Technology used by Facebook, Google and Amazon to turn spoken language into text, recognize faces and target advertising could help doctors fight one of the deadliest infections in American hospitals.
Clostridium difficile, a bacterium spread by physical contact with objects or infected people, thrives in hospitals, causing 453,000 cases a year and 29,000 deaths in the United States, according to a 2015 study in the New England Journal of Medicine. Traditional methods such as monitoring hygiene and warning signs often cannot stop the infection.
But what if it were possible to systematically target those most vulnerable to C. diff? Erica Shenoy, an infectious-disease specialist at Massachusetts General Hospital, and Jenna Wiens, a computer scientist and assistant professor of engineering at the University of Michigan, did just that when they created an algorithm to predict a patient's risk of developing a C. diff infection, known as CDI. Using patients' vital signs and other health records, this method -- still in an experimental phase -- is something the researchers want to see integrated into hospital routines.
The CDI algorithm -- based on a form of artificial intelligence called machine learning -- is at the leading edge of a technological wave starting to hit the U.S. health care industry. After years of experiments, machine learning's predictive powers are well-established, and it is ready to move from labs to broad real-world applications, said Zeeshan Syed, who directs Stanford University's Clinical Inference and Algorithms Program.
"The implications of machine learning are profound," Syed said. "Yet it also promises to be an unpredictable, disruptive force -- likely to alter the way medical decisions are made and put some people out of work."
Machine learning relies on artificial neural networks that roughly mimic the way animal brains learn.
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For example, as a fox maps new terrain, responding to smells, sights and noises, it continually adapts and refines its behavior to maximize the odds of finding its next meal. Neural networks map virtual terrains of ones and zeroes. A machine learning algorithm programmed to identify images of coffee cups might compare photos of random objects against a database of coffee cup pictures; by examining more images, it systematically learns the features to make a positive ID more quickly and accurately.
Shenoy's and Wiens' CDI algorithm analyzed a data set from 374,000 inpatient admissions to Massachusetts General Hospital and the University of Michigan Health System, seeking connections between cases of CDI and the circumstances behind them.
The records contained over 4,000 distinct variables. "We have data pertaining to everything from lab results to what bed they are in, to who is in the bed next to them and whether they are infected. We included all medications, labs and diagnoses. And we extracted this on a daily basis," Wiens said. "You can imagine, as the patient moves around the hospital, risk evolves over time, and we wanted to capture that."
As it repeatedly analyzes this data, the machine learning process extracts warning signs of disease that doctors may miss -- constellations of symptoms, circumstances and details of medical history most likely to result in infection at any point in the hospital stay.