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Clinical Manifestations.

Bargav Jagatha1,2, Ting Fang Alvin Ang3,4,5, Rhoda Au3,4,5,6,7,8

  • 1Department of Medicine/Section of Preventive Medicine and Epidemiology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA.

Alzheimer'S & Dementia : the Journal of the Alzheimer'S Association
|December 25, 2025
PubMed
Summary
This summary is machine-generated.

Deep learning models effectively impute missing cognitive data in Alzheimer's Disease (AD) research. The SAITS model shows superior performance in handling longitudinal data gaps, improving clinical research accuracy.

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Area of Science:

  • Neurology
  • Data Science
  • Biostatistics

Background:

  • Longitudinal cognitive assessments in Alzheimer's Disease (AD) research often suffer from missing data due to logistical, personal, or health-related issues.
  • Missing data challenges clinical research and patient monitoring, necessitating robust imputation methods.
  • Deep learning approaches offer promising, assumption-free alternatives to traditional imputation techniques for longitudinal data.

Purpose of the Study:

  • To evaluate the effectiveness of deep learning-based imputation methods for missing longitudinal cognitive data in Alzheimer's Disease (AD).
  • To assess the performance of the Self-Attention-based Imputation for Time Series (SAITS) model in imputing cognitive scores within AD studies.

Main Methods:

  • Developed and applied the SAITS deep learning model to impute missing values in Mini-Mental State Examination (MMSE) and Alzheimer's Disease Assessment Scale-Cognitive (ADAS-cog) scores.
  • Trained and evaluated SAITS using longitudinal data from GERAS-EU and GERAS-US studies, comparing it against iTransformer, BRITS, mean imputation, and last observation carried forward (LOCF).
  • Performance was assessed using Mean Absolute Error (MAE) on randomly masked test data over 100 trials.

Main Results:

  • SAITS demonstrated superior performance in imputing cognitive data across both GERAS-EU and GERAS-US cohorts.
  • Achieved the lowest MAE values for imputing ADAS-cog total scores (1.636 in GERAS-EU, 1.628 in GERAS-US) and MMSE total scores (0.567 in GERAS-EU, 1.178 in GERAS-US).

Conclusions:

  • Deep learning techniques, particularly SAITS, show significant potential for accurately imputing missed longitudinal cognitive data in AD.
  • Future research will explore the impact of these imputation methods on downstream tasks, such as predicting cognitive decline in AD patients.