INITIAL TIDAL VOLUME PREDICTION FOR ACUTE RESPIRATORY DISTRESS SYNDROME PATIENTS BASED ON A MULTIPLE LINEAR REGRESSION MODEL

Acute respiratory distress syndrome (ARDS) is a critical condition that causes alveolar injury and impairs gas exchange. Proper setting of the initial tidal volume during mechanical ventilation is crucial for patient survival and recovery. This study aims to predict the initial tidal volume for ARDS patients using a multiple linear regression model based on data from 1580 Medical Information Mart for Intensive Care IV (MIMIC-IV) and 2295 eICU Collaborative Research Database (eICU-CRD) patients receiving noninvasive respiratory support. The model incorporates 15 variables, including gender, age, pH, and blood gas indicators. Regression coefficients were estimated using the least squares method, and model performance was evaluated using metrics such as mean squared error (MSE), R-squared ([Formula: see text]), mean absolute error (MAE), and mean absolute percentage error (MAPE). The results show that the model has good stability and generalization, with the best performance achieving an MSE of 824.89 and [Formula: see text] of 0.73. These findings suggest a significant correlation between the selected features and tidal volume, providing a basis for personalized treatment and aiding clinicians in mechanical ventilation decision-making for ARDS patients.