Jiménez-Serrano Santiago, Tortajada Salvador, García-Gómez Juan Miguel
1 Biomedical Informatics Group, Institute for the Applications of Advanced Information and Communication Technologies (ITACA), Polytechnic University of Valencia , Valencia, Spain .
2 Joint Research Unit in Biomedical Engineering-eRPSS (ICT Applied to Healthcare Process Re-engineering), Health Research Institute Hospital La Fe, Valencia , Spain .
Telemed J E Health. 2015 Jul;21(7):567-74. doi: 10.1089/tmj.2014.0113. Epub 2015 Mar 3.
Postpartum depression (PPD) is a disorder that often goes undiagnosed. The development of a screening program requires considerable and careful effort, where evidence-based decisions have to be taken in order to obtain an effective test with a high level of sensitivity and an acceptable specificity that is quick to perform, easy to interpret, culturally sensitive, and cost-effective. The purpose of this article is twofold: first, to develop classification models for detecting the risk of PPD during the first week after childbirth, thus enabling early intervention; and second, to develop a mobile health (m-health) application (app) for the Android(®) (Google, Mountain View, CA) platform based on the model with best performance for both mothers who have just given birth and clinicians who want to monitor their patient's test.
A set of predictive models for estimating the risk of PPD was trained using machine learning techniques and data about postpartum women collected from seven Spanish hospitals. An internal evaluation was carried out using a hold-out strategy. An easy flowchart and architecture for designing the graphical user interface of the m-health app was followed.
Naive Bayes showed the best balance between sensitivity and specificity as a predictive model for PPD during the first week after delivery. It was integrated into the clinical decision support system for Android mobile apps.
This approach can enable the early prediction and detection of PPD because it fulfills the conditions of an effective screening test with a high level of sensitivity and specificity that is quick to perform, easy to interpret, culturally sensitive, and cost-effective.
产后抑郁症（PPD）是一种常常未被诊断出来的疾病。筛查项目的开展需要大量且细致的工作，必须基于证据做出决策，以获得一种高效的检测方法，该方法具有高灵敏度、可接受的特异性，检测速度快、易于解读、具有文化敏感性且成本效益高。本文的目的有两个：第一，开发用于检测产后第一周PPD风险的分类模型，从而实现早期干预；第二，基于对刚分娩的母亲和想要监测其患者检测情况的临床医生来说性能最佳的模型，为安卓（®）（谷歌，加利福尼亚州山景城）平台开发一款移动健康（m-health）应用程序（app）。
使用机器学习技术以及从七家西班牙医院收集的产后女性数据，训练了一组用于估计PPD风险的预测模型。采用留出法进行内部评估。遵循了一个简单的流程图和架构来设计m-health应用程序的图形用户界面。
作为分娩后第一周PPD的预测模型，朴素贝叶斯在灵敏度和特异性之间表现出了最佳平衡。它被集成到了安卓移动应用程序的临床决策支持系统中。
这种方法能够实现PPD的早期预测和检测，因为它满足了一种高效筛查检测的条件，具有高灵敏度和特异性，检测速度快、易于解读、具有文化敏感性且成本效益高。
