Detecting the impact of subject characteristics on machine learning-based diagnostic applications.

ABSTRACT: Collection of high-dimensional, longitudinal digital health data has the potential to support a wide-variety of research and clinical applications including diagnostics and longitudinal health tracking. Algorithms that process these data and inform digital diagnostics are typically developed using training and test sets generated from multiple repeated measures collected across a set of individuals. However, the inclusion of repeated measurements is not always appropriately taken into account in the analytical evaluations of predictive performance. The assignment of repeated measurements from each individual to both the training and the test sets ("record-wise" data split) is a common practice and can lead to massive underestimation of the prediction error due to the presence of "identity

SUBMITTER: Chaibub Neto E 

PROVIDER: S-EPMC6789029 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature