Predicting pregnancy using large-scale data from a women's health tracking mobile application
Open accessAbstract
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 interpretable time trends from our deep learning models, and show these trends are consistent with previous fertility research. Our findings illustrate the potential that women's health tracking data offers for predicting pregnancy on a broader population; we conclude by discussing the steps needed to fulfill this potential.
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Fertility awareness apps and the associated databases of fertility data provide a unique opportunity to examine a large number of menstrual cycles in order to improve understanding.
“Besides the potential benefits to the individual, fertility awareness apps and the associated databases of fertility data provide a unique opportunity to examine a large number of menstrual cycles in order to improve understanding.25,26”
From Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles (2019) · cites this paper as [25]
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Real-world menstrual cycle characteristics of more than 600,000 menstrual cycles
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“Besides the potential benefits to the individual, fertility awareness apps and the associated databases of fertility data provide a unique opportunity to examine a large number of menstrual cycles in order to improve understanding.(25,26) The mobile app used in the study can be used to prevent a pregnancy (‘Prevent’ m…”
Method
- Authors
- Liu, B., Shi, S., Wu, Y., Thomas, D., Symul, L., Pierson, E., & Leskovec, J.
- Affiliations
- Stanford University (US)
- Clue by BioWink GmbH, Berlin
- Stanford Medicine (US)
- Corresponding author
- Pierson, Emma
- Venue
- The World Wide Web Conference
- Publisher
- ACM
- Published
- 2019-05-13
- Volume
- 2019
- Pages
- 2999-3005
- PMC
- PMC6752881
- OpenAlex
- W2902149548
- Cited by
- 34
- References
- 43
- Citations by year
- 2026: 1 · 2025: 6 · 2024: 4 · 2023: 6 · 2022: 3 · 2021: 5 · 2020: 4 · 2019: 5
- FWCI
- 4.84
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- International World Wide Web Conference (WWW) (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- National Defense Science and Engineering Graduate
- National Institute of Biomedical Imaging and Bioengineering (U54EB020405)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Medicine › Reproductive Medicine › Reproductive Health and Technologies
- Keywords
- pregnancy prediction, mobile health apps, fertility, LSTM, logistic regression
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