The technology used by Facebook, Google, and Amazon to turn spoken language into text, recognize faces and target advertising could help doctors combat one of the deadliest killers in American hospitals.
Clostridium difficile, a deadly 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 U.S., according to a 2015 study in the New England Journal of Medicine. Traditional methods such as monitoring hygiene and warning signs often fail to stop the disease.
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, or CDI. Using patients’ vital signs and other health records, this method—still in an experimental phase—is something both 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. healthcare industry. After years of experimentation, machine learning’s predictive powers are well-established, and it is poised to move from labs to broad real-world applications, said Zeeshan Syed, who directs Stanford University’s Clinical Inference and Algorithms Program.
Machine learning (ML) relies on artificial neural networks that roughly mimic the way animal brains learn. As a fox maps new terrain, for instance, 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 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 ML 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.
Such algorithms, now commonplace in internet commerce, finance and self-driving cars, are relatively untested in medicine and healthcare. In the U.S., the transition from written to electronic health records has been slow, and the format and quality of the data still vary by health system—and sometimes down to the medical practice level—creating obstacles for computer scientists.
But other trends are proving inexorable: Computing power has grown exponentially while getting cheaper. Once, creating a machine learning algorithm required networks of mainframe computers; now it can be done on a laptop.
