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Mom2B: a study of perinatal health via smartphone application and machine learning methods

Published online by Cambridge University Press:  01 September 2022

A. Bilal ,
D. Bathula ,
E. Bränn ,
E. Fransson ,
J. Virk ,
F. Papadopoulos  and
A. Skalkidou
A. Bilal
Affiliation:
Uppsala University, Neuroscience, Psychiatry, Uppsala, Sweden
D. Bathula
Affiliation:
Indian Institute of Technology Ropar, Computer Science And Engineering, Punjab, India
E. Bränn
Affiliation:
Uppsala University, Women’s And Children’s Health, Uppsala, Sweden
E. Fransson
Affiliation:
Uppsala University, Women’s And Children’s Health, Uppsala, Sweden
J. Virk
Affiliation:
Indian Institute of Technology Ropar, Computer Science And Engineering, Punjab, India
F. Papadopoulos
Affiliation:
Uppsala University, Neuroscience, Psychiatry, Uppsala, Sweden
A. Skalkidou*
Affiliation:
Uppsala University, Women’s And Children’s Health, Uppsala, Sweden
*
*Corresponding author.

Abstract

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Introduction

Peripartum depression (PPD) impacts around 12% of women globally and is a leading cause of maternal mortality. However, there are currently no accurate methods in use to identify women at high risk for depressive symptoms on an individual level. An initial study was done to assess the value of deep learning models to predict perinatal depression from women at six weeks postpartum. Clinical, demographic, and psychometric questionnaire data was obtained from the “Biology, Affect, Stress, Imaging and Cognition during Pregnancy and the Puerperium” (BASIC) cohort, collected from 2009-2018 in Uppsala, Sweden. An ensemble of artificial neural networks and decision trees-based classifiers with majority voting gave the best and balanced results, with nearly 75% accuracy. Predictive variables identified in this study were used to inform the development of the ongoing Swedish Mom2B study.

Objectives

The aim of the Mom2be study is to use digital phenotyping data collected via the Mom2B mobile app to evaluate predictive models of the risk of perinatal depression.

Methods

In the Mom2B app, clinical, sociodemographic and psychometric information is collected through questionnaires, including the Edinburgh Postnatal Depression Scale (EPDS). Audio recordings are recurrently obtained upon prompts, and passive data from smartphone sensors and activity logs, reflecting social-media activity and mobility patterns. Subsequently, we will implement and evaluate advanced machine learning and deep learning models to predict the risk of PPD in the third pregnancy trimester, as well as during the early and late postpartum period, and identify variables with the strongest predictive value.

Results

Analyses are ongoing.

Conclusions

Pending results.

Disclosure

No significant relationships.

Keywords

peripartum depressiondigital phenotyping datadeep learning models
Type
Abstract
Information
European Psychiatry , Volume 65 , Special Issue S1: Abstracts of the 30th European Congress of Psychiatry , June 2022 , pp. S574 - S575
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2022. Published by Cambridge University Press on behalf of the European Psychiatric Association
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