Roche Data & Analytics Chapter (Data Science), Kaiseraugst, Switzerland.
Genentech, Inc, South San Francisco, CA, United States.
J Med Internet Res. 2024 Oct 4;26:e47879. doi: 10.2196/47879.
Machine learning offers quantitative pattern recognition analysis of wearable device data and has the potential to detect illness onset and monitor influenza-like illness (ILI) in patients who are infected.
This study aims to evaluate the ability of machine-learning algorithms to distinguish between participants who are influenza positive and influenza negative in a cohort of symptomatic patients with ILI using wearable sensor (activity) data and self-reported symptom data during the latent and early symptomatic periods of ILI.
This prospective observational cohort study used the extreme gradient boosting (XGBoost) classifier to determine whether a participant was influenza positive or negative based on 3 models using symptom-only data, activity-only data, and combined symptom and activity data. Data were collected from the Home Testing of Respiratory Illness (HTRI) study and FluStudy2020, both conducted between December 2019 and October 2020. The model was developed using the FluStudy2020 data and tested on the HTRI data. Analyses included participants in these studies with an at-home influenza diagnostic test result. Fitbit (Google LLC) devices were used to measure participants' steps, heart rate, and sleep parameters. Participants detailed their ILI symptoms, health care-seeking behaviors, and quality of life. Model performance was assessed by area under the curve (AUC), balanced accuracy, recall (sensitivity), specificity, precision (positive predictive value), negative predictive value, and weighted harmonic mean of precision and recall (F) score.
An influenza diagnostic test result was available for 953 and 925 participants in HTRI and FluStudy2020, respectively, of whom 848 (89%) and 840 (90.8%) had activity data. For the training and validation sets, the highest performing model was trained on the combined symptom and activity data (training AUC=0.77; validation AUC=0.74) versus symptom-only (training AUC=0.73; validation AUC=0.72) and activity-only (training AUC=0.68; validation AUC=0.65) data. For the FluStudy2020 test set, the performance of the model trained on combined symptom and activity data was closely aligned with that of the symptom-only model (combined symptom and activity test AUC=0.74; symptom-only test AUC=0.74). These results were validated using independent HTRI data (combined symptom and activity evaluation AUC=0.75; symptom-only evaluation AUC=0.74). The top features guiding influenza detection were cough; mean resting heart rate during main sleep; fever; total minutes in bed for the combined model; and fever, cough, and sore throat for the symptom-only model.
Machine-learning algorithms had moderate accuracy in detecting influenza, suggesting that previous findings from research-grade sensors tested in highly controlled experimental settings may not easily translate to scalable commercial-grade sensors. In the future, more advanced wearable sensors may improve their performance in the early detection and discrimination of viral respiratory infections.
机器学习提供了对可穿戴设备数据的定量模式识别分析，具有检测疾病发作和监测感染流感样疾病（ILI）患者的潜力。
本研究旨在评估机器学习算法在ILI 症状患者队列中使用可穿戴传感器（活动）数据和自我报告的症状数据，在 ILI 的潜伏和早期症状期区分流感阳性和流感阴性参与者的能力。
这项前瞻性观察队列研究使用极端梯度提升（XGBoost）分类器，根据仅使用症状数据、仅使用活动数据以及同时使用症状和活动数据的 3 种模型，确定参与者是流感阳性还是阴性。数据来自 Home Testing of Respiratory Illness（HTRI）研究和 FluStudy2020，均在 2019 年 12 月至 2020 年 10 月之间进行。该模型使用 FluStudy2020 数据进行开发，并在 HTRI 数据上进行了测试。分析包括这些研究中具有家庭流感诊断测试结果的参与者。Fitbit（谷歌有限责任公司）设备用于测量参与者的步数、心率和睡眠参数。参与者详细描述了他们的 ILI 症状、寻求医疗保健的行为和生活质量。通过曲线下面积（AUC）、平衡准确性、召回率（灵敏度）、特异性、精度（阳性预测值）、阴性预测值和精度和召回率的加权调和平均值（F）评分评估模型性能。
在 HTRI 和 FluStudy2020 中，分别有 953 名和 925 名参与者的流感诊断测试结果可用，其中 848 名（89%）和 840 名（90.8%）有活动数据。对于训练和验证集，表现最好的模型是基于症状和活动数据的组合（训练 AUC=0.77；验证 AUC=0.74），而不是仅基于症状（训练 AUC=0.73；验证 AUC=0.72）和仅基于活动（训练 AUC=0.68；验证 AUC=0.65）数据。对于 FluStudy2020 测试集，基于症状和活动数据的组合训练模型的性能与仅基于症状的模型非常接近（组合症状和活动测试 AUC=0.74；仅症状测试 AUC=0.74）。使用独立的 HTRI 数据对这些结果进行了验证（组合症状评估 AUC=0.75；仅症状评估 AUC=0.74）。指导流感检测的顶级功能是咳嗽；主要睡眠期间的平均静息心率；发烧；综合模型的总卧床时间；以及仅症状模型的发烧、咳嗽和喉咙痛。
机器学习算法在检测流感方面具有中等准确性，这表明以前在高度受控的实验环境中测试的研究级传感器的研究结果可能不容易转化为可扩展的商业级传感器。未来，更先进的可穿戴传感器可能会提高它们在病毒呼吸道感染的早期检测和鉴别中的性能。
