论文标题
未接听电话，自动电话和健康支持：通过增加计划参与度来改善孕产妇的健康结果
Missed calls, Automated Calls and Health Support: Using AI to improve maternal health outcomes by increasing program engagement
论文作者
论文摘要
印度占全球孕产妇死亡的11％，其中一名妇女每15分钟死于分娩。缺乏获得预防性护理信息是一个重大的问题，导致了高产妇的发病率和死亡人数，尤其是在低收入家庭中。我们与印度的非营利组织Armman合作，通过早期识别可能不参与这些计划的女性来进一步使用基于呼叫的信息程序，这些女性可能会影响健康参数，这些计划会积极影响健康参数。我们分析了由Armman登记的300,000多名女性的匿名呼叫记录，该妇女在ARMMAN中使用的呼叫呼叫呼叫的呼叫呼叫来定期使用，以定期使用健康相关的健康相关信息。我们建立了强大的深度学习模型，以预测呼叫日志和受益人的人口统计信息的短期和长期辍学风险。对于短期预测，我们的模型比竞争基线的表现要高13％，长期预测要好7％。我们还通过使用我们的方法执行有针对性的干预措施的试点验证来讨论该方法在现实世界中的适用性。
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.
