Mom2B: a study of perinatal health via smartphone application and machine learning methods
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...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Cambridge University Press
2022-06-01
|
| Series: | European Psychiatry |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S0924933822014729/type/journal_article |
| _version_ | 1827754089074655232 |
|---|---|
| author | A. Bilal D. Bathula E. Bränn E. Fransson J. Virk F. Papadopoulos A. Skalkidou |
| author_facet | A. Bilal D. Bathula E. Bränn E. Fransson J. Virk F. Papadopoulos A. Skalkidou |
| author_sort | A. Bilal |
| collection | DOAJ |
| description |
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.
|
| first_indexed | 2024-03-11T07:44:13Z |
| format | Article |
| id | doaj.art-761102e0d3c641ee8443ca56875e904e |
| institution | Directory Open Access Journal |
| issn | 0924-9338 1778-3585 |
| language | English |
| last_indexed | 2024-03-11T07:44:13Z |
| publishDate | 2022-06-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | European Psychiatry |
| spelling | doaj.art-761102e0d3c641ee8443ca56875e904e2023-11-17T05:07:55ZengCambridge University PressEuropean Psychiatry0924-93381778-35852022-06-0165S574S57510.1192/j.eurpsy.2022.1472Mom2B: a study of perinatal health via smartphone application and machine learning methodsA. Bilal0D. Bathula1E. Bränn2E. Fransson3J. Virk4F. Papadopoulos5A. Skalkidou6Uppsala University, Neuroscience, Psychiatry, Uppsala, SwedenIndian Institute of Technology Ropar, Computer Science And Engineering, Punjab, IndiaUppsala University, Women’s And Children’s Health, Uppsala, SwedenUppsala University, Women’s And Children’s Health, Uppsala, SwedenIndian Institute of Technology Ropar, Computer Science And Engineering, Punjab, IndiaUppsala University, Neuroscience, Psychiatry, Uppsala, SwedenUppsala University, Women’s And Children’s Health, Uppsala, Sweden 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. https://www.cambridge.org/core/product/identifier/S0924933822014729/type/journal_articleperipartum depressiondigital phenotyping datadeep learning models |
| spellingShingle | A. Bilal D. Bathula E. Bränn E. Fransson J. Virk F. Papadopoulos A. Skalkidou Mom2B: a study of perinatal health via smartphone application and machine learning methods European Psychiatry peripartum depression digital phenotyping data deep learning models |
| title | Mom2B: a study of perinatal health via smartphone application and machine learning methods |
| title_full | Mom2B: a study of perinatal health via smartphone application and machine learning methods |
| title_fullStr | Mom2B: a study of perinatal health via smartphone application and machine learning methods |
| title_full_unstemmed | Mom2B: a study of perinatal health via smartphone application and machine learning methods |
| title_short | Mom2B: a study of perinatal health via smartphone application and machine learning methods |
| title_sort | mom2b a study of perinatal health via smartphone application and machine learning methods |
| topic | peripartum depression digital phenotyping data deep learning models |
| url | https://www.cambridge.org/core/product/identifier/S0924933822014729/type/journal_article |
| work_keys_str_mv | AT abilal mom2bastudyofperinatalhealthviasmartphoneapplicationandmachinelearningmethods AT dbathula mom2bastudyofperinatalhealthviasmartphoneapplicationandmachinelearningmethods AT ebrann mom2bastudyofperinatalhealthviasmartphoneapplicationandmachinelearningmethods AT efransson mom2bastudyofperinatalhealthviasmartphoneapplicationandmachinelearningmethods AT jvirk mom2bastudyofperinatalhealthviasmartphoneapplicationandmachinelearningmethods AT fpapadopoulos mom2bastudyofperinatalhealthviasmartphoneapplicationandmachinelearningmethods AT askalkidou mom2bastudyofperinatalhealthviasmartphoneapplicationandmachinelearningmethods |