Baseline indices of cardiorespiratory fitness for predicting acute mountain sickness: an interpretable four-task ensemble model.

Background: Acute mountain sickness (AMS) poses formidable challenges for individuals venturing into high-altitude (HA) environments. Early and precise prediction of AMS is crucial for the timely identification of high-risk individuals and the development of effective treatment strategies. We sought to develop and validate an interpretable Acute Mountain Sickness Four-Task Ensemble (AMS-FTE) model for predicting AMS occurrence and severity according to the old and new LLS definitions. Methods: This study enrolled 330 healthy subjects who underwent rapid exposure to HA. Questionnaire surveys utilizing the Lake Louise Score for AMS were administered upon arrival. Five machine learning (ML) algorithms were evaluated independently for each prediction task, after which RF, AdaBoost, and LightGBM were combined to construct the AMS-FTE framework. To enhance interpretability, the SHapley Additive exPlanation method illuminated feature importance. The area under the receiver operating characteristic curve and decision curve analysis were evaluated to calculate predictive power and assess the model's clinical applicability. Results: Following algorithm comparison, AMS-FTE model was constructed to provide unified predictions for the four AMS-related tasks. Based on feature importance rankings, interpretable final model was developed for each task using the top 5 contributing features, which were associated with baseline cardiopulmonary fitness (post-exercise heart rate, post-exercise SpO Conclusion: The predictive value of baseline cardiorespiratory fitness parameters proved valuable in distinguishing AMS and non-AMS subjects, as revealed through an interpretable ML model and a support system. This will enable earlier diagnosis for AMS, and more targeted interventions for acute altitude illness.