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An Algorithm That Can Identify Stroke Patients And At-risk Patients Sooner 

An Algorithm That Can Identify Stroke Patients And At-risk Patients Sooner

Research led by FIU Business found that a machine learning (ML) algorithm that uses hospital data and social determinants of health data can help diagnose a stroke quickly – with 83% accuracy.

The discovery will allow medical personnel to identify stroke patients and those at risk of stroke sooner, improving their results. Stroke is among the most common and dangerous misdiagnosed conditions.

The ML algorithm was developed using data of suspected stroke patients – such as age, race and a number of underlying conditions. The algorithm "learns" and improves as it analyzes more data.

Social determinants of health (SDoH) are non-medical factors, such as income and housing stability, access to transportation, race, and social isolation, that affect a wide range of health outcomes. Using all of this information, the algorithm helps to quickly diagnose a potential stoke patient.

"Our algorithm can incorporate a lot of variables to analyze and interpret complex patterns, which will allow emergency department care teams to make better and faster decisions," said Min Chen, associate professor of information systems and business analytics at FIU Business and one of the researchers.

"Our algorithm can incorporate a lot of variables to analyze and interpret complex patterns, which will allow emergency department care teams to make better and faster decisions."

– Min Chen

Published in the January 2023 issue of the Journal of Medical Internet Research, the study outlines how researchers developed the ML stroke prediction algorithm using emergency department and hospitalization records from hospitals in the state of Florida from 2012 to 2014, merged with SDoH data from the American Community Survey.

If a hospital is using the researchers' ML algorithm, when a patient arrives with stroke or stroke-like symptoms, an automated, computerassisted screening tool will quickly analyze all of the patient's information. If it predicts that the patient is at a high risk for stroke, a pop-up will be triggered to alert the emergency department team.

This type of technology is undergoing pilot testing in ERs of several prominent healthcare systems.

To facilitate the widespread implementation of this kind of ML-enabled decision support systems, it is important to have extensive health information exchange that enables patients' data to flow seamlessly across providers, as well as a standardized way to collect granular SDoH data.

Chen conducted the study with Xuan Tan of Clara University and Rema Padman of Carnegie Mellon University.