Modelling ICU Patients to Improve Care Requirements and Outcome Prediction of Acute Respiratory Distress Syndrome: A Supervised Learning Approach

摘要

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 (Meta Vision) 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).