Developing a New Score: How Machine Learning Improves Risk Prediction
Composite risk scores have been used for decades to identify disease risk and health status in the general population. However, current approaches often fail to identify people who would benefit from intervention or recommend unnecessary intervention. Machine learning promises to improve accuracy, ensuring targeted treatment for patients that need it and reducing unnecessary intervention.
Framingham Risk Score, the gold standard for predicting the likelihood of heart disease, predicts hospitalizations with about 56% accuracy. It uses factors such as age, gender, smoking, cholesterol levels, and systolic blood pressure to calculate risk. It doesn't ask for family history, ethnicity, or physical activity level, all of which play a role in heart health.
A stroke-risk scoring system called CHADS2 (Cardiovascular Disease-Heart Attack-Diabetes) fares better, with an 82% accuracy rate, but can misclassify high-risk patients as moderate risk, according to a 2001 study.
Because machine learning algorithms analyze large data sets quickly, they predict disease risk with greater accuracy and can predict a patient's odds of hospital admission and readmission. Armed with this information, hospitals can administer medication or make lifestyle recommendations to keep patients out of the hospital. Considering Medicare payments (or lack of penalties, rather) hinge on readmission rates, hospitals have a financial incentive to leverage their data.
The availability of electronic health records...
Source: MDDI - Category: Medical Devices Authors: Heather R. Johnson Tags: R & D Source Type: news
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