ArticleProceedings of the ... International World-Wide Web Conference. International WWW Conference2019
Predicting pregnancy using large-scale data from a women's health tracking mobile application.
Article in Proceedings of the ... International World-Wide Web Conference. International WWW Conference, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence-Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review.Journal of medical Internet research · 2024Pooled it
- Review
- The association of COVID-19 vaccination and menstrual health: A period-tracking app-based cohort study.Vaccine: X · 2024Article
- Feasibility of continuous distal body temperature for passive, early pregnancy detection.PLOS digital health · 2022Article
- Menstrual Cycle Tracking Applications and the Potential for Epidemiological Research: A Comprehensive Review of the Literature.Current epidemiology reports · 2021Article
- Article
- Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis.JMIR medical informatics · 2020Review
- Assessment of menstrual health status and evolution through mobile apps for fertility awareness.NPJ digital medicine · 2019Article
- Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles.NPJ digital medicine · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Predicting pregnancy has been a fundamental problem in women's health for more than 50 years. Previous datasets have been collected via carefully curated medical studies, but the recent growth of women's health tracking mobile apps offers potential for reaching a much broader population. However, the feasibility of predicting pregnancy from mobile health tracking data is unclear. Here we develop four models - a logistic regression model, and 3 LSTM models - to predict a woman's probability of becoming pregnant using data from a women's health tracking app, Clue by BioWink GmbH. Evaluating our models on a dataset of 79 million logs from 65,276 women with ground truth pregnancy test data, we show that our predicted pregnancy probabilities meaningfully stratify women: women in the top 10% of predicted probabilities have a 89% chance of becoming pregnant over 6 menstrual cycles, as compared to a 27% chance for women in the bottom 10%. We develop a technique for extracting
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.