Improving Mechanical Ventilator Clinical Decision Support Systems with a Machine Learning Classifier for Determining Ventilator Mode.

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ID: 35909
2019
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Ranked #4 of 170 articles by views in Studies in health technology and informatics

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Abstract
Clinical decision support systems (CDSS) will play increasing role in improving quality of medical care for critically ill patients. However, due to limitations in current informatics infrastructure, CDSS do not always have complete information on state of supporting physiologic monitoring devices, which can limit input data available to CDSS. This is especially true in use case of mechanical ventilation (MV), where current CDSS have no knowledge of critical ventilation settings, such as ventilation mode. To enable MV CDSS make accurate recommendations related to ventilator mode, we developed a highly performant machine learning model that is able to perform per-breath classification of five of most widely used ventilation modes in USA with average F1-score of 97.52%. We also show how our approach makes methodologic improvements over previous work and is highly robust to missing data caused by software/sensor error.
Reference Key
rehm2019improvingstudies Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Rehm, Gregory B;Kuhn, Brooks T;Nguyen, Jimmy;Anderson, Nicholas R;Chuah, Chen-Nee;Adams, Jason Y;
Journal Studies in health technology and informatics
Year 2019
DOI
10.3233/SHTI190235
URL
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