Kuan-lin Huang, PhD, Assistant Professor of Genetics and Genomic Sciences at the Icahn School of Medicine at Mount Sinai
Dementia is a neurodegenerative disorder, commonly known to affect cognitive function—including memory and reasoning. It is also a factor contributing to death. According to the Centers for Disease Control and Prevention, dementia is currently the seventh leading cause of death in the United States. Alzheimer’s disease is the most common form of dementia, accounting for approximately 70 percent of cases.
Researchers have used artificial intelligence and machine learning to help diagnose and classify dementia. But less effort has been put into understanding mortality among patients with dementia.
A group of researchers at the Icahn School of Medicine at Mount Sinai seeks to tackle this problem by developing a machine learning model to predict risks of death for a patient within 1-, 3-, 5-, and 10-year thresholds of a dementia diagnosis.
“We really want to call attention to how Alzheimer’s disease is actually a major cause of death,” says Kuan-lin Huang, PhD, Assistant Professor of Genetics and Genomic Sciences and Principal Investigator of the Precision Omics Lab at Icahn Mount Sinai.
“When people think of dementia, they think of patients losing their memory, as opposed to when people think about cardiovascular disease or cancer, they think about mortality,” says Dr. Huang. “As someone who has a family member who unfortunately passed away from Alzheimer’s disease, I’ve seen how the late stage of the disease—because you lose certain bodily functions—can become quite lethal.” In late-stage dementia, the disease destroys neurons and other brain cells, which could inhibit swallowing, breathing, or heart rate regulation, or cause deadly associated complications such as urinary tract infections or falls.
In the study, the team focused on this question: Given a person’s age, specific type of dementia, and other factors, what will be the risk the person will end up passing within a certain number of years?
For its model, the team used XGBoost, a machine learning algorithm that utilizes “gradient boosting.” This algorithm is based on the use of many decision trees—“if-this, then-that”-type reasoning. It learns from errors made by previous simple trees and collectively can make strong predictions.
Here’s how the study’s lead authors, Jimmy Zhang and Luo Song in Dr. Huang’s research team, leveraged machine learning to shed light on mortality in dementia.
The study used data from more than 40,000 unique patients from the National Alzheimer’s Coordinating Center, a database spanning about 40 Alzheimer’s disease centers across the United States. The model achieved an area under the receiver operating characteristic curve (AUC-ROC) score of more than 0.82 across the 1-, 3-, 5-, and 10-year thresholds. Compared to an AUC-ROC of 0.5, which amounts to a random guess that correctly predicts 50 percent of the time, the model performed reasonably well in predicting a dementia patient’s mortality, but still has room for improvement. By conducting stratified analyses within each dementia type, the researchers also identified distinct predictors of mortality across eight dementia types.
Findings were published in Communications Medicine on February 28.
In this Q&A, Dr. Huang discusses the team’s research.
What was the motivation for your study?
We wanted to address the challenges in dementia care: namely, to identify patients with dementia at high risk of near-term mortality, and to understand the factors contributing to mortality risk across different types of dementia.
What are the implications?
Clinically, it supports the early identification of high-risk patients, enabling targeted care strategies and personalized care. On a research level, it underscores the value of machine learning in understanding complex diseases like dementia and paves the way for future studies to explore predictive modeling in other aspects of dementia care.
What are the limitations of the study?
While our study includes nationwide data, to make the model more generalizable, it still needs to be adapted to different research and clinical settings.
How might these findings be put to use?
These findings could enhance the care of dementia patients by identifying those at high risk of mortality for more personalized management strategies. On a broader scale, the study’s methodologies and insights could influence future research in predictive modeling for dementia, potentially leading to improved patient outcomes and more efficient health care systems.
What is your plan for following up on this study?
We plan to refine our dementia models by including treatment effects and genetic data, and exploring advanced deep learning techniques for more accurate predictions.
Learn more about how Mount Sinai researchers and clinicians are leveraging machine learning to improve patient lives