title: Using electronic health record collected clinical variables to predict medical intensive care unit mortality.
abstract: BACKGROUND
  Clinical decision support systems are used to help predict patient stability and mortality in the Intensive Care Unit (ICU). Accurate patient information can assist clinicians with patient management and in allocating finite resources. However, systems currently in common use have limited predictive value in the clinical setting. The increasing availability of Electronic Health Records (EHR) provides an opportunity to use medical information for more accurate patient stability and mortality prediction in the ICU.
  
  
  OBJECTIVE
  Develop and evaluate an algorithm which more accurately predicts patient mortality in the ICU, using the correlations between widely available clinical variables from the EHR.
  
  
  METHODS
  We have developed an algorithm,AutoTriage, which uses eight common clinical variables from the EHR to assign patient mortality risk scores. Each clinical variable produces a subscore, and combinations of two or three discretized clinical variables also produce subscores. A combination of weighted subscores produces the overall score. We validated the performance of this algorithm in a retrospective study on the MIMIC III medical ICU dataset.
  
  
  RESULTS
  AutoTriage12 h mortality prediction yields an Area Under Receiver Operating Characteristic value of 0.88 (95% confidence interval 0.86 to 0.88). At a sensitivity of 80%,AutoTriagemaintains a specificity of 81% with a diagnostic odds ratio of 16.26.
  
  
  CONCLUSIONS
  Through the multidimensional analysis of the correlations between eight common clinical variables,AutoTriageprovides an improvement in the specificity and sensitivity of patient mortality prediction over existing prediction methods.
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Jacob
authors.initials: J
authors.lastname: Calvert
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Qingqing
authors.initials: Q
authors.lastname: Mao
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Jana L
authors.initials: JL
authors.lastname: Hoffman
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Melissa
authors.initials: M
authors.lastname: Jay
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Thomas
authors.initials: T
authors.lastname: Desautels
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Hamid
authors.initials: H
authors.lastname: Mohamadlou
authors.affiliation: Kaiser Permanente South San Francisco Medical Center, South San Francisco, CA, USA; Department of Emergency Medicine, University of California San Francisco, San Francisco, CA, USA.
authors.forename: Uli
authors.initials: U
authors.lastname: Chettipally
authors.affiliation: Dascena Inc., Hayward, CA, USA.
authors.forename: Ritankar
authors.initials: R
authors.lastname: Das
keywords: Clinical decision support systems; Electronic health records; Medical informatics; Mortality prediction