Model that uses machine learning methods and patient data at hospital arrival predicts strokes more accurately

Stroke is among the most dangerous and commonly misdiagnosed medical conditions. Black and Hispanic people, women, older people on Medicare, and people in rural areas are less likely to be diagnosed in time for treatment to be effective. In a new study, researchers used machine learning methods and data available when patients enter the hospital to develop a model that predicts strokes with more accuracy than current models.

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