Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach.
Predictors of Contemporary under-5 Child Mortality in Low- and Middle-Income Countries: A Machine Learning Approach.
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DOI：
10.3390/ijerph18031315
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发表时间：
2021-02-01
影响因子：
--
通讯作者：
Esposito G
中科院分区：
文献类型：
--
作者：
Bizzego A;Gabrieli G;Bornstein MH;Deater-Deckard K;Lansford JE;Bradley RH;Costa M;Esposito G
儿童死亡率（CM）是一个全球性问题，每年影响低收入和中等收入国家（LMIC）多达6.81%的儿童。我们使用来自27个LMIC的多指标聚类调查（MICS）数据（N = 275,160）和机器学习方法对37个CM远端原因进行排序，并根据预测效力确定前10个原因。根据前10个原因，我们确定了条件有所改善的家庭。我们通过调查2005-2007年至2013-2017年多指标类集调查期间，CM变化与国家一级条件改善家庭百分比变化之间的关系，回顾性验证了结果。我们的方法的一个独特贡献是确定了鲜为人知的远端原因，这些远端原因可能解释了众所周知的近端原因：值得注意的是，已确定的远端原因可以通过社会、教育和身体干预来预防和治疗。我们展示了如何使用机器学习从大数据集中获取操作信息，以指导干预措施和政策制定者。
Child Mortality (CM) is a worldwide concern, annually affecting as many as 6.81% children in low- and middle-income countries (LMIC). We used data of the Multiple Indicators Cluster Survey (MICS) (N = 275,160) from 27 LMIC and a machine-learning approach to rank 37 distal causes of CM and identify the top 10 causes in terms of predictive potency. Based on the top 10 causes, we identified households with improved conditions. We retrospectively validated the results by investigating the association between variations of CM and variations of the percentage of households with improved conditions at country-level, between the 2005–2007 and the 2013–2017 administrations of the MICS. A unique contribution of our approach is to identify lesser-known distal causes which likely account for better-known proximal causes: notably, the identified distal causes and preventable and treatable through social, educational, and physical interventions. We demonstrate how machine learning can be used to obtain operational information from big dataset to guide interventions and policy makers.
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影响因子：
4.6
作者：
BRADLEY, RH;CALDWELL, BM
通讯作者：
CALDWELL, BM
影响因子：
7.7
作者：
BALTAZAR, JC;SOLON, FS
通讯作者：
SOLON, FS
影响因子：
168.9
作者：
Liu, Li;Oza, Shefali;Hogan, Dan;Chu, Yue;Perin, Jamie;Zhu, Jun;Lawn, Joy E.;Cousens, Simon;Mathers, Colin;Black, Robert E.
通讯作者：
Black, Robert E.
DOI：
10.1016/s2214-109x(18)30059-7
发表时间：
2018-05
期刊：
The Lancet. Global health
影响因子：
--
作者：
Chao F;You D;Pedersen J;Hug L;Alkema L
通讯作者：
Alkema L
影响因子：
158.5
作者：
Gauderman, WJ;Avol, E;Peters, J
通讯作者：
Peters, J