Dataset Information

Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

ABSTRACT: 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.Observational cohort study.Twenty-four-bed trauma step-down unit.Two thousand one hundred fifty-three patients.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.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.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).

SUBMITTER: Chen L

PROVIDER: S-EPMC4911247 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

Chen Lujie L Dubrawski Artur A Wang Donghan D Fiterau Madalina M Guillame-Bert Mathieu M Bose Eliezer E Kaynar Ata M AM Wallace David J DJ Guttendorf Jane J Clermont Gilles G Pinsky Michael R MR Hravnak Marilyn M

Critical care medicine 20160701 7

<h4>Objective</h4>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.<h4>Design</h4>Observational cohort study.<h4>Setting</h4>Twenty-four-bed trauma step-down unit.<h4>Patients</h4>Two thousand one hundred fifty-three patients.<h4>Intervention</h4>Noninvasive vital sign monitoring data (heart rate, respiratory rate, peripheral oximetry) recorded on all admissions at 1/20 ...[more]

PMID: 26992068

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Project description:BackgroundPrehospital vital sign documentation in paediatric patients is incomplete, especially in patients ≤ 2 years. The aim of the study was to increase vital sign registration in paediatric patients through specific educational initiatives.MethodsProspective quasi-experimental study with interrupted time-series design in the North Denmark and South Denmark regions. The study consecutively included all children aged < 18 years attended by the emergency medical service (EMS) from 1 July 2019 to 31 December 2021. Specific educational initiatives were conducted only in the North Denmark EMS and included video learning and classroom training based on the European Paediatric Advanced Life Support principles. The primary outcome was the proportion of patients who had their respiratory rate, peripheral capillary oxygen saturation, heart rate and level of consciousness recorded at least twice. We used a binomial regression model stratified by age groups to compare proportions of the primary outcome in the pre- and post-intervention periods in each region.ResultsIn North Denmark, 7551 patients were included, while 15,585 patients from South Denmark were used as a reference. Virtually all of the North Denmark EMS providers completed the video learning (98.7%). The total study population involved patients aged ≤ 2 months (5.5%), 3-11 months (7.4%), 1-2 years (18.8%), 3-7 years (16.2%) and ≥ 8 years (52.1%). In the intervention region, the primary outcome increased from the pre- to the post-intervention period from 35.3% to 40.5% [95% CI for difference 3.0;7.4]. There were large variations in between age groups with increases from 18.8% to 27.4% [95% CI for difference 5.3;12.0] among patients aged ≤ 2 years, from 33.5% to 43.7% [95% CI for difference 4.9;15.5] among patients aged 3-7 years and an insignificant increase among patients aged ≥ 8 years (from 46.4% to 47.9% [95% CI for difference - 1.7;4.7]). In the region without the specific educational interventions, proportions were steady for all age groups throughout the entire study period.ConclusionsMandatory educational initiatives for EMS providers were associated with an increase in the extent of vital sign registration in paediatric patients ≤ 7 years. Incomplete vital registration was associated with, but not limited to non-urgent cases.

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Dataset Information

Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

Publications

Using Supervised Machine Learning to Classify Real Alerts and Artifact in Online Multisignal Vital Sign Monitoring Data.

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