- Google Brain is working with top hospitals to predict health outcomes from medical data.
- That data was stripped of personally-identifiable information before it was shared with Google.
- This is the latest in a series of research projects from Google to apply its machine learning expertise to health care.
By Christina Farr Reporter
Prediction algorithms for when you might get sick
Google is committed to using its machine learning technology in health care. Just one of a handful of projects underway at Google Brain, its research group, involves figuring out potential medical events based on a massive store of clinical data provided by local Bay Area hospitals. Its computers might soon determine whether patients are at a higher risk of a potentially life-threatening disease like sepsis, or are more likely to be readmitted to the hospital after discharge.
Google is building tools to predict when you’ll get sick.
The company is applying its machine learning expertise, which it originally developed for consumer products like Translate and Image Search, to health care. To get there, it worked with hospitals, including Stanford Medicine, UC San Francisco and The University of Chicago Medicine, which stripped millions of patient medical records of personally identifying data and shared them with Google’s research team, Google Brain.
“We can improve predictions for medical events that might happen to you,” said Katherine Chou, the head of product at Google Brain, in an interview with CNBC. “We have validated the data and seen promising results.” Those results will not be released until a formal review process.
Hospitals are increasingly under the gun to keep patients healthy and out of the emergency room. Increasingly, health systems are shifting away from “fee for service” models, in which they get paid for pricey tests and procedures, to “value-based care,” where they’re rewarded for improving health outcomes. That shift is a big opportunity for Silicon Valley’s technology companies and startups, which are working with existing data to help hospitals take proactive steps to keep their patients healthy.
So, for instance, a computer might soon determine the likelihood that certain patients will acquire a potentially life-threatening disease like sepsis, or end up being readmitted after being discharged from the hospital.
The advance also addresses a big problem in medical specialties like radiology and pathology, where clinicians are saddled with a massive amount of information and too little time. Even a well-trained human eye can occasionally miss something.
Many of the top hospitals have their own technology teams, but they pale in comparison to the computing talent at Google. For Atul Butte, director of the Institute of Computational Health Sciences at UCSF, the draw was Google’s amazing in-house machine learning expertise.
Butte said the project “bubbled up” because UCSF has a wealth of medical data, including admissions reports, medical records, diagnoses, lab results and so on, but it has not yet mined this information to make predictions about patient outcomes.
“This isn’t a research project,” he said. “It’s more of a scientific collaboration around improving the quality of care for patients.”