Adapting Machine Learning Diagnostic Models to New Populations Using a
  Small Amount of Data: Results from Clinical Neuroscience
Rongguang Wang Guray Erus Pratik Chaudhari Christos Davatzikos
Machine learning (ML) has shown great promise for revolutionizing a number of
areas, including healthcare. However, it is also facing a reproducibility
crisis, especially in medicine. ML models that are carefully constructed from
and evaluated on a training set might not generalize well on data from
different patient populations or acquisition instrument settings and protocols.
We tackle this problem in the context of neuroimaging of Alzheimer's disease
(AD), schizophrenia (SZ) and brain aging. We develop a weighted empirical risk
minimization approach that optimally combines data from a source group, e.g.,
subjects are stratified by attributes such as sex, age group, race and clinical
cohort to make predictions on a target group, e.g., other sex, age group, etc.
using a small fraction (10%) of data from the target group. We apply this
method to multi-source data of 15,363 individuals from 20 neuroimaging studies
to build ML models for diagnosis of AD and SZ, and estimation of brain age. We
found that this approach achieves substantially better accuracy than existing
domain adaptation techniques: it obtains area under curve greater than 0.95 for
AD classification, area under curve greater than 0.7 for SZ classification and
mean absolute error less than 5 years for brain age prediction on all target
groups, achieving robustness to variations of scanners, protocols, and
demographic or clinical characteristics. In some cases, it is even better than
training on all data from the target group, because it leverages the diversity
and size of a larger training set. We also demonstrate the utility of our
models for prognostic tasks such as predicting disease progression in
individuals with mild cognitive impairment. Critically, our brain age
prediction models lead to new clinical insights regarding correlations with
neurophysiological tests.