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Predicting pregnancy has been a fundamental problem in women's health formore than 50 years. Previous datasets have been collected via carefully curatedmedical studies, but the recent growth of women's health tracking mobile appsoffers potential for reaching a much broader population. However, thefeasibility of predicting pregnancy from mobile health tracking data isunclear. Here we develop four models -- a logistic regression model, and 3 LSTMmodels -- to predict a woman's probability of becoming pregnant using data froma women's health tracking app, Clue by BioWink GmbH. Evaluating our models on adataset of 79 million logs from 65,276 women with ground truth pregnancy testdata, we show that our predicted pregnancy probabilities meaningfully stratifywomen: women in the top 10% of predicted probabilities have a 89% chance ofbecoming pregnant over 6 menstrual cycles, as compared to a 27% chance forwomen in the bottom 10%. We develop a technique for extracting interpretabletime trends from our deep learning models, and show these trends are consistentwith previous fertility research. Our findings illustrate the potential thatwomen's health tracking data offers for predicting pregnancy on a broaderpopulation; we conclude by discussing the steps needed to fulfill thispotential.

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