Study in Frontiers in Physiology finds sex-specific criteria in machine learning models may reduce underdiagnosis of heart disease in women.
New machine learning models using sex-specific criteria may help overcome the underdiagnosis of women's heart disease, according to a study published in Frontiers in Physiology. Cardiovascular disease in women is underdiagnosed compared to men, possibly due to sex-neutral diagnostic criteria. The researchers found that using sex-specific criteria could reduce underdiagnosis in women, and an electrocardiogram (EKG) is the best exam to improve detection of cardiovascular disease in both sexes.
April 23, 2024
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