A machine learning-based severity stratification tool for high altitude pulmonary edema.

Abstract

This study aimed to identify key predictors for the severity of High Altitude Pulmonary Edema (HAPE) to assist clinicians in promptly recognizing severely affected patients in the emergency department, thereby reducing associated mortality rates. Multinomail logistic regression, random forest, and decision tree methods were utilized to determine important predictor variables and evaluate model performance. A total of 508 patients diagnosed with HAPE were included in the study, with 53 variables analyzed. Lung rales, sputum sputuming, heart rate, and oxygen saturation were identified as the most relevant predictors for the LASSO model. Subsequently, Multinomail logistic regression, decision tree, and random forest models were trained and evaluated using these factors on a test set. The random forest model showed the highest performance, with an accuracy of 77.94%, precision of 70.27%, recall of 68.22%, and F1 score of 68.96%, outperforming the other models. Further analysis revealed significant differences in predictive capabilities among the models for HAPE patients at varying severity levels. The random forest model demonstrated high predictive accuracy across all severity levels of HAPE, particularly excelling in identifying severely ill patients with an impressive AUC of 0.86. The study assessed the reliability and effectiveness of the HAPE severity scoring model by validating Multinomail logistic regression and random forest models. This study introduces a valuable screening tool for categorizing the severity of HAPE, aiding healthcare providers in recognizing individuals with severe HAPE, enabling prompt treatment and the formulation of suitable therapeutic approaches.