AI Medical Compendium Topic

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Poverty

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The impact of austerity on children: Uncovering effect heterogeneity by political, economic, and family factors in low- and middle-income countries.

Social science research
Which children are most vulnerable when their government imposes austerity? Research tends to focus on either the political-economic level or the family level. Using a sample of nearly two million children in 67 countries, this study synthesizes theo...

Food Access in New York City During the COVID-19 Pandemic: Social Media Monitoring Study.

JMIR formative research
BACKGROUND: The COVID-19 pandemic exacerbated issues of poverty and food insecurity in New York City, and many residents experienced difficulty accessing available resources to help them get food on the table. Social media presents an opportunity to ...

Machine learning and phone data can improve targeting of humanitarian aid.

Nature
The COVID-19 pandemic has devastated many low- and middle-income countries, causing widespread food insecurity and a sharp decline in living standards. In response to this crisis, governments and humanitarian organizations worldwide have distributed ...

Balancing national economic policy outcomes for sustainable development.

Nature communications
The 2030 Sustainable Development Goals (SDGs) aim at jointly improving economic, social, and environmental outcomes for human prosperity and planetary health. However, designing national economic policies that support advancement across multiple Sust...

Application of Machine Learning to Child Mode Choice with a Novel Technique to Optimize Hyperparameters.

International journal of environmental research and public health
Travel mode choice (TMC) prediction is crucial for transportation planning. Most previous studies have focused on TMC in adults, whereas predicting TMC in children has received less attention. On the other hand, previous children's TMC prediction stu...

High-resolution rural poverty mapping in Pakistan with ensemble deep learning.

PloS one
High resolution poverty mapping supports evidence-based policy and research, yet about half of all countries lack the survey data needed to generate useful poverty maps. To overcome this challenge, new non-traditional data sources and deep learning t...

Machine learning study using 2020 SDHS data to determine poverty determinants in Somalia.

Scientific reports
Extensive research has been conducted on poverty in developing countries using conventional regression analysis, which has limited prediction capability. This study aims to address this gap by applying advanced machine learning (ML) methods to predic...

Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.

Journal of substance use and addiction treatment
BACKGROUND: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-leve...

Unveiling predictive factors for household-level stunting in India: A machine learning approach using NFHS-5 and satellite-driven data.

Nutrition (Burbank, Los Angeles County, Calif.)
OBJECTIVES: Childhood stunting remains a significant public health issue in India, affecting approximately 35% of children under 5. Despite extensive research, existing prediction models often fail to incorporate diverse data sources and address the ...

Testing a Machine Learning-Based Adaptive Motivational System for Socioeconomically Disadvantaged Smokers (Adapt2Quit): Protocol for a Randomized Controlled Trial.

JMIR research protocols
BACKGROUND: Individuals who are socioeconomically disadvantaged have high smoking rates and face barriers to participating in smoking cessation interventions. Computer-tailored health communication, which is focused on finding the most relevant messa...