An artificial intelligence (AI) algorithm has produced another significant breakthrough using attention mechanisms and a convolutional neural network to accurately identify tell-tale signs of Alzheimer's. The AI tool developed by the Stevens Institute of Technology is said to be able to explain its conclusions, thus enabling human experts to check the accuracy of its diagnosis by up to 95%. AI has made huge strides in the medical sector and this latest news is further evidence that the speed at which the technology is moving shows no signs of ceasing any time soon. The algorithm is trained to identify subtle linguistic patterns previously overlooked by using texts composed by both healthy subjects and known Alzheimer's sufferers. The team of researchers then converted each sentence into a unique numerical sequence, or vector, representing a specific point in a 512-dimensional space.
Alzheimer's disease is becoming increasingly prevalent as life expectancies lengthen. But the complexity of the condition makes it hard to find effective treatments. One way to expedite the search that's yielded promising results is using AI to find existing drugs that could be repurposed to combat the disorder. Harvard researchers recently used the approach to identify 80 candidate medications that merit further investigation. They discovered the contenders through a framework they call DRIAD (Drug Repurposing In Alzheimer's Disease).
Accurate and detailed models of the progression of neurodegenerative diseases such as Alzheimer's (AD) are crucially important for reliable early diagnosis and the determination and deployment of effective treatments. In this paper, we introduce the ALPACA (Alzheimer's disease Probabilistic Cascades) model, a generative model linking latent Alzheimer's progression dynamics to observable biomarker data. In contrast with previous works which model disease progression as a fixed ordering of events, we explicitly model the variability over such orderings among patients which is more realistic, particularly for highly detailed disease progression models. We describe efficient learning algorithms for ALPACA and discuss promising experimental results on a real cohort of Alzheimer's patients from the Alzheimer's Disease Neuroimaging Initiative. Papers published at the Neural Information Processing Systems Conference.
Alzheimer disease (AD) is a neurodegenerative disorder characterized by progressive impairment of memory and other cognitive functions. Regression analysis has been studied to relate neuroimaging measures to cognitive status. However, whether these measures have further predictive power to infer a trajectory of cognitive performance over time is still an under-explored but important topic in AD research. We propose a novel high-order multi-task learning model to address this issue. The proposed model explores the temporal correlations existing in data features and regression tasks by the structured sparsity-inducing norms.