  India accounts for 11% of maternal deaths globally where a woman dies in
childbirth every fifteen minutes. Lack of access to preventive care information
is a significant problem contributing to high maternal morbidity and mortality
numbers, especially in low-income households. We work with ARMMAN, a non-profit
based in India, to further the use of call-based information programs by
early-on identifying women who might not engage on these programs that are
proven to affect health parameters positively.We analyzed anonymized
call-records of over 300,000 women registered in an awareness program created
by ARMMAN that uses cellphone calls to regularly disseminate health related
information. We built robust deep learning based models to predict short term
and long term dropout risk from call logs and beneficiaries' demographic
information. Our model performs 13% better than competitive baselines for
short-term forecasting and 7% better for long term forecasting. We also discuss
the applicability of this method in the real world through a pilot validation
that uses our method to perform targeted interventions.
