This paper is in the following e-collection/theme issue:

Wearable Devices and Sensors (903) Machine Learning (1501) Pregnancy Information, Education and Lifestyle Interventions (304) mHealth for Data Collection and Research (981) Evaluation and Research Methodology for mHealth (210) mHealth for Wellness, Behavior Change and Prevention (2922) Ecological Momentary Assessment (EMA) (321) Obstetrics, Gynecology, and Reproductive Medicine (235)

Published on in Vol 10, No 8 (2022): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/33850, first published .
Predicting the Next-Day Perceived and Physiological Stress of Pregnant Women by Using Machine Learning and Explainability: Algorithm Development and Validation

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

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

Authors of this article:

Ada Ng1 Author Orcid Image ;   Boyang Wei1 Author Orcid Image ;   Jayalakshmi Jain1 Author Orcid Image ;   Erin A Ward2 Author Orcid Image ;   S Darius Tandon2 Author Orcid Image ;   Judith T Moskowitz2 Author Orcid Image ;   Sheila Krogh-Jespersen2 Author Orcid Image ;   Lauren S Wakschlag2 Author Orcid Image ;   Nabil Alshurafa2 Author Orcid Image
Article Authors Cited by (9) Tweetations (5) Metrics

Ada Ng   1 , PhD ;   Boyang Wei   1 , MS ;   Jayalakshmi Jain   1 , MS ;   Erin A Ward   2 , MA, MSW ;   S Darius Tandon   2 , PhD ;   Judith T Moskowitz   2 , MPH, PhD ;   Sheila Krogh-Jespersen   2 , PhD ;   Lauren S Wakschlag   2 , PhD ;   Nabil Alshurafa   2 , PhD

1 McCormick School of Engineering, Northwestern University, Evanston, IL, United States

2 Northwestern University Feinberg School of Medicine, Chicago, IL, United States

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