title: A computational approach to mortality prediction of alcohol use disorder inpatients.
abstract: Health information technologies can assist clinicians in the Intensive Care Unit (ICU) by providing additional analysis of patient stability. However, because patient diagnoses can be confounded by chronic alcohol use, the predictive value of existing systems is suboptimal. Through the use of Electronic Health Records (EHR), we have developed computer software called AutoTriage to generate accurate predictions through multi-dimensional analysis of clinical variables. We analyze the performance of AutoTriage on the Alcohol Use Disorder (AUD) subpopulation in this study, and build on results we reported for AutoTriage performance on the general population in previous work.
  
  
  
  AUD-related ICD-9 codes were used to obtain a patient population from MIMIC III ICU dataset for a retrospective study. Patient mortality risk score is generated through analysis of eight EHR-based clinical variables. The score is determined by combining weighted subscores, each of which are obtained from singlets, doublets or triplets of one or more of the eight continuous-valued clinical variable inputs. A temporally updating risk score is computed with a continuously revised 12-hour mortality prediction.
  
  
  
  Among AUD patients, in a non-overlapping test set, AutoTriage outperforms existing systems with an Area Under Receiver Operating Characteristic (AUROC) value of 0.934 for 12-h mortality prediction. At a sensitivity of 90%, AutoTriage achieves a specificity of 80%, positive predictive value of 40%, negative predictive value of 89%, and an Odds Ratio of 36.
  
  
  
  For mortality prediction, AutoTriage demonstrates improvements in both the accuracy and the Odds Ratio over current systems among the AUD patient population.
authors.affiliation: Dascena, Inc, United States.
authors.forename: Jacob
authors.initials: J
authors.lastname: Calvert
authors.affiliation: Dascena, Inc, United States.
authors.forename: Qingqing
authors.initials: Q
authors.lastname: Mao
authors.affiliation: Department of Medicine, Stanford University, United States.
authors.forename: Angela J
authors.initials: AJ
authors.lastname: Rogers
authors.affiliation: Department of Emergency Medicine, University of California, San Francisco, United States.
authors.forename: Christopher
authors.initials: C
authors.lastname: Barton
authors.affiliation: Dascena, Inc, United States.
authors.forename: Melissa
authors.initials: M
authors.lastname: Jay
authors.affiliation: Dascena, Inc, United States.
authors.forename: Thomas
authors.initials: T
authors.lastname: Desautels
authors.affiliation: Dascena, Inc, United States.
authors.forename: Hamid
authors.initials: H
authors.lastname: Mohamadlou
authors.affiliation: Department of Bioengineering, University of California, Berkeley, United States.
authors.forename: Jasmine
authors.initials: J
authors.lastname: Jan
authors.affiliation: Dascena, Inc, United States.
authors.forename: Ritankar
authors.initials: R
authors.lastname: Das
mesh_terms: D000328:Adult; D000437:Alcoholism / Q000401:mortality*; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D011237:Predictive Value of Tests; D012984:Software; D013997:Time Factors; D014218:Triage / Q000379:methods*
keywords: Alcohol use disorder; Clinical decision support systems; Electronic health records; Medical informatics; Mortality prediction