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Related Concept Videos

Longitudinal Research02:20

Longitudinal Research

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Sometimes we want to see how people change over time, as in studies of human development and lifespan. When we test the same group of individuals repeatedly over an extended period of time, we are conducting longitudinal research. Longitudinal research is a research design in which data-gathering is administered repeatedly over an extended period of time. For example, we may survey a group of individuals about their dietary habits at age 20, retest them a decade later at age 30, and then again...
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Longitudinal studies are also widely used in other medical and social science fields. For instance, in cardiovascular research, they can monitor patients' health over decades to identify risk factors for heart disease, such as high cholesterol or smoking, and evaluate the long-term effectiveness of preventive measures. Similarly, in mental health studies, researchers might follow individuals from adolescence into adulthood to understand the development and progression of conditions like...
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Dementia is a collective term for cognitive disorders primarily affecting memory, thinking, and reasoning. It is not a specific disease but a syndrome, with Alzheimer's disease being the most common cause, accounting for approximately 60-80% of cases. Other types include vascular dementia, Lewy body dementia, and frontotemporal dementia. Dementia affects millions worldwide, particularly older adults, though it is not a normal part of aging.
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Alzheimer's Disease (AD) is a continually advancing neurodegenerative disorder, distinguished by escalating memory loss, cognitive dysfunction, and dementia. The disease unfolds in three stages: preclinical, mild cognitive impairment (MCI), and dementia. Its onset is insidious, and the progression gradual, with the cause not well explained by other disorders.
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Constructing and Visualizing Models using Mime-based Machine-learning Framework
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Cross-Dataset Evaluation of Dementia Longitudinal Progression Prediction Models.

Chen Zhang1,2,3, Lijun An1,2,3, Naren Wulan1,2,3

  • 1Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.

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Summary

The L2C-FNN model demonstrated superior Alzheimer's Disease (AD) progression prediction across diverse datasets. This advanced algorithm shows significant potential for accurate short-term and long-term dementia forecasting.

Keywords:
Alzheimer's diseaseXGBoostdomain generalizationfeature engineeringlongitudinal progression modelingrecurrent neural networks

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Area of Science:

  • Neuroscience
  • Medical Informatics
  • Machine Learning

Background:

  • Accurate Alzheimer's Disease (AD) progression prediction is crucial for clinical management.
  • The 2019 TADPOLE challenge assessed 92 algorithms for AD prediction using the ADNI dataset.
  • Generalizability of TADPOLE algorithms to external datasets remains largely unexamined.

Purpose of the Study:

  • To evaluate the generalization performance of top Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) algorithms on independent datasets.
  • To compare the predictive accuracy of five selected algorithms, including the TADPOLE winner FROG and its variants, MinimalRNN, and AD-Map.
  • To assess the robustness of these algorithms across varying patient data availability and prediction time horizons.

Main Methods:

  • Five algorithms (FROG variants, MinimalRNN, AD-Map) were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset.
  • These models were subsequently tested on three external datasets comprising 2312 participants and 13,200 time points.
  • The FROG algorithm's Longitudinal-to-Cross-sectional (L2C) transformation was a key component, converting variable-length longitudinal data into fixed-length feature vectors.

Main Results:

  • The L2C-FNN model, a variant of FROG, exhibited the best overall performance in predicting Alzheimer's Disease progression.
  • L2C-FNN and AD-Map showed comparable top performance in predicting cognition and ventricle volume.
  • L2C-FNN outperformed other models in clinical diagnosis prediction and maintained its accuracy across different numbers of observed time points and prediction horizons up to 6 years.

Conclusions:

  • The L2C-FNN model demonstrates strong potential for accurate short-term and long-term Alzheimer's Disease progression prediction.
  • The findings highlight the effectiveness of the L2C transformation in handling complex longitudinal data for neurodegenerative disease prediction.
  • The study provides valuable insights into the generalizability of machine learning models for Alzheimer's Disease research and clinical application.