Real alerts and artifact classification in archived multi-signal vital sign monitoring data: implications for mining big data

Hravnak, Marilyn; Chen, Lujie; Dubrawski, Artur; Bose, Eliezer; Clermont, Gilles; Pinsky, Michael R.

doi:10.1007/s10877-015-9788-2

Cited by 31 publications

(23 citation statements)

References 39 publications

(38 reference statements)

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“…Artifacts pose a significant challenge to accurate classification of signal data. Hravnak and colleagues developed an approach to distin-guish real alerts from artifacts in archived multi-signal vital sign monitoring data [7]. Accurate "data cleaning" steps are critical for mining "big data" sets of high-volume, real-time physiologic data.…”

Section: Discussionmentioning

confidence: 99%

Sensor, Signal, and Imaging Informatics

Hsu¹,

Park²,

Kahn³

2017

Yearb Med Inform

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SummaryObjective: To summarize significant contributions to sensor, signal, and imaging informatics published in 2016. Methods: We conducted an extensive search using PubMed® and Web of Science® to identify the scientific contributions published in 2016 that addressed sensors, signals, and imaging in medical informatics. The three section editors selected 15 candidate best papers by consensus. Each candidate article was reviewed by the section editors and at least two other external reviewers. The final selection of the six best papers was conducted by the editorial board of the Yearbook. Results: The selected papers of 2016 demonstrate the important scientific advances in management and analysis of sensor, signal, and imaging information. Conclusion: The growing volume of signal and imaging data provides exciting new challenges and opportunities for research in medical informatics. Evolving technologies provide faster and more effective approaches for pattern recognition and diagnostic evaluation. The papers selected here offer a small glimpse of the high-quality scientific work published in 2016 in the domain of sensor, signal, and imaging informatics.

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Section: Discussionmentioning

confidence: 99%

Sensor, Signal, and Imaging Informatics

Hsu¹,

Park²,

Kahn³

2017

Yearb Med Inform

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“…[7]) and persisting for at least 80% of the time over a 3-minute window. Using the method described in our previous work (8,9), we identified a subset of clinically important CRI events to be used as the predictive endpoint in this study.…”

Section: Methodsmentioning

confidence: 99%

Dynamic and Personalized Risk Forecast in Step-Down Units. Implications for Monitoring Paradigms

et al. 2017

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Rationale: Cardiorespiratory insufficiency (CRI) is a term applied to the manifestations of loss of normal cardiorespiratory reserve and portends a bad outcome. CRI occurs commonly in hospitalized patients, but its risk escalation patterns are unexplored.Objectives: To describe the dynamic and personal character of CRI risk evolution observed through continuous vital sign monitoring of individual step-down unit patients.Methods: Using a machine learning model, we estimated risk trends for CRI (defined as exceedance of vital sign stability thresholds) for each of 1,971 admissions (1,880 unique patients) to a 24-bed adult surgical trauma step-down unit at an urban teaching hospital in Pittsburgh, Pennsylvania using continuously recorded vital signs from standard bedside monitors. We compared and contrasted risk trends during initial 4-hour periods after step-down unit admission, and again during the 4 hours immediately before the CRI event, between cases (ever had a CRI) and control subjects (never had a CRI). We further explored heterogeneity of risk escalation patterns during the 4 hours before CRI among cases, comparing personalized to nonpersonalized risk.Measurements and Main Results: Estimated risk was significantly higher for cases (918) than control subjects (1,053; P < 0.001) during the initial 4-hour stable periods. Among cases, the aggregated nonpersonalized risk trend increased 2 hours before the CRI, whereas the personalized risk trend became significantly different from control subjects 90 minutes ahead. We further discovered several unique phenotypes of risk escalation patterns among cases for nonpersonalized (14.6% persistently high risk, 18.6% early onset, 66.8% late onset) and personalized risk (7.7% persistently high risk, 8.9% early onset, 83.4% late onset). Conclusions:Insights from this proof-of-concept analysis may guide design of dynamic and personalized monitoring systems that predict CRI, taking into account the triage and real-time monitoring utility of vital signs. These monitoring systems may prove useful in the dynamic allocation of technological and clinical personnel resources in acute care hospitals.

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“…A recent review of the history of clinical decision support states the dramatic improvement in this sector due to the advent of cognitive aids to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness [5]. An example of AI application in the hospital setting is the use of machine-learning algorithms to automatically classify vital sign alerts as real or artifact, in order to clean such data for future modeling, by training expert-labeled vital sign data streams [6]. Researchers have also studied the creation and implementation of data-driven vital sign parameters to reduce alarm fatigue in a pediatric acute care unit [7].…”

Section: Decision Support and Hospital Monitoringmentioning

confidence: 99%