Google Using FHIR, Deep Learning for Healthcare Predictive Analytics
As part of Google’s ongoing quest to enhance the accuracy and usefulness of machine learning, researchers from the tech giant are expanding their partnerships with leading healthcare organizations to create more advanced EHR predictive analytics.
Collaborations with the University of Chicago, Stanford University, and UC San Francisco are leveraging deep learning alongside innovative data standards like FHIR to tackle some of healthcare’s most intractable problems of cost, quality, and patient safety.
The discipline of machine learning – which can also be known as deep learning, cognitive computing, or artificial intelligence – has been advancing rapidly in just the past few months as developers from all industries throw resources into their data science divisions.
Healthcare has been an early beneficiary of this trend. Numerous projects from Google and others have highlighted the value of machine learning for diagnostics and predictive analytics.
In March, Google researchers published a paper explaining how machine learning could play an important role in pathology by identifying metastasized cancer tissue nearly as accurately as a human pathologist.
Deep learning algorithms produced “heat maps” of extremely large images of biopsy slides to identify the presence of tumor cells. The algorithm achieved 89 percent accuracy when identifying breast cancer that had spread to lymph nodes, compared to just 73 percent accuracy from a human pathologist spending 30 hours on the task.
Researchers at Stanford University have also harnessed the power of Google’s machine learning to identify skin cancers. In a paper published in Nature this year, the team noted that the artificial intelligence algorithm matched the diagnostic performance of 21 flesh-and-blood dermatologists.
Katherine Chou from the Google Brain team believes that these initiatives are just the beginning of what machine learning can do for providers and patients.
“We’re ready to do more: machine learning is mature enough to start accurately predicting medical events—such as whether patients will be hospitalized, how long they will stay, and whether their health is deteriorating despite treatment for conditions such as urinary tract infections, pneumonia, or heart failure,” Chou wrote in a Google blog post announcing the company’s latest healthcare research partnership with the University of Chicago.
“Advanced machine learning can discover patterns in de-identified medical records (that is, stripped of any personally identifiable information) to predict what is likely to happen next, and thus, anticipate the needs of the patients before they arise.”
The need for these big data analytics tools is “urgent and immense,” she added.
