背景:产后抑郁症(PPD)对孕产妇健康构成重大挑战。目前检测PPD的方法依赖于亲自产后访视,这有助于诊断不足。此外,识别PPD症状可能具有挑战性。因此,我们探索了使用来自消费者可穿戴设备的数字生物标志物进行PPD识别的潜力.
目的:这项研究的主要目的是展示使用机器学习(ML)和与心率相关的数字生物标志物的可行性,身体活动,以及从消费级可穿戴设备中获得的能量消耗,以识别PPD。
方法:使用我们所有的研究计划注册等级v6数据集,我们对分娩后有和无PPD的女性进行了计算表型分析.使用Fitbit的数字生物标志物开发了个体ML模型,以辨别孕前,怀孕,产后没有抑郁症,和产后抑郁症(即,PPD诊断)期间。使用广义线性模型建立模型,随机森林,支持向量机,和k-最近邻算法,并使用κ统计量和接收器工作特性曲线下的多类面积(mAUC)进行评估,以确定具有最佳性能的算法。我们的个性化ML方法的特异性在一组分娩且未经历PPD的妇女中得到证实。此外,我们评估了既往抑郁史对模型表现的影响.我们使用Shapley加性解释确定了预测PPD期的变量重要性,并使用置换方法确认了结果。最后,我们将我们的个性化ML方法与传统的基于队列的ML模型进行了PPD识别,并使用灵敏度比较了模型性能,特异性,精度,召回,和F1得分。
结果:具有有效Fitbit数据的分娩妇女患者队列包括<20名PPD患者和39名无PPD患者。我们的结果表明,使用数字生物标志物的个体内模型在孕前识别,怀孕,产后没有抑郁症,和产后抑郁症(即,PPD诊断)周期,随机森林(mAUC=0.85;κ=0.80)模型优于广义线性模型(mAUC=0.82;κ=0.74),支持向量机(mAUC=0.75;κ=0.72),和k-最近邻(mAUC=0.74;κ=0.62)。没有PPD的女性的模型性能下降,说明该方法的特异性。既往抑郁史并不影响PPD识别模型的功效。此外,我们发现PPD最具预测性的生物标志物是基础代谢率期间燃烧的卡路里.最后,个性化模型的性能超过了传统的基于队列的PPD检测模型.
结论:这项研究将消费者可穿戴设备确立为PPD识别的有前途的工具,并强调个性化ML方法。这可以改变早期疾病检测策略。
BACKGROUND: Postpartum depression (PPD) poses a significant maternal health challenge. The current approach to detecting PPD relies on in-person postpartum visits, which contributes to underdiagnosis. Furthermore, recognizing PPD symptoms can be challenging. Therefore, we explored the potential of using digital biomarkers from consumer wearables for PPD recognition.
OBJECTIVE: The main goal of this study was to showcase the viability of using machine learning (ML) and digital biomarkers related to heart rate, physical activity, and energy expenditure derived from consumer-grade wearables for the recognition of PPD.
METHODS: Using the All of Us Research Program Registered Tier v6 data set, we performed computational phenotyping of women with and without PPD following childbirth. Intraindividual ML models were developed using digital biomarkers from Fitbit to discern between prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods. Models were built using generalized linear models, random forest, support vector machine, and k-nearest neighbor algorithms and evaluated using the κ statistic and multiclass area under the receiver operating characteristic curve (mAUC) to determine the algorithm with the best performance. The specificity of our individualized ML approach was confirmed in a cohort of women who gave birth and did not experience PPD. Moreover, we assessed the impact of a previous history of depression on model performance. We determined the variable importance for predicting the PPD period using Shapley additive explanations and confirmed the results using a permutation approach. Finally, we compared our individualized ML methodology against a traditional cohort-based ML model for PPD recognition and compared model performance using sensitivity, specificity, precision, recall, and F1-score.
RESULTS: Patient cohorts of women with valid Fitbit data who gave birth included <20 with PPD and 39 without PPD. Our results demonstrated that intraindividual models using digital biomarkers discerned among prepregnancy, pregnancy, postpartum without depression, and postpartum with depression (ie, PPD diagnosis) periods, with random forest (mAUC=0.85; κ=0.80) models outperforming generalized linear models (mAUC=0.82; κ=0.74), support vector machine (mAUC=0.75; κ=0.72), and k-nearest neighbor (mAUC=0.74; κ=0.62). Model performance decreased in women without PPD, illustrating the method\'s specificity. Previous depression history did not impact the efficacy of the model for PPD recognition. Moreover, we found that the most predictive biomarker of PPD was calories burned during the basal metabolic rate. Finally, individualized models surpassed the performance of a conventional cohort-based model for PPD detection.
CONCLUSIONS: This research establishes consumer wearables as a promising tool for PPD identification and highlights personalized ML approaches, which could transform early disease detection strategies.