Machine learning models accelerate chronic disease estimation in US small areas

Researchers have developed machine learning models to address the lag in small-area estimation (SAE) of chronic diseases. By learning the relationship between frequently updated area-level predictors and existing SAE outputs, these models can generate timely estimates. The study evaluated various global and geographically weighted ML models for county-level SAE of ten chronic conditions across the US, finding that geographically weighted frameworks like geographically weighted random forest and geographically weighted regression offer scalable solutions for rapid SAE generation. AI

IMPACT These ML models offer a scalable solution for generating timely health outcome data, aiding in faster identification of health disparities.

RANK_REASON The cluster contains a research paper detailing the application of machine learning for health outcome estimation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• arterial hypertension
• arthritis
• asthma
• Cancer
• CDC PLACES
• chronic obstructive pulmonary disease
• diabetes
• geographically weighted random forest
• Geographically Weighted Regression: The Analysis of Spatially Varying Relationships
• heart disease
• major depressive disorder
• stroke
• US

• paper
• other

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