An Improvised Classification Model for Predicting Delirium

Stud Health Technol Inform. 2019 Aug 21:264:1566-1567. doi: 10.3233/SHTI190537.

Abstract

With the vast increase of digital healthcare data, there is an opportunity to mine the data for understanding inherent health patterns. Although machine-learning techniques demonstrated their applications in healthcare to answer several questions, there is still room for improvement in every aspect. In this paper, we are demonstrating a method that improves the performance of a delirium prediction model using random forest in combination with logistic regression.

Keywords: Algorithms; Delirium; Logistic Models.

MeSH terms

  • Delirium*
  • Humans
  • Logistic Models
  • Machine Learning*