title: Can Cluster-Boosted Regression Improve Prediction of Death and Length of Stay in the ICU?
abstract: Sharing of personal health information is subject to multiple constraints, which may dissuade some organizations from sharing their data. Summarized deidentified data, such as that derived from k-means cluster analysis, is subject to far fewer privacy-related constraints. In this paper, we examine the extent to which analysis of clustered patient types can match predictions made by analyzing the entire dataset at once. After reviewing relevant literature, and explaining how data are summarized in each cluster of similar patients, we compare the results of predicting death, and length of stay (LOS) in the ICU1ICU: Intensive care unit.
authors: Mahsa
authors: M
authors: Rouzbahman
authors: Aleksandra
authors: A
authors: Jovicic
authors: Mark
authors: M
authors: Chignell
mesh_terms: D016000:Cluster Analysis; D057225:Data Mining / Q000379:methods*; D057286:Electronic Health Records; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007902:Length of Stay / Q000706:statistics & numerical data*; D008490:Medical Informatics / Q000379:methods*