2026-10-11T12:36:51Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33719143 2026-09-14T05:42:15Z category_393 portal_316 item_type_3 month_year_09_2026

Data Sheet 1_Prediction of moderate-to-severe postoperative thirst in general anesthesia patients based on automated machine learning and clinical nursing translation.docx Yi Zhan (1397086) Siting Yang (15265760) Haoran Wang (272166) Foetal Development and Medicine automated machine learning enhanced recovery after surgery (ERAS) explainable artificial intelligence general anesthesia postoperative thirst risk prediction Objective<p>To develop an Automated Machine Learning (AutoML)-based model for screening Moderate-to-Severe Postoperative Thirst (MSPOT) risk at surgery-to-PACU handover and a prototype clinical decision support system.</p>Methods<p>This retrospective single-center cohort included 928 patients undergoing general anesthesia (training set: n = 650; held-out internal test set: n = 278). An Improved Wave Optics Optimizer (IWOO) framework integrated feature selection and hyperparameter optimization using demographic, preoperative, and intraoperative variables. Prediction was performed at anesthesia-to-PACU handover, after final intraoperative data became available and before routine postoperative NRS thirst assessment. Performance was evaluated using ROC-AUC, PR-AUC, calibration analysis, Brier score, and decision curve analysis (DCA). SHAP was used to summarize feature contributions.</p>Results<p>In the held-out internal test set, the AutoML model achieved a ROC-AUC of 0.9053, PR-AUC of 0.9080, and Brier score of 0.129, outperforming conventional models. DCA showed greater net benefit across thresholds of 1%–95%. In same-center temporal validation, discrimination remained acceptable (ROC-AUC: 0.8807; PR-AUC: 0.8778), but calibration deteriorated (Brier score: 0.1924; intercept: −1.2250; slope: 0.5811), indicating average risk overestimation and overly extreme probabilities. Six predictors were identified: esmolol use, intraoperative blood loss, operation time, ASA classification, ERAS, and intraoperative fluid volume.</p>Conclusions<p>The AutoML model showed strong discrimination and transparent feature attribution. The prototype illustrates its potential use at PACU handover to prioritize early assessment and comfort-oriented thirst management. However, temporal miscalibration indicates that individualized probabilities are not ready for clinical decision-making without recalibration and subsequent prospective validation.</p> 2026-09-14T05:42:15Z Dataset Dataset 10.3389/fmed.2026.1846230.s002 https://figshare.com/articles/dataset/Data_Sheet_1_Prediction_of_moderate-to-severe_postoperative_thirst_in_general_anesthesia_patients_based_on_automated_machine_learning_and_clinical_nursing_translation_docx/33719143 CC BY 4.0