AIMC Topic: Infant Mortality

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Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths.

Population health metrics
BACKGROUND: Despite contemporaneous declines in neonatal mortality, recent studies show the existence of left-behind populations that continue to have higher mortality rates than the national averages. Additionally, many of these deaths are from prev...

An artificial neural network approach for predicting infant mortality status in Ethiopia.

BMC public health
Infant mortality is a major public health issue that is rooted in the larger problem of socio-economic and healthcare disparities. Deep learning techniques were employed in this study to predict infant mortality using data gathered via 2019 Ethiopia ...

Development and evaluation of machine learning training strategies for neonatal mortality prediction using multicountry data.

Scientific reports
Neonatal mortality poses a critical challenge in global health, particularly in low- and middle-income countries. Leveraging advancements in technology, such as machine learning (ML) algorithms, offers the potential to improve neonatal care by enabli...

Mortality risk associated with clinical signs of possible serious bacterial infection (PSBI) in young infants in Africa and Asia: protocol for a secondary pooled analysis.

BMJ open
INTRODUCTION: The WHO's Integrated Management of Childhood Illness (IMCI) in young infants <2 months of age includes the identification and management of signs of possible serious bacterial infection (PSBI). However, equal importance is given to all ...

Identifying determinants of under-5 mortality in Bangladesh: A machine learning approach with BDHS 2022 data.

PloS one
BACKGROUND: Under-5 mortality in Bangladesh remains a critical indicator of public health and socio-economic development. Traditional methods often struggle to capture the complex, non-linear relationships influencing under-5 mortality. This study le...

Exploring the achievements and forecasting of SDG 3 using machine learning algorithms: Bangladesh perspective.

PloS one
BACKGROUND: Sustainable Development Goal 3 (SDG 3), focusing on ensuring healthy lives and well-being for all, holds global significance and is particularly vital for Bangladesh. Neonatal Mortality Rate (NMR), Under-5 Mortality Rate (U5MR), Maternal ...

Analyzing and forecasting under-5 mortality trends in Bangladesh using machine learning techniques.

PloS one
BACKGROUND: Under-5 mortality remains a critical social indicator of a country's development and economic sustainability, particularly in developing nations like Bangladesh. This study employs machine learning models, including Linear Regression, Rid...

Machine-learning-based evaluation of the usefulness of lactate for predicting neonatal mortality in preterm infants.

Pediatrics and neonatology
BACKGROUND: Unlike in adult and pediatric patients, the usefulness of lactate in preterm infants has not been thoroughly discussed. This study aimed to evaluate whether the lactate level in the first hours of life is an important factor associated wi...

Geographic inequities in neonatal survival in Nigeria: a cross-sectional evidence from spatial and artificial neural network analyses.

Journal of biosocial science
This study was conducted to provide empirical evidence of geographical variations of neonatal mortality and its associated social determinants with a view to improving neonatal survival at the subnational level in Nigeria. With a combination of spati...