Ogallo William, Speakman Skyler, Akinwande Victor, Varshney Kush R, Walcott-Bryant Aisha, Wayua Charity, Weldemariam Komminist, Mershon Claire-Helene, Orobaton Nosa
IBM Research - Africa, Nairobi, Kenya.
IBM Research - T. J. Watson Research Center, Yorktown Heights, NY.
AMIA Annu Symp Proc. 2021 Jan 25;2020:963-972. eCollection 2020.
This study aimed at identifying the factors associated with neonatal mortality. We analyzed the Demographic and Health Survey (DHS) datasets from 10 Sub-Saharan countries. For each survey, we trained machine learning models to identify women who had experienced a neonatal death within the 5 years prior to the survey being administered. We then inspected the models by visualizing the features that were important for each model, and how, on average, changing the values of the features affected the risk of neonatal mortality. We confirmed the known positive correlation between birth frequency and neonatal mortality and identified an unexpected negative correlation between household size and neonatal mortality. We further established that mothers living in smaller households have a higher risk of neonatal mortality compared to mothers living in larger households; and that factors such as the age and gender of the head of the household may influence the association between household size and neonatal mortality.
本研究旨在确定与新生儿死亡率相关的因素。我们分析了来自撒哈拉以南10个国家的人口与健康调查（DHS）数据集。对于每次调查，我们训练机器学习模型以识别在调查进行前5年内经历过新生儿死亡的妇女。然后，我们通过可视化对每个模型重要的特征，以及平均而言改变这些特征的值如何影响新生儿死亡风险来检查模型。我们证实了生育频率与新生儿死亡率之间已知的正相关关系，并确定了家庭规模与新生儿死亡率之间意外的负相关关系。我们进一步确定，与生活在大家庭中的母亲相比，生活在小家庭中的母亲有更高的新生儿死亡风险；并且户主的年龄和性别等因素可能会影响家庭规模与新生儿死亡率之间的关联。
