Predictive Modeling

Artificial intelligence in medicine isn’t limited to image recognition. In some cases, the A.I. doesn’t even need to see images to make a diagnostic prediction. Take the “Deep Patient” project. Researchers at the Icahn School of Medicine at Mount Sinai gave their A.I. 700,000 electronic health records (EHRs), hoping that the machine would be able to make the connections between disease or condition predictors, and eventual diagnosis.

Once the A.I. had been given the chance to parse all the information (patient X developed lung cancer, patient Y developed heart disease), it was tested. It was given data on 76,000 patients whose diagnosis was known, but not given to the computer. Their results were impressive, drastically outperforming “evaluations based only on raw EHR data, doing particularly well at predicting severe diabetes, schizophrenia, and various cancers.”

They weren’t the only ones who had positive results. A team of UK researchers performed a similar test, to see if their learning algorithm could accurately predict heart attacks. It “correctly predicted 7.6% more events than the ACC/AHA method, and it raised 1.6% fewer false alarms.” What’s more, the machine used different guidelines to make its judgement calls, highlighting the fact that doctors may be using the wrong metrics:

"Several of the risk factors that the machine-learning algorithms identified as the strongest predictors are not included in the ACC/AHA guidelines, such as severe mental illness and taking oral corticosteroids. Meanwhile, none of the algorithms considered diabetes, which is on the ACC/AHA list, to be among the top 10 predictors."

The corporate world has been benefiting from “big data” for several years, and now, with the help of A.I., medicine is too.