Predicting pregnancy using large-scale data from a women's health tracking mobile application

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 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.

Comparison of Multivariable Logistic Regression and Other Machine Learning Algorithms for Prognostic Prediction Studies in Pregnancy Care: Systematic Review and Meta-Analysis

“…Briefly, we collected studies that either developed or validated a prediction model applying either LR (77/142, 54.2%) or non-LR machine learning algorithms (50/142, 35.2%; Table 1) . Overall, 15 studies applied both LR and non-LR algorithms (15/142, 10.6%) [156][157][158][159][160][161][162][163][164][165][166][167][168][169][170]. The cohort population of the studies in this review consisted of every type of population, study design, timing, and setting that we desired to discuss in this review.…”

“…50/77, 65% vs 19/77, 25%). Most used data sets were from retrospective cohorts for LR (53/77, 69%) [29-36,38-42,47,49-54,56-61,64-66,68-71,76-80,83,87-90,92,94,95, 97,100-105], non-LR prediction studies (27/50, 54%) [107,108,111-113,116,117,121,122,127,130-138,140,142,143,148,150,151, 153,154], or both (9/15, 60%)[157][158][159][160][163][164][165][167][168][169][170]. A retrospective cohort is one of the recommended study designs for prognostic purposes instead of diagnostic prediction[21].…”

Data Capsule: A New Paradigm for Automatic Compliance with Data Privacy Regulations

“…We are currently in the preliminary stages of a collaborative case study to apply the data capsule paradigm to enforce HIPAA in a medical study of menstrual data collected via mobile app. The goal of this study [32] and similar work [33,34] is to demonstrate the use of mobile apps to assess menstrual health and fertility. Data capsules will allow study participants to submit their sensitive data in the context of a policy which protects its use.…”

Labeling self-tracked menstrual health records with hidden semi-Markov models

“…To our knowledge, there are no available labeled datasets for menstrual self-tracked data. Thus, supervised labeling methods such as Long-Short-Term-Memory (LSTM) models (Hochreiter, 1997; Liu et al, 2019), or Transformers (Vaswani et al, 2017), cannot be used. Fortunately, this lack of available labeled samples is balanced by a good knowledge of the underlying reproductive biology.…”