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

Figure 1. Performance of ML models for prediction of clinical outcomes. ROC curves comparing the performance of 5 ML models across 4 prediction tasks: (A) AMS diagnosed by the old LLS, (B) AMS diagnosed by the new LLS, (C) AMS score using the old LLS and (D) AMS score using the new LLS. AdaBoost, adaptive boosting; AMS, acute mountain sickness; AUC, area under the ROC curve; LightGBM, light gradient boosting machine; LLS, Lake Louise Score; ML, machine learning; RF, random forest; ROC, receiver operating characteristic; SVM, support vector machine; XGBoost, extreme gradient boosting.

Figure 2. Global interpretability of the AMS-FTE model using SHAP. SHAP summary plots for the AMS-FTE model across 4 prediction tasks: (A) AMS diagnosed by the old LLS, (B) AMS diagnosed by the new LLS, (C) AMS score using the old LLS, and (D) AMS score using the new LLS. AMS, Acute mountain sickness; AMS-FTE, Acute Mountain Sickness Four-Task Ensemble; LLS, Lake Louise Score; SHAP, SHapley Additive exPlanations.

Figure 3. Mean absolute SHAp value of the AMS-FTE model. Mean absolute SHAP value for the AMS-FTE model across 4 prediction tasks: (A) AMS diagnosed by the old LLS, (B) AMS diagnosed by the new LLS, (C) AMS score using the old LLS, and (D) AMS score using the new LLS. AMS, Acute mountain sickness; AMS-FTE, Acute Mountain Sickness Four-Task Ensemble; LLS, Lake Louise Score; SHAP, SHapley Additive exPlanations.

Figure 4. AUCs for the AMS-FTE model across five selected features in each prediction task. ROC curves of 5 selected features in the AMS-FTE model across 4 prediction tasks: (A) AMS diagnosed by the old LLS, (B) AMS diagnosed by the new LLS, (C) AMS score using the old LLS and (D) AMS score using the new LLS. AMS, acute mountain sickness; AMS-FTE, Acute Mountain Sickness Four-Task Ensemble; AUC, rea under the ROC curve; LLS, Lake Louise Score; ROC, eceiver operating characteristic.

Figure 5. DCA for the AMS-FTE model across five selected features in each prediction task. DCA of 5 selected features in the AMS-FTE model across 4 prediction tasks: (A) AMS diagnosed by the old LLS, (B) AMS diagnosed by the new LLS, (C) AMS score using the old LLS and (D) AMS score using the new LLS. AMS, Acute mountain sickness. AMS-FTE, Acute mountain sickness four-task ensemble. DCA, Decision curve analysis. LLS, Lake Louise score.

Figure 6. Overview of the SHAP-based AMS prediction support system. User interfaces of the AMS prediction support system designed to support clinical implementation. This system employs a AMS-FTE model, integrating 5 variables including post-exercise HR, post-exercise SpO2, AT-RCP time, peak VO2, and ΔVO2 AT-RCP to predict the incidence and severity of AMS. AMS, acute mountain sickness; AMS-FTE, acute mountain sickness four-task ensemble; AT, anaerobic threshold; HR, heart rate; RCP, respiratory compensation point; SHAP, SHapley additive explanations; SpO2, percutaneous oxygen saturation.