Novel learning-based framework for predicting Alzheimer’s disease progression

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According to the World Health Organization, approximately 55 million people worldwide are living with dementia. The most common form is Alzheimer’s disease, an incurable condition that causes brain function to deteriorate.

In addition to its physical effects, Alzheimer’s has an emotional, social, and economic impact not only on people with the disease, but also on those who love and care for them. Because symptoms worsen over time, it is important for both patients and their caregivers to prepare for the eventual need to increase the amount of support as the disease progresses.

To that end, researchers at the University of Texas at Arlington have developed a novel learning-based framework that will help accurately identify where Alzheimer’s patients are on the disease-development spectrum. This will help them better predict the timing of the later stages, making it easier to plan for future care as the disease progresses.

Over the decades, various predictive methods have been proposed and evaluated in terms of their ability to predict Alzheimer’s disease and its precursors, mild cognitive impairment.”

Dajiang Zhu, Associate Professor of Computer Science and Engineering, UTA

He is the lead author of a new peer-reviewed paper published Open Access Pharmacological research. “Many of these earlier predictions ignored the continuous nature of Alzheimer’s disease development and disease transition phases.”

Work supported by more than $2 million in grants from the National Institutes of Health and the National Institute on Aging, Zhu’s Medical Imaging and Neuroscientific Discovery Research Lab and Li Wang, an associate professor of mathematics at UTA, have developed a new learning-based embedding framework that helps Alzheimer’s The process codes the different stages of disease development in a process they call a “disease-embedding tree” or DItree. Using this framework, DETree can not only efficiently and accurately predict any of the five subtle clinical groups of Alzheimer’s disease progression but also provide more in-depth status information by projecting where the patient will be as the disease progresses.

To test their Dietary framework, the researchers used data from 266 people with Alzheimer’s disease from the Multicenter Alzheimer’s Disease Neuroimaging Initiative. The results of the DETree technique were compared with other widely used methods for predicting the progression of Alzheimer’s disease, and the experiment was repeated several times using machine learning-methods to validate the technique.

“We know that people with Alzheimer’s disease often develop symptoms that deteriorate at very different rates,” Zhu said. “We are pleased that our new framework is more accurate than other prediction models available, which we hope will help patients and their families better plan for the uncertainties of this complex and devastating disease.”

He and his team believe the Dietary framework has the potential to predict the progression of other diseases that have multiple clinical stages of development, such as Parkinson’s disease, Huntington’s disease, and Creutzfeldt-Jakob disease.


Journal Reference:

Zhang, L., etc. (2024). Disease2Vec: encoding Alzheimer’s progression through disease embedding trees. Pharmacological research.

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