title: Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.
abstract: OBJECTIVE
  The use of machine-learning algorithms to classify alerts as real or artifacts in online noninvasive vital sign data streams to reduce alarm fatigue and missed true instability.
  
  
  METHODS
  Observational cohort study.
  
  
  METHODS
  Twenty-four-bed trauma step-down unit.
  
  
  METHODS
  Two thousand one hundred fifty-three patients.
  
  
  METHODS
  Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 Hz, and noninvasive blood pressure less frequently, and partitioned data into training/validation (294 admissions; 22,980 monitoring hours) and test sets (2,057 admissions; 156,177 monitoring hours). Alerts were vital sign deviations beyond stability thresholds. A four-member expert committee annotated a subset of alerts (576 in training/validation set, 397 in test set) as real or artifact selected by active learning, upon which we trained machine-learning algorithms. The best model was evaluated on test set alerts to enact online alert classification over time.
  
  
  RESULTS
  The Random Forest model discriminated between real and artifact as the alerts evolved online in the test set with area under the curve performance of 0.79 (95% CI, 0.67-0.93) for peripheral oximetry at the instant the vital sign first crossed threshold and increased to 0.87 (95% CI, 0.71-0.95) at 3 minutes into the alerting period. Blood pressure area under the curve started at 0.77 (95% CI, 0.64-0.95) and increased to 0.87 (95% CI, 0.71-0.98), whereas respiratory rate area under the curve started at 0.85 (95% CI, 0.77-0.95) and increased to 0.97 (95% CI, 0.94-1.00). Heart rate alerts were too few for model development.
  
  
  CONCLUSIONS
  Machine-learning models can discern clinically relevant peripheral oximetry, blood pressure, and respiratory rate alerts from artifacts in an online monitoring dataset (area under the curve > 0.87).
authors.affiliation: 1Auton Lab, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA. 2Department of Acute and Tertiary Care, University of Pittsburgh Schools of Nursing, Pittsburgh, PA. 3Department of Critical Care Medicine, University of Pittsburgh Schools of Medicine, Pittsburgh, PA.
authors.forename: Lujie
authors.initials: L
authors.lastname: Chen
authors.forename: Artur
authors.initials: A
authors.lastname: Dubrawski
authors.forename: Donghan
authors.initials: D
authors.lastname: Wang
authors.forename: Madalina
authors.initials: M
authors.lastname: Fiterau
authors.forename: Mathieu
authors.initials: M
authors.lastname: Guillame-Bert
authors.forename: Eliezer
authors.initials: E
authors.lastname: Bose
authors.forename: Ata M
authors.initials: AM
authors.lastname: Kaynar
authors.forename: David J
authors.initials: DJ
authors.lastname: Wallace
authors.forename: Jane
authors.initials: J
authors.lastname: Guttendorf
authors.forename: Gilles
authors.initials: G
authors.lastname: Clermont
authors.forename: Michael R
authors.initials: MR
authors.lastname: Pinsky
authors.forename: Marilyn
authors.initials: M
authors.lastname: Hravnak
mesh_terms: D016477:Artifacts; D001795:Blood Pressure Determination; D056902:Clinical Alarms / Q000145:classification*; D015331:Cohort Studies; D006339:Heart Rate; D006801:Humans; D008991:Monitoring, Physiologic / Q000379:methods*; D010092:Oximetry; D056152:Respiratory Rate; D000069553:Supervised Machine Learning; D055986:Vital Signs