用量子支持向量机提升老年人压力检测准确率，减少漏诊风险。
Quantum Hybrid Support Vector Machines for Stress Detection in Older Adults
- 将压力检测转为异常检测，结合皮质醇水平标注数据。
- 40人实验显示量子模型准确率更高，召回率显著优于传统方法。
- 适合关注医疗异常检测与量子机器学习应用的研究者。
压力会增加老年人认知障碍风险并降低生活质量。智能医疗可借助量子机器学习实现预防性与诊断性支持。本文提出一种新颖方法，将压力检测建模为异常检测问题，采用量子混合支持向量机。通过可穿戴智能手表采集基线生理信号作为正常数据，应激状态信号作为异常数据，并以皮质醇浓度为真实标签。利用基于核函数的预处理技术探索复杂特征空间。在40名老年人中，借助TSST刺激协议进行实验验证。结果表明，在有限特征条件下，量子机器学习相比经典方法具有更高准确率；同时，量子方法召回率更优，说明其在医疗场景中更少遗漏异常，有助于及时诊断与干预。
原文摘要 · Abstract (English)
Stress can increase the possibility of cognitive impairment and decrease the quality of life in older adults. Smart healthcare can deploy quantum machine learning to enable preventive and diagnostic support. This work introduces a unique technique to address stress detection as an anomaly detection problem that uses quantum hybrid support vector machines. With the help of a wearable smartwatch, we mapped baseline sensor reading as normal data and stressed sensor reading as anomaly data using cortisol concentration as the ground truth. We have used quantum computing techniques to explore the complex feature spaces with kernel-based preprocessing. We illustrate the usefulness of our method by doing experimental validation on 40 older adults with the help of the TSST protocol. Our findings highlight that using a limited number of features, quantum machine learning provides improved accuracy compared to classical methods. We also observed that the recall value using quantum machine learning is higher compared to the classical method. The higher recall value illustrates the potential of quantum machine learning in healthcare, as missing anomalies could result in delayed diagnostics or treatment.
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