According to the team, their program is right 90% of the time. It will be used to provide appropriate end-of-life car to hospital patients with a terminal disease.
The program uses artificial intelligence and was trained through analysis of 160,000 adult and child patient files from Stanford and Lucile Packard Children's hospital.
It looked at things in health records such as diagnosis, procedures performed and which medicine a patient was taking. When the algorithm was applied to 40,000 active patients (it was asked to predict which would die in the next three to twelve months) it was correct in 90% of cases.
"The scale of data available allowed us to build an all-cause mortality prediction model, instead of being disease or demographic specific," said Anand Avati, a member of Stanford University's AI Lab, reported IBTimes .
The researchers plan to continue refining the system with more data before it is rolled out to hospitals and medical staff.
Kenneth Jung, a research scientist at Stanford University said: "We think that keeping a doctor in the loop and thinking of this as 'machine learning plus the doctor' is the way to go as opposed to blindly doing medical interventions based on algorithms... that puts us on firmer ground both ethically and safety-wise"
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