Researchers at the University of Southern California have discovered "hidden" indicators of Alzheimer's in medical data that could result in earlier diagnosis of the disease and better prognosis for patients. Using machine learning, USC researchers identified potential blood-based markers of Alzheimer's disease that could be detected with a routine blood test. "This type of analysis is a novel way of discovering patterns of data to identify key diagnostic markers of disease," said Paul Thompson, associate director of the USC Mark and Mary Stevens Neuroimaging and Informatics Institute and professor in USC's Keck School of Medicine. "In a very large database of health measures, it helped us discover predictive features of Alzheimer's disease that nobody suspected were there." Also See: MRI brain scans better ID people likely to develop Alzheimer's In their study, published in Frontiers in Aging Neuroscience, the USC research team analyzed medical data in the Alzheimer's Disease Neuroimaging Initiative database--collected from 829 older adults--to identify predictors of cognitive decline and brain atrophy during a one-year period.
Research and development for new drugs is both an expensive and lengthy process, often lasting years, if not decades. With the development of artificial intelligence technology however, this process is both becoming more cost-efficient and shorter, something that is expected to exponentially accelerate the development of new drugs. According to Brendan Frey, founder and CEO of AI-based drug discovery company, Deep Genomics, developing drugs has traditionally been like gambling, "It's like the Big Pharma companies come into a casino, put a million-dollar coin into a slot machine and with some probability like 10% or something, they get a win (Robertson: 2019)." This is where AI comes in. Until now, Deep Genomics has developed over 20 machine learning systems trained from both public and proprietary data to screen for disease-causing mutations while looking for drug targets.
The greatest buzzwords to come from 2018 were blockchain and artificial intelligence (AI). Blockchain has had it's highs and lows, however AI is trending much more positively. Artificial Intelligence isn't brand new, we've seen the likes of Alexa, Siri, Cortana, and Google not only in our hands but now welcomed into our homes. All of these have gained huge momentum at a consistent pace for the last few years. Other well-known established tech companies, governments, and cutting-edge startups are all looking for a share of the expanding market, and Healthcare companies are right there with them.
Twin registers have been used for years to study the roles that genetics and the environment play in people's health. In the mid-1980s, the government sought information from 30,000 veterans who were twins to compare the health of men who served in Vietnam with brothers who did not. Other studies have used twin registers to try to determine who gets Alzheimer's disease, why some people develop attention deficit hyperactivity disorder and what influences a woman's ability to orgasm.