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Can 10 Million Patient Records Help Reduce Bias In Healthcare AI? This Startup Is Banking On it
Posted
January 30, 2024
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January 30, 2024
1,590
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1.89%
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4:22Now PlayingIn 2023, cardiologist David Ouyang developed an algorithm to try and catch heart failure earlier. It analyzes electrocardiograms or ECGs – recordings of electrical heartbeat signals – to predict if a patient has heart failure, flagging them to the doctor so they get further testing.
But Ouyang, who works at Cedars-Sinai in Los Angeles, needed to be sure that the algorithm would be accurate for both men and women — as well as people of different races and ethnicities or who live in different places, factors that can impact the symptoms of heart failure. In other words, he needed to make sure the algorithm wouldn’t have any bias, a long-standing issue for algorithms that are trained using historical data from a limited number of people. If that data is not representative of all types of patients, then the resulting algorithm won’t be accurate for everyone.
To get answers, he turned to Dandelion Health, a startup that’s working on a solution to help check for potential bias of this type in healthcare algorithms. Dandelion is creating a massive, de-identified dataset from millions of patient records so developers can build and test the performance of their algorithms across diverse types of patients. Ouyang said working with Dandelion gave him “more confidence” that his algorithm is “mature and ready to deploy.”
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