When it comes to predicting an individual’s risk of suffering a heart attack or stroke over the next 10 years, machine learning models are no more consistent—and so no more reliable as guides to care pathways—than traditional statistical techniques.
The researchers making the conclusion do so after analyzing medical records from 3.6 million patients in the U.K. who were tracked by registry over a 20-year period ending in 2018.
The team looked at 19 different prediction methods, 12 of which represented some type of machine learning.
The disappointing showings were at their worst when a model in either category assumed a patient was free of any cardiovascular disease when their records were inadvertently “censored,” meaning their clinical information stopped getting updated for whatever reason.
Logistic models and commonly used machine learning models “should not be directly applied to the prediction of long-term risks without considering censoring,” the authors write in their study, published this month in The BMJ. “Survival models that consider censoring and that are explainable … are preferable.”
Credit: Google News
