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Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learni...

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learni...

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_506688a327e04dec8dff6e7e36ea6487

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

About this item

Full title

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Publisher

Toronto: JMIR Publications

Journal title

JMIR mHealth and uHealth, 2022-08, Vol.10 (8), p.e33850-e33850

Language

English

Formats

Publication information

Publisher

Toronto: JMIR Publications

More information

Scope and Contents

Contents

Background: Cognitive behavioral therapy–based interventions are effective in reducing prenatal stress, which can have severe adverse health effects on mothers and newborns if unaddressed. Predicting next-day physiological or perceived stress can help to inform and enable pre-emptive interventions for a likely physiologically and perceptibly stress...

Alternative Titles

Full title

Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_doaj_primary_oai_doaj_org_article_506688a327e04dec8dff6e7e36ea6487

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_506688a327e04dec8dff6e7e36ea6487

Other Identifiers

ISSN

2291-5222

E-ISSN

2291-5222

DOI

10.2196/33850

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