AIMC Topic: Family Characteristics

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Aligning a Household-Level Service Array Through a Jurisdiction-Wide Child Maltreatment Prevention Effort: Protocol for a Geospatial and Counterfactual Modeling Study.

JMIR research protocols
BACKGROUND: Child maltreatment is associated with multiple negative outcomes at the individual and societal levels. Children experiencing maltreatment are at greater risk of a host of negative outcomes (eg, psychological disorders, substance use, vio...

Reweaving the Threads of Korean History: AI-Driven Restoration of the Daegu-bu Household Registers (1681-1876).

Scientific data
In this study, we have applied advanced masked language models (MLMs)-BERT, DistilBERT, ELECTRA, and RoBERTa-to infer missing and misinterpreted values in comprehensive family register data. Our data compiles Daegu-bu household register books, trienn...

Formulation and validation of a regional household wealth index for sub-Saharan Africa.

PloS one
A new era in global health assistance requires a focus on efficiently using limited and declining donor funds. This shift requires better evaluation methods to allocate resources effectively. Most evaluations in low- and middle-income countries (LMIC...

Equitable AI: Exploring the role of gender in poverty estimation models using geospatial data.

PloS one
Household surveys have been the foundation for poverty measurement in developing countries for the past half-century, but the spatial and temporal gaps in these survey data often limit how well anti-poverty programs can be targeted, monitored, or eva...

Machine learning based classification of catastrophic health expenditures: a cross-sectional study of Korean low-income households.

BMC health services research
BACKGROUND: Despite the National Health Insurance (NHI) system implemented in South Korea, concerns persist regarding access to health coverage for low-income households. To address this issue, this study aims to use machine learning-based data minin...

Understanding the determinants of treated bed net use in Ethiopia: A machine learning classification approach using PMA Ethiopia 2023 survey data.

PloS one
INTRODUCTION: Malaria remains a significant public health challenge in Ethiopia, with over 7.3 million cases and 1,157 deaths reported between January 1 and October 20, 2024. Despite extensive distribution campaigns, 35% of insecticide-treated nets (...

The impact of intelligent devices utilization on household medical expenditure of older adults in China.

Scientific reports
With the rapid development of artificial intelligence, there is an increasing utilization of intelligent devices by older adults. The relationship between the utilization of intelligent devices and household medical expenditure has garnered widesprea...

Efficiency-Driven Adaptive Task Planning for Household Robot Based on Hierarchical Item-Environment Cognition.

IEEE transactions on cybernetics
Task planning focused on household robots represents a conventional yet complex research domain, necessitating the development of task plans that enable robots to execute unfamiliar household services. This area has garnered significant research inte...

Assessing fecal contamination from human and environmental sources using as an indicator in rural eastern Ethiopian households-a cross-sectional study from the EXCAM project.

Frontiers in public health
INTRODUCTION: Enteric pathogens are a leading causes of diarrheal deaths in low-and middle-income countries. The Exposure Assessment of Infections in Rural Ethiopia (EXCAM) project, aims to identify potential sources of bacteria in the genus and, m...

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 ...