Development of radiology prediction models using feature analysis

John A. Carrino, Lucila Ohno-Machado

Research output: Contribution to journalArticlepeer-review

Abstract

Rationale and Objectives. This article provides an introduction to prediction models and their application in diagnostic imaging research. Prediction models capitalize on the different degrees of association among variables to make a prediction of a health state, formulate a rule, or quantify individual contributions of various predictor variables. The purpose of this article is to elucidate the rationale, implication, and interpretation of prediction models using imaging features. Materials and Methods. The techniques and challenges of developing, testing, and implementing prediction models are described. Prediction model development methods are similar to data-mining techniques. Results. Learning objectives are to review prediction rule (model) methods, learn how prediction models may be applied to feature analysis, and understand the challenges of developing, testing, and implementing prediction models.

Original languageEnglish (US)
Pages (from-to)415-421
Number of pages7
JournalAcademic radiology
Volume12
Issue number4
DOIs
StatePublished - Apr 2005

Keywords

  • Data rule
  • Decision mining
  • Feature analysis
  • Prediction model
  • Prediction rule

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

Fingerprint Dive into the research topics of 'Development of radiology prediction models using feature analysis'. Together they form a unique fingerprint.

Cite this