title: Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data.
abstract: Huge hospital information system databases can be mined for knowledge discovery and decision support, but artifact in stored non-invasive vital sign (VS) high-frequency data streams limits its use. We used machine-learning (ML) algorithms trained on expert-labeled VS data streams to automatically classify VS alerts as real or artifact, thereby "cleaning" such data for future modeling. 634 admissions to a step-down unit had recorded continuous noninvasive VS monitoring data [heart rate (HR), respiratory rate (RR), peripheral arterial oxygen saturation (SpO2) at 1/20 Hz, and noninvasive oscillometric blood pressure (BP)]. Time data were across stability thresholds defined VS event epochs. Data were divided Block 1 as the ML training/cross-validation set and Block 2 the test set. Expert clinicians annotated Block 1 events as perceived real or artifact. After feature extraction, ML algorithms were trained to create and validate models automatically classifying events as real or artifact. The models were then tested on Block 2. Block 1 yielded 812 VS events, with 214 (26 %) judged by experts as artifact (RR 43 %, SpO240 %, BP 15 %, HR 2 %). ML algorithms applied to the Block 1 training/cross-validation set (tenfold cross-validation) gave area under the curve (AUC) scores of 0.97 RR, 0.91 BP and 0.76 SpO2. Performance when applied to Block 2 test data was AUC 0.94 RR, 0.84 BP and 0.72 SpO2. ML-defined algorithms applied to archived multi-signal continuous VS monitoring data allowed accurate automated classification of VS alerts as real or artifact, and could support data mining for future model building.
authors.affiliation: Department of Acute and Tertiary Care, University of Pittsburgh Schools of Nursing, 336 Victoria Hall; 3500 Victoria St., Pittsburgh, PA, 15261, USA. mhra@pitt.edu.
authors.forename: Marilyn
authors.initials: M
authors.lastname: Hravnak
authors.affiliation: Carnegie Mellon University Robotics Institute (Auton Lab), Pittsburgh, PA, USA.
authors.forename: Lujie
authors.initials: L
authors.lastname: Chen
authors.affiliation: Carnegie Mellon University Robotics Institute (Auton Lab), Pittsburgh, PA, USA.
authors.forename: Artur
authors.initials: A
authors.lastname: Dubrawski
authors.affiliation: Department of Acute and Tertiary Care, University of Pittsburgh Schools of Nursing, 336 Victoria Hall; 3500 Victoria St., Pittsburgh, PA, 15261, USA.
authors.forename: Eliezer
authors.initials: E
authors.lastname: Bose
authors.affiliation: Department of Critical Care Medicine, University of Pittsburgh Schools of Medicine, Pittsburgh, PA, USA.
authors.forename: Gilles
authors.initials: G
authors.lastname: Clermont
authors.affiliation: Department of Critical Care Medicine, University of Pittsburgh Schools of Medicine, Pittsburgh, PA, USA.
authors.forename: Michael R
authors.initials: MR
authors.lastname: Pinsky
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D019540:Area Under Curve; D016477:Artifacts; D001794:Blood Pressure; D056902:Clinical Alarms; D003627:Data Interpretation, Statistical; D057225:Data Mining / Q000379:methods*; D020000:Decision Support Systems, Clinical; D005260:Female; D006339:Heart Rate; D006751:Hospital Information Systems; D006801:Humans; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D008991:Monitoring, Physiologic; D009991:Oscillometry; D012306:Risk; D055986:Vital Signs
keywords: Archived data; Artifact; Big-data; Cardiorespiratory instability; Machine learning; Physiologic monitoring; Vital signs