title: Is the Sequence of SuperAlarm Triggers More Predictive Than Sequence of the Currently Utilized Patient Monitor Alarms?
abstract: Our previous studies have shown that "code blue" events can be predicted by SuperAlarm patterns that are multivariate combinations of monitor alarms and laboratory test results cooccurring frequently preceding the events but rarely among control patients. Deploying these patterns to the monitor data streams can generate SuperAlarm sequences. The objective of this study is to test the hypothesis that SuperAlarm sequences may contain more predictive sequential patterns than monitor alarms sequences.
  
  
  
  Monitor alarms and laboratory test results are extracted from a total of 254 adult coded and 2213 control patients. The training dataset is composed of subsequences that are sampled from complete sequences and then further represented as fixed-dimensional vectors by the term frequency inverse document frequency method. The information gain technique and weighted support vector machine are adopted to select the most relevant features and train a classifier to differentiate sequences between coded patients and control patients. Performances are assessed based on an independent dataset using three metrics: sensitivity of lead time (SenL@T), alarm frequency reduction rate (AFRR), and work-up to detection ratio (WDR).
  
  
  
  The performance of 12-h-long sequences of SuperAlarm can yield a SenL@2 of 93.33%, an AFRR of 87.28%, and a WDR of 3.01. At an AFRR = 87.28%, SenL@2 for raw alarm sequences and discretized alarm sequences are 73.33% and 70.19%, respectively. At a WDR = 3.01, SenL@2 are 49.88% and 43.33%.
  
  
  
  The results demonstrate that SuperAlarm sequences indeed outperform monitor alarm sequences and suggest that one can focus on sequential patterns from SuperAlarm sequences to develop more precise patient monitoring solutions.
authors.forename: Yong
authors.initials: Y
authors.lastname: Bai
authors.forename: Duc
authors.initials: D
authors.lastname: Do
authors.forename: Quan
authors.initials: Q
authors.lastname: Ding
authors.forename: Jorge Arroyo
authors.initials: JA
authors.lastname: Palacios
authors.forename: Yalda
authors.initials: Y
authors.lastname: Shahriari
authors.forename: Michele M
authors.initials: MM
authors.lastname: Pelter
authors.forename: Noel
authors.initials: N
authors.lastname: Boyle
authors.forename: Richard
authors.initials: R
authors.lastname: Fidler
authors.forename: Xiao
authors.initials: X
authors.lastname: Hu
mesh_terms: D000465:Algorithms; D056902:Clinical Alarms / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods; D006801:Humans; D015233:Models, Statistical; D008991:Monitoring, Physiologic / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity