基于时间感知Transformer的AECOPD预测模型
Time-Aware Tranformer-Based Prediction Model for AECOPD
AI总结：
针对AECOPD症状变化快、现有模型延迟高的问题，本文提出基于时间感知Transformer的预测模型，利用家用呼吸机数据实现更及时的AECOPD预测，性能优于传统方法。
AI中文摘要：
慢性阻塞性肺疾病急性加重（AECOPD）的症状变化迅速，因此具备时间敏感性的预测模型至关重要。然而，当前多数研究AECOPD的机器学习模型使用临床和实验室数据，这必然会导致延迟。为确保及时检测AECOPD并最小化延迟，本文聚焦于仅可获取家用呼吸机呼吸数据的家庭监测场景，提出了一种基于时间感知Transformer的AECOPD预测模型，该模型利用时间感知Transformer生成有意义的患者表征，以捕捉呼吸机数据中的症状及其时间进展。实验结果表明，本文提出的基于时间感知Transformer的方法在多项分类任务中均优于传统方法，凸显了其提升AECOPD预测准确性的潜力。
英文摘要：
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.
