Using machine learning to model dose-response relationships.

Journal: Journal of evaluation in clinical practice
PMID: 27240883

Abstract

RATIONALE, AIMS AND OBJECTIVES: Establishing the relationship between various doses of an exposure and a response variable is integral to many studies in health care. Linear parametric models, widely used for estimating dose-response relationships, have several limitations. This paper employs the optimal discriminant analysis (ODA) machine-learning algorithm to determine the degree to which exposure dose can be distinguished based on the distribution of the response variable. By framing the dose-response relationship as a classification problem, machine learning can provide the same functionality as conventional models, but can additionally make individual-level predictions, which may be helpful in practical applications like establishing responsiveness to prescribed drug regimens.

Authors

  • Ariel Linden
    Linden Consulting Group, LLC, Ann Arbor, MI, USA.
  • Paul R Yarnold
    Optimal Data Analysis, LLC, Chicago, IL, USA.
  • Brahmajee K Nallamothu
    From the Division of Cardiology, Department of Medicine; Cardiovascular Research Institute; Institute for Human Genetics; and Institute for Computational Health Sciences, University of California San Francisco, and California Institute for Quantitative Biosciences (R.C.D.); and VA Health Services Research and Development Center for Clinical Management Research, VA Ann Arbor Healthcare System, MI; Michigan Center for Health Analytics and Medical Prediction (M-CHAMP), Department of Internal Medicine, University of Michigan Medical School, Ann Arbor (B.K.N.).

Keywords

External Resources

Popular Topics
Recent Journals