Research News

Cleveland Clinic, in collaboration with the National Football League Players Association (NFLPA), is launching a research initiative to use artificial intelligence (AI) and machine learning to better characterize neurological disease, and ultimately improve diagnosis, prognosis prediction, and disease intervention and prevention.

The nearly $1.3 million project, funded by the NFLPA, will support the creation of a coordinated research network consisting of multiple bodies drawn from healthcare facilities, research groups and private industry to develop clinically relevant, user-friendly algorithms based on large datasets.

"Neurological diseases such as Parkinson's and Alzheimer's have become public health emergencies as our population ages, and they are also of utmost important to the NFLPA," says the initiative's principal investigator, Jay Alberts, PhD, a researcher in the Department of Biomedical Engineering and vice chair of innovation for the Neurological Institute. "This collaborative research project will use machine learning techniques to address fundamental gaps in our understanding of disease processes and management."

Transforming data to a clinical decision tool

The initiative will develop machine learning algorithms related to cognitive impairment, first using longitudinal data from the general population and then data from a cohort of former NFL players. The researchers will:

• Identify various phenotypes of cognitive impairment, which will vary in severity from mild impairment to dementia, and incorporate different forms of dementia.
• Define matched control cohorts of patients who are representative of the selected phenotype but who did not develop cognitive impairment.
• Construct trajectory models of the cognitive impairment phenotypes, linking patient-entered data to specific outcomes.
• Model effective treatment interventions-including behavioral modifications, surgery and therapeutics-on disease progression.
• Create clinical decision-support tools to aid early recognition of various diagnoses involving cognitive impairment, as well as predict prognosis and guide interventions.

A rich data source

The first phase will leverage electronic health records from more than 3.5 million Cleveland Clinic patients over the past decade, including those treated for Parkinson's disease, Alzheimer's disease, multiple sclerosis and amyotrophic lateral sclerosis. Many patients had been treated for years before receiving their neurological diagnosis, so their data may contain valuable clues to their disease before a diagnosis was made.

In addition to standard medical record data, many patients' electronic health records include routinely completed self-reported outcomes and other self-entered data that reflect quality of life, depression, cognition and physical function, which can help provide a more holistic view.

"The amount and consistency of long-term patient outcomes data at Cleveland Clinic are unique in the world," says Dr. Alberts.

The coordinated research network will also acquire outside data from public, private and clinical trial datasets to further enrich the model.

Key algorithmic attributes: Clarity and usability

According to Dr. Alberts, the models are being built for clinical integration to support physician decision-making. Statisticians, mathematicians and clinical experts will collaborate on developing state-of-the-art, clinically relevant models.

"The NFLPA is interested in transforming traditional 'black box' machine learning models into 'glass box' models, without sacrificing performance," Dr. Alberts explains. "The goal is for clinicians and other users to understand why the algorithm comes to a conclusion, increasing their ability to use the tool and enhancing their trust in the process." Moreover, use of this glass box approach helps ensure that models are free from racial or sex bias.

He anticipates that the project will lead to clinically relevant algorithms within a year. They will be published and be of immediate use to the NFLPA and Cleveland Clinic.

"The resulting clinical decision-support tools should be especially useful to busy community primary care providers who can employ them to better care for neurological patients or trigger appropriate referral to specialists in neurological disease," Dr. Alberts concludes. "The tools will also help inform education and safety initiatives for the NFLPA."