迟问题。
慢性阻塞性肺疾病急性加重（AECOPD）症状变化迅速，亟需具备时间敏感性的预测模型。然而，当前多数机器学习模型依赖临床与实验室数据，不可避免引入延迟。为实现AECOPD的及时检测并最小化延迟，本文聚焦于仅可获取日常使用呼吸机所记录的呼吸数据的居家监测场景。提出一种时间感知Transformer-based AECOPD预测模型，利用时间感知Transformer捕捉呼吸数据中的症状及其随时间演变的动态特征，生成有意义的患者表征。实验结果表明，该方法在多个分类任务中均优于传统方法，展现出显著提升的预测准确性，具有实际应用潜力。
原文摘要 · Abstract (English)
The rapid symptom change of Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) makes it critical to have time-sensitive prediction models. However, most current machine learning models studying AECOPD use clinical and laboratory data, which will inevitably cause latency. To ensure timely detection of AECOPD and minimize latency, this paper focuses on home monitoring scenarios where only respiratory data from daily-use ventilators is available. We introduce a Time-Aware transformer-based AECOPD prediction model, which generates meaningful patient representations using the Time-Aware transformer to capture the symptoms and their temporal progression in ventilator data. Our experimental results demonstrate that our Time-Aware transformer-based approach outperforms traditional methods in multiple classification tasks, highlighting its potential to enhance AECOPD prediction accuracy.
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