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Related Experiment Video

Updated: Jan 7, 2026

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Clinical Manifestations.

Yignan He1, Yu Leng1, Ana-Maria Vranceanu1

  • 1Massachusetts General Hospital, Boston, MA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 26, 2025
PubMed
Summary
This summary is machine-generated.

Social determinants of health (SDoH) data can predict cognitive decline, offering an accessible method for early detection of cognitive impairment. This study developed a robust stacking ensemble model using SDoH for improved dementia screening.

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Area of Science:

  • Gerontology
  • Public Health
  • Machine Learning

Background:

  • Early diagnosis of Alzheimer's disease and related dementias (AD/ADRD) is crucial but faces accessibility challenges with current methods.
  • Social determinants of health (SDoH) and lifestyle factors present accessible alternatives for predicting cognitive decline.

Purpose of the Study:

  • To develop and validate a stacking ensemble model for early detection of cognitive impairment using SDoH data.
  • To assess the feasibility and performance of SDoH-based prediction of cognitive decline.

Main Methods:

  • Utilized data from the Mexican Health and Aging Study (MHAS), a longitudinal survey of adults aged 50+.
  • Employed a two-level stacking ensemble architecture to predict global cognition using demographic, economic, health, and lifestyle variables.
  • Trained and tested the model on data collected across multiple years to predict cognitive function.

Main Results:

  • The ensemble model achieved a Root-Mean-Square Deviation (RMSE) of 39.25, indicating approximately 10.2% deviation from the full cognitive score range.
  • LightGBM was the best-performing base model, and feature importance analysis identified key social and lifestyle factors.
  • Sensitivity analyses confirmed model robustness, though biases were noted in certain subgroups due to dataset representation.

Conclusions:

  • SDoH data offers a feasible approach for the early detection of cognitive impairment, balancing predictive accuracy and interpretability.
  • The developed model demonstrates the potential of accessible data for cognitive health assessment.
  • Future research should focus on predicting changes in cognitive scores, considering the influence of SDoH.