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takyon writes:

Google is training machines to predict when a patient will die

A woman with late-stage breast cancer went to a hospital, fluids flooding her lungs. She saw two doctors and got a radiology scan. The hospital's computers read her vital signs and estimated a 9.3% chance she would die during her stay.

Then came Google's turn. An new type of algorithm created by the company read up on the woman — 175,639 data points — and rendered its assessment of her death risk: 19.9%. She died in a matter of days.

The harrowing account of the unidentified woman's death was published by Google in May in research highlighting the healthcare potential of neural networks, a form of artificial intelligence software that is particularly good at using data to automatically learn and improve. Google had created a tool that could forecast a host of patient outcomes, including how long people may stay in hospitals, their odds of readmission and chances they will soon die.

What impressed medical experts most was Google's ability to sift through data previously out of reach: notes buried in PDFs or scribbled on old charts. The neural net gobbled up all this unruly information then spat out predictions. And it did so far faster and more accurately than existing techniques. Google's system even showed which records led it to conclusions.

Scalable and accurate deep learning with electronic health records (open, DOI: 10.1038/s41746-018-0029-1) (DX)

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