Yubin Park, CTO of Accordion Health, explains why traditional off-the-shelf machine learning models fail in clinical healthcare settings.
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Park: Healthcare professionals deeply mistrust the machine learning community
“If you talk to any, ah, any people in the healthcare industry, ah, they have a lot of big, ah, mistrust to the machine learning communities.”
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Park: Precision medicine is just the tip of the iceberg
“Precision medicine is actually a very, ah, tip of iceberg, which is, ah, actually a much larger stream of, ah, revolution in healthcare, ah, which is called a personalized healthcare.”
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Park: Naive ML models fail in healthcare due to patient bias
“Some people may think this is a very simple machine learning problem. Like, okay, so let's grab some data, analyze it, and then build some sort of model to predict their cost and outcomes, and let's just apply in the reality. Without knowing the fact that the …”
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Park: Post-discharge optimization fits poorly into standard ML frameworks
“This problem cannot be easily cast into a classification regression problems. Ah, it's not like a traditional machine learning framework.”
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Park: Personalized healthcare extends to benefit plans and financial incentives
“So what I'm saying here is, ah, every axis, all the components in the healthcare can be personalized. It's not just about the treatment and prevention. It's, ah, it's about, like, service bundle. It's about the benefit plan. It's about the financial incentives…”
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Park: Crawling scientific literature can model healthcare counterfactuals
“Actually, you can crawl all the scientific articles about because they already performed the cohort studies, A-B testing, all those things. You can analyze all those Of previous studies. And use those things to make to answer the what if questions to the, for …”