- Poster presentation
- Open Access
Semi automated adjudication of vital sign alerts in step-down units
© Fiterau et al.; 2015
- Published: 1 October 2015
- Machine Learn
- Vital Sign
- Chart Review
- Expert Clinician
- Grant Acknowledgment
Machine Learning (ML) has shown predictive utility in analyzing vital sign (VS) data collected from physiologically unstable monitored patients. Training an ML model usually requires sizable amounts of labeled ground-truth data typically obtained via laborious manual chart reviews by expert clinicians.
To reduce effort of clinicians adjudicating vital sign alerts as true alerts or artifacts. The approach can also enable real time filtering of artifacts in vital sign monitoring systems.
Noninvasive VS data including ECG-derived heart rate (HR), respiratory rate (RR), systolic and diastolic blood pressure (BP), and pulse oxygen saturation (SpO2) is monitored to issue alerts whenever VS exceed any of pre-set stability thresholds . Two experts independently annotated 40 of such alerts only using informative low-dimensional projections of data onto statistical features extracted from raw VS data, automatically selected by our ML system. Then these experts adjudicated the same alerts using the available chart time series. We summarized the results to observe consistency of adjudication. The statistical features were extracted from each raw VS stream independently during the alert window. 260 such alerts were adjudicated using the framework described in  by a committee of 4 experts.
Expert annotation study.
Correctly adjudicated using low-d projection
Required chart review to adjudicate
Chart review disagrees with low-d
Effective training of ML-based automatic alert adjudication systems can be achieved at substantial reduction of the effort required of expert clinicians. The resulting models can be used to confidently identify a significant percentage of the artifactual alerts.
NIH NINR R01NR013912; NSF1320347.
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.