News Release

Ai finds key signs that predict patient survival across dementia types

Findings support early identification of high-risk patients, enabling more targeted and personalized care

Peer-Reviewed Publication

The Mount Sinai Hospital / Mount Sinai School of Medicine

Survival Analysis Based On Dementia Subtypes


Survival Analysis Based On Dementia Subtypes

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Credit: Zhang & Song et al., Communications Medicine

New York, NY [February 28, 2024]—Researchers at the Icahn School of Medicine at Mount Sinai and others have harnessed the power of machine learning to identify key predictors of mortality in dementia patients.

The study, published in the February 28 online issue of Communications Medicine [10.1038/s43856-024-00437-7], addresses critical challenges in dementia care by pinpointing patients at high risk of near-term death and uncovers the factors that drive this risk. Unlike previous studies that focused on diagnosing dementia, this research delves into predicting patient prognosis, shedding light on mortality risks and contributing factors in various kinds of dementia.

Dementia has emerged as a major cause of death in societies with increasingly aging populations. However, predicting the exact timing of death in dementia cases is challenging due to the variable progression of cognitive decline affecting the body’s normal functions, say the researchers.

"Our findings are significant as they illustrate the potential of machine learning models to accurately anticipate mortality risk in dementia patients over varying timeframes," said corresponding author Kuan-lin Huang, PhD, Assistant Professor of Genetics and Genomic Sciences at Icahn Mount Sinai. “By pinpointing a concise set of clinical features, including performance on neuropsychological and other available testing, our models empower health care providers to make more informed decisions about patient care, potentially leading to more tailored and timely interventions."

Using data from the U.S. National Alzheimer’s Coordinating Center that included 45,275 participants and 163,782 visit records, the study created machine learning models based on clinical and neurocognitive features. These models predicted mortality at one, three, five, and 10 years. The study developed specific models for eight types of dementia through stratified analyses.

The study also found that neuropsychological test results were a better predictor of mortality risk in dementia patients than age-related factors such as cancer and heart disease, underscoring dementia's significant role in mortality among those with neurodegenerative conditions.

"The implications of our research extend beyond clinical practice, as it underscores the value of machine learning in unraveling the complexities of diseases like dementia. This study lays the groundwork for future investigations into predictive modeling in dementia care,” says Dr. Huang. “However, while machine learning holds great promise for improving dementia care, it's important to remember that these models aren't crystal balls for individual outcomes. Many factors, both personal and medical, shape a patient's journey."

Next, the research team plans to refine their models by incorporating treatment effects and genetic data and exploring advanced deep-learning techniques for even more precise predictions.

Given the aging population, dementia has emerged as an increasingly pressing public health concern, ranking as the seventh leading cause of death and the fourth most burdensome disease or injury in the United States in 2016, based on years of life lost. As of 2022, Alzheimer’s and other dementias cost an estimated $1 trillion annually, impacting approximately 6.5 million Americans and 57.4 million people worldwide, with projections suggesting a tripling by 2050.

The paper is titled “Machine learning models identify predictive features of patient mortality across dementia types.”

The remaining authors on the paper are Jimmy Zhang (currently at Columbia University); Luo Song (currently an MD candidate at The University of Queensland, Australia); Zachary Miller (University of Washington, Seattle); and Kwun C. G. Chan, PhD (University of Washington, Seattle).

Please see Communications Medicine [10.1038/s43856-024-00437-7for funding details.


About the Icahn School of Medicine at Mount Sinai

The Icahn School of Medicine at Mount Sinai is internationally renowned for its outstanding research, educational, and clinical care programs. It is the sole academic partner for the eight- member hospitals* of the Mount Sinai Health System, one of the largest academic health systems in the United States, providing care to a large and diverse patient population.  

Ranked 13th nationwide in National Institutes of Health (NIH) funding and among the 99th percentile in research dollars per investigator according to the Association of American Medical Colleges, Icahn Mount Sinai has a talented, productive, and successful faculty. More than 3,000 full-time scientists, educators, and clinicians work within and across 44 academic departments and 36 multidisciplinary institutes, a structure that facilitates tremendous collaboration and synergy. Our emphasis on translational research and therapeutics is evident in such diverse areas as genomics/big data, virology, neuroscience, cardiology, geriatrics, as well as gastrointestinal and liver diseases. 

Icahn Mount Sinai offers highly competitive MD, PhD, and Master’s degree programs, with current enrollment of approximately 1,300 students. It has the largest graduate medical education program in the country, with more than 2,000 clinical residents and fellows training throughout the Health System. In addition, more than 550 postdoctoral research fellows are in training within the Health System. 

A culture of innovation and discovery permeates every Icahn Mount Sinai program. Mount Sinai’s technology transfer office, one of the largest in the country, partners with faculty and trainees to pursue optimal commercialization of intellectual property to ensure that Mount Sinai discoveries and innovations translate into healthcare products and services that benefit the public.

Icahn Mount Sinai’s commitment to breakthrough science and clinical care is enhanced by academic affiliations that supplement and complement the School’s programs.

Through the Mount Sinai Innovation Partners (MSIP), the Health System facilitates the real-world application and commercialization of medical breakthroughs made at Mount Sinai. Additionally, MSIP develops research partnerships with industry leaders such as Merck & Co., AstraZeneca, Novo Nordisk, and others.

The Icahn School of Medicine at Mount Sinai is located in New York City on the border between the Upper East Side and East Harlem, and classroom teaching takes place on a campus facing Central Park. Icahn Mount Sinai’s location offers many opportunities to interact with and care for diverse communities. Learning extends well beyond the borders of our physical campus, to the eight hospitals of the Mount Sinai Health System, our academic affiliates, and globally.


Mount Sinai Health System member hospitals: The Mount Sinai Hospital; Mount Sinai Beth Israel; Mount Sinai Brooklyn; Mount Sinai Morningside; Mount Sinai Queens; Mount Sinai South Nassau; Mount Sinai West; and New York Eye and Ear Infirmary of Mount Sinai.









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