An Analytic Platform for the Rapid and Reproducible Annotation of Ventilator Waveform Data

Gregory B. Rehm, Brooks T. Kuhn, Monica K. Lieng, Irene Cortes-Puch, Jimmy Nguyen, Edward C. Guo, Jean-Pierre Delplanque, Nicholas R. Anderson, Jason Y. Adams
2019 · doi:10.1101/568386
preprint OA: closed CC-BY-4.0
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Abstract

Algorithmic classifiers are crucial components of clinical decision support (CDS) systems needed to advance healthcare delivery. Robust CDS systems must be derived and validated via creation of multi-reviewer adjudicated gold standard datasets. Manual annotation of physiologic data such as mechanical ventilator waveform data (VWD) can be time-consuming, and lacks methodological consistency in dataset development. To address these issues, we have created a system for annotating and adjudicating VWD called the Annotation PipeLine (APL) to optimize VWD annotation by expert reviewers. APL combines visual assessment of waveform characteristics with metadata display, enabling inclusion of quantitative thresholds into annotation decisions by reviewers. APL also includes specific features for resolving multi-reviewer disagreements and generating gold standard data sets. APL’s unique combination of methods and open source framework may accelerate the creation of CDS algorithms for ventilator management, and may serve as a model for future research into physiologic waveform annotation systems.

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License: CC-BY-4.0