title: Machine learning, medical diagnosis, and biomedical engineering research - commentary.
abstract: A large number of papers are appearing in the biomedical engineering literature that describe the use of machine learning techniques to develop classifiers for detection or diagnosis of disease. However, the usefulness of this approach in developing clinically validated diagnostic techniques so far has been limited and the methods are prone to overfitting and other problems which may not be immediately apparent to the investigators. This commentary is intended to help sensitize investigators as well as readers and reviewers of papers to some potential pitfalls in the development of classifiers, and suggests steps that researchers can take to help avoid these problems. Building classifiers should be viewed not simply as an add-on statistical analysis, but as part and parcel of the experimental process. Validation of classifiers for diagnostic applications should be considered as part of a much larger process of establishing the clinical validity of the diagnostic technique.
authors.affiliation: Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA. kfoster@seas.upenn.edu.
authors.forename: Kenneth R
authors.initials: KR
authors.lastname: Foster
authors.forename: Robert
authors.initials: R
authors.lastname: Koprowski
authors.forename: Joseph D
authors.initials: JD
authors.lastname: Skufca
mesh_terms: D001185:Artificial Intelligence; D001698:Biomedical Engineering / Q000379:methods*; D019937:Diagnostic Techniques and Procedures; D050031:Hashimoto Disease / Q000175:diagnosis; D006801:Humans