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


METHODS
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.


RESULTS
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.


CONCLUSIONS
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.
1 Biomedical Informatics Group, Institute for the Applications of Advanced Information and Communication Technologies (ITACA), Polytechnic University of Valencia , Valencia, Spain .
Santiago
S
Jiménez-Serrano
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 .
Salvador
S
Tortajada
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 .|3 Biomedical Imaging Research Group (GIBI230), Health Research Institute Hospital La Fe, Valencia , Spain .
Juan Miguel
JM
García-Gómez
D000328:Adult; D019052:Depression, Postpartum / Q000209:etiology*; D005260:Female; D005544:Forecasting; D006801:Humans; D000069550:Machine Learning; D011446:Prospective Studies; D012307:Risk Factors; D012680:Sensitivity and Specificity; D011795:Surveys and Questionnaires; D017216:Telemedicine
Android; classifiers; decision support; machine learning; mobile health; pattern recognition; postpartum depression