The most in-depth analysis of human brain tissue ever done in Alzheimer's disease has found evidence for the controversial theory that viruses play a role in the condition. If true, it could mean that some instances of Alzheimer's might be treated with anti-viral drugs. Alzheimer's is the most common cause of dementia, affecting some 47 million people worldwide.
For many complex diseases, there is a wide variety of ways in which an individual can manifest the disease. The challenge of personalized medicine is to develop tools that can accurately predict the trajectory of an individual's disease, which can in turn enable clinicians to optimize treatments. We represent an individual's disease trajectory as a continuous-valued continuous-time function describing the severity of the disease over time. We propose a hierarchical latent variable model that individualizes predictions of disease trajectories. This model shares statistical strength across observations at different resolutions--the population, subpopulation and the individual level. We describe an algorithm for learning population and subpopulation parameters offline, and an online procedure for dynamically learning individual-specific parameters. Finally, we validate our model on the task of predicting the course of interstitial lung disease, a leading cause of death among patients with the autoimmune disease scleroderma. We compare our approach against state-of-the-art and demonstrate significant improvements in predictive accuracy.
Out of the total number, 48 were scans of people with the disease, while 48 were scans of people who suffered from mild cognitive impairment and eventually developed full-blown Alzheimer's. The AI was able to diagnose Alzheimer's 86 percent of the time. More importantly, it was able to detect mild cognitive impairment 84 percent of the time, making it a potentially effective tool for early diagnosis. With more samples and further development, though, the AI could become more accurate until it's reliable enough to be used as a non-invasive early detection system.
We haven't reached widespread adoption of AI in enterprises yet, but it's coming. More enterprises have adopted stream processing, though, because it can enable a variety of mission-critical use cases. A new AI algorithm could help detect Alzheimer's disease early. A new report explains why a number of factors determine whether you need cloud or on-premise solutions for AI and HPC.