A Mobile Health Application to Predict Postpartum Depression Based on Machine Learning

Telemedicine Journal and E-health : the Official Journal of the American Telemedicine Association
Santiago Jiménez-SerranoJuan Miguel García-Gómez

Abstract

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 a...Continue Reading

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Jul 15, 2015·Cyberpsychology, Behavior and Social Networking·Brenda K Wiederhold
Jan 14, 2016·IEEE/ACM Transactions on Computational Biology and Bioinformatics·Yanmin GongYuanxiong Guo
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Apr 6, 2021·Computers, Informatics, Nursing : CIN·Ji Min SeoHyejin Lee
Sep 7, 2017··Patricia Angela R. AbuKyle Kristopher P. Garcia

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