title: Understanding vasopressor intervention and weaning: risk prediction in a public heterogeneous clinical time series database.
abstract: BACKGROUND
  The widespread adoption of electronic health records allows us to ask evidence-based questions about the need for and benefits of specific clinical interventions in critical-care settings across large populations.
  
  
  OBJECTIVE
  We investigated the prediction of vasopressor administration and weaning in the intensive care unit. Vasopressors are commonly used to control hypotension, and changes in timing and dosage can have a large impact on patient outcomes.
  
  
  METHODS
  We considered a cohort of 15 695 intensive care unit patients without orders for reduced care who were alive 30 days post-discharge. A switching-state autoregressive model (SSAM) was trained to predict the multidimensional physiological time series of patients before, during, and after vasopressor administration. The latent states from the SSAM were used as predictors of vasopressor administration and weaning.
  
  
  RESULTS
  The unsupervised SSAM features were able to predict patient vasopressor administration and successful patient weaning. Features derived from the SSAM achieved areas under the receiver operating curve of 0.92, 0.88, and 0.71 for predicting ungapped vasopressor administration, gapped vasopressor administration, and vasopressor weaning, respectively. We also demonstrated many cases where our model predicted weaning well in advance of a successful wean.
  
  
  CONCLUSIONS
  Models that used SSAM features increased performance on both predictive tasks. These improvements may reflect an underlying, and ultimately predictive, latent state detectable from the physiological time series.
authors.affiliation: Department of Computer Science, Yale University, New Haven, CT, USA.
authors.forename: Mike
authors.initials: M
authors.lastname: Wu
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Marzyeh
authors.initials: M
authors.lastname: Ghassemi
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Mengling
authors.initials: M
authors.lastname: Feng
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Leo A
authors.initials: LA
authors.lastname: Celi
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Szolovits
authors.affiliation: Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA.
authors.forename: Finale
authors.initials: F
authors.lastname: Doshi-Velez
mesh_terms: D019540:Area Under Curve; D016208:Databases, Factual; D057286:Electronic Health Records; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units; D008954:Models, Biological; D018570:Risk Assessment; D014662:Vasoconstrictor Agents / Q000008:administration & dosage / Q000627:therapeutic use*
chemical_list: D014662:Vasoconstrictor Agents
keywords: autoregressive models; electronic health records; latent variable models; risk prediction