Stroke prediction with Machine Learning
Stroke is among the most common and dangerous misdiagnosed medical conditions, and timely detection is key to effective management
Research from Florida International University’s College of Business (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 – before the results of laboratory tests or diagnostic images are available – with 83 percent accuracy.
Stroke is among the most common and dangerous misdiagnosed medical conditions, and timely detection is key to effective management. Patients who are treated within an hour of the onset of symptoms have a greater chance of surviving and avoiding long-term brain damage. Data indicates that Blacks, Hispanics, women, older adults on Medicare and residents of rural areas are less likely to be diagnosed during this crucial window. Currently used pre-hospital stroke scales miss approximately 30 percent of cases.
If a hospital is using the researchers’ ML algorithm, when a patient arrives with stroke or stroke-like symptoms, an automated, computer-assisted screening tool will quickly analyze all 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.
Read the full report here – https://business.fiu.edu/index.cfm
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