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By Parmy Olson and Brianna Abbott
Google's artificial-intelligence unit says it has developed an algorithm that can predict who is at high risk of developing a common kidney condition.
The algorithm, developed by the DeepMind Health laboratories at Google parent company Alphabet Inc., marks a new application of machine learning in health care. Yet it also shows the shortcomings of many such efforts so far, in this case partly because the algorithm is accurate a little more than half the time.
The algorithm could predict the sudden deterioration of kidney function, called acute kidney injury, two days before the potential injury with 55.8% accuracy, according to a paper published in the journal Nature on Wednesday. For the more severe kidney injuries, like cases that later required dialysis, the accuracy was closer to 90%.
The algorithm flagged two false alarms for every one correct prediction, according to the paper, with some of the false flags in patients with chronic kidney disease.
Some health-care-technology experts cautioned that such an algorithm would need further testing before being applied in a live hospital setting, which has a more diverse array of patients and often incomplete information about them. DeepMind Health had built its system using records from the U.S. Department of Veterans Affairs for patients that were 94% male.
Eric Topol, director of the Scripps Research Translational Institute who wrote a book on artificial intelligence in medicine, didn't see a problem with the model's miss rate because doctors are used to working with uncertainty, though he still doesn't think the algorithm is ready for the clinical setting.
"It's got a lot of promise," Dr. Topol said. "But it really needs to be shown with a much more general population."
Dominic King, DeepMind's medical director, said the company had evidence that it could apply its algorithm to demographics other than men, and would further develop its model using a more diverse set of patients before applying it in hospitals.
DeepMind plans to incorporate its unnamed algorithm into an app that alerts doctors of patients at risk for acute kidney injury in 12 to 18 months, Dr. King said. Such use would mark DeepMind's first application of artificial intelligence in health care, which the company is hoping to commercialize.
More than 100 doctors at eight hospitals in London are already using the app, called Streams, which can identify patients with acute kidney injury.
Currently the Streams app uses the results from a blood test -- measuring levels of a waste product in the body called creatinine -- to warn the doctors via their phones that a patient on their ward is showing signs of acute kidney injury.
"What we're talking about here is, at some point in the future, moving away from alerts generated on half a dozen individual bits of information, to a more comprehensive prediction based on the wider electronic health records," Dr. King said in an interview.
A number of firms have been bringing artificial intelligence to various aspects of health care, from research into the molecular roots of disease to determining the best course of treatment for patients.
Identifying patients at risk of developing a medical condition could help doctors and health insurers intervene ahead of time, helping the patient and reducing costs.
Acute kidney injury refers to abrupt declines in kidney function, limiting the organ's ability to filter waste from the blood. Untreated, the condition can lead to long-term kidney damage and even death. It often occurs among diabetes and hospital patients. Some cases are preventable, if they are caught early enough.
To develop a model for predicting who is at risk of the sudden episodes, DeepMind used an approach to artificial intelligence called machine learning to look for patterns in the Veterans Affairs electronic health records and build a model for predicting those at risk of acute kidney injury.
"It's trying to find patterns in that sea of very complex information," said Dr. King.
The model that emerged analyzes thousands of pieces of information about a patient, such as age, blood pressure and timing of movement from one hospital bed to another, from their electronic health records to determine risk of acute kidney injury.
DeepMind said its model can identify high-risk patients two days before doctors would normally diagnose it.
Yet some outside experts said the models weren't ready for use in the real world.
"It's pretty opaque in terms of what it teaches either renal specialists or machine-learning specialists," said Julia Powles, a law professor at the University of Western Australia who researches the way technology companies gather data.
Aside from involving patients who were nearly all male, 13% of the patients in the Veteran Affairs database had acute kidney injury compared with, for instance, 20% of people in the U.K., Ms. Powles said. The discrepancy, she said, could make it harder to transfer findings from that study to a wider patient population.
Write to Parmy Olson at email@example.com
(END) Dow Jones Newswires
July 31, 2019 13:15 ET (17:15 GMT)
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