The genetic cause of autism spectrum disorder is notoriously hard to research. Genetic markers for the disorder are tough to match from patient to patient because they're so rare--one of the most common genetic signifiers is only found in less than one percent of those diagnosed with autism. Even when genetic anomalies are found, they must be checked against family members genomes to ensure it's not attributable to a more commonly inherited mutation that doesn't cause disease. Researchers at Princeton and the Simons Foundation turned the traditional approach on its head, teaching a machine learning algorithm to look for the genetic relationships that could cause autism. The algorithm scoured a digital network of the human genome's interactions, looking for relationships and connections that are similar to those in previously-known markers for autism.
More and more, 67-year-old Washington resident Lon Coleman feels like he's wandering through a fog. He walks into the living room and forgets why, or makes a phone call only to blank on whose number he dialed. An author of three books who once wrote up to five poems a day, now the lines that spring to his mind often slip away as soon as he puts pencil to paper. Sometimes the fog clears, and when his memory comes back, "it's amazing," he says. "Sometimes it doesn't, I have to admit."
IBM Research Data Scientist Eric Clark explores wearable technologies that could help monitor and analyze biological data from study subjects on Thursday, April 7, 2016 at IBM's T. J. Watson Research Center in Yorktown, NY. IBM and Pfizer are teaming up in an effort to give Parkinson's Disease research an analytical edge. The tech titan and the pharmaceutical giant plan to utilize non-invasive wearables to collect and monitor real-time patient data. The end goal of the research project is to advance the way neurological diseases are diagnosed and treated while also speeding up clinical trials to bring new drugs to market. The study, which is expected to last up to three years, will glean patient data from a system of sensors, wearables and mobile devices that will monitor patients around the clock, not just episodically.
Patient Number Two was born to first-time parents, late 20s, white. The pregnancy was normal and the birth uncomplicated. But after a few months, it became clear something was wrong. The child had ear infection after ear infection and trouble breathing at night. He was small for his age, and by his fifth birthday, still hadn't spoken.
Big data in healthcare can now be measured in exabytes, and every day more data is being thrown into the mix in the form of patient-generated information, wearables and EHR systems. Traditional methods of analysis are no longer enough to handle, let alone take proper advantage of, the potential that healthcare data holds. This is where deep machine learning (or simply, "deep learning") comes in. However, its greatest power lies in its ability to extract value from data in ways that humans and traditional machine learning methods cannot. Deep machine learning has applications in a number of healthcare areas.