Improving Alzheimer’s Disease Clinical Trials’ Design by Machine Learning Models


Despite the high burden of Alzheimer’s disease and increased efforts in research, the success rate of the pharmaceutical randomized clinical trials (RCTs) for dementia drugs has been abysmally low in the last two decades. This is thought to be partly due to clinical heterogeneity and difficulty in predicting which person will have cognitive decline during the one to five years of follow-up in a clinical trial. One strategy to improve design of clinical trials and boost their power is using predictive models that effectively can estimate probability of disease progression and cognitive decline. In recent years, innovative machine learning techniques have been used increasingly in pharmaceutical research and development for prediction of clinical outcomes and response to treatments in various fields of medicine. However, to date, such techniques have not been used in design or conduct of AD trials. This project aims to develop a machine learning platform that can be practically used in design and conduct of future clinical trials. We will use advanced machine learning and deep learning methods and data from previously completed RCTs for treatment of mild-to-moderate AD to develop precise models that can predict disease progression and rate of cognitive decline.


Funding to Date



Clinical Trial Design, Drug Development


Ali Ezzati, M.D.

Richard Lipton, M.D.

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