Machine learning could improve the ability to determine whether a new drug works in the brain, and could enable researchers to detect drug effects that would be missed by conventional statistics tests, according to researchers at University College London (UCL) in the U.K.
To test this theory, the researchers examined large-scale data from stroke patients, extracting the anatomical pattern of brain damage and creating the largest collection of anatomically registered images of stroke ever assembled.
The team used gaze direction as an index of the impact of stroke, objectively measuring from the eyes as seen on imaging scans. They then simulated a large-scale meta-analysis of a set of hypothetical drugs to determine if treatment effects of different magnitudes overlooked by conventional statistical analysis could be identified with machine learning.
"The real value of machine learning lies not so much in automating things we find easy to do naturally, but formalizing very complex decisions," says UCL's Parashkev Nachev.
From University College London
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