Abstract
The acute respiratory distress syndrome (ARDS) is a frequent type of respiratory failure observed in intensive care units. The Berlin classification identifies three severity levels of ARDS (mild, moderate, and severe), but this classification is under controversy in the medical community because it reflects neither the care requirements nor the expected clinical outcome of the patients. Here, the database MIMIC III (MetaVision) was used to investigate the similarity of patients within each one of the Berlin severity groups. We also ranked the relevance of common ARDS descriptive features and proposed four alternative classifiers to improve Berlin’s classification in the prediction of the duration of mechanical ventilation and mortality. One of these classifiers proved to be significantly better than current proposals and, therefore, it can be considered as a robust model to potentially improve health care processes and quality in the management of ARDS patients in Intensive Care Units (ICUs).
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Notes
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During the fold-cross validation, the statistical/machine learning method to produce the model may consider not to use all the features, but only some of them.
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The authors acknowledge financial support from the RETOS P-BreasTreat project (DPI2016-77415-R) of the Spanish Ministerio de Economia y Competitividad.
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Sayed, M., Riaño, D. (2019). Modelling ICU Patients to Improve Care Requirements and Outcome Prediction of Acute Respiratory Distress Syndrome: A Supervised Learning Approach. In: Marcos, M., et al. Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems. KR4HC TEAAM 2019 2019. Lecture Notes in Computer Science(), vol 11979. Springer, Cham. https://doi.org/10.1007/978-3-030-37446-4_4
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