Training Algorithms to Prevent Death Spirals in Hospitals

Hospitals collect all manner of data on patients, from doctors’ notes to test results to measurements such as pulse and blood pressure. Doctors have long known the data points can be leading indicators of potentially fatal medical emergencies. If physicians were able to analyze the data to identify when serious deterioration starts, they could save lives. Now computers have started doing just that.

Researchers are using artificial intelligence algorithms to comb through the records of patients who suffered, say, sepsis or lung failure. The software examines data points from hours or even days before the onset of a crisis to see which combinations of factors might have predicted a fatal condition. The algorithm trains itself to model the warning signs. “It’s the idea that you could recognize these risk points or the point of tipping toward the cascade,” says Eric Horvitz, a research director at Microsoft Corp. who, together with scientists from the University of Washington, is studying AI methods for early detection of conditions such as heart, lung, and kidney failure. They’re using 10 years of data from 80,000 patients.