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10.1186/s12911-025-03082-9 http://scihub22266oqcxt.onion/10.1186/s12911-025-03082-9 40611154!ä!40611154 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 247.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 280.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\40611154.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMC+Med+Inform+Decis+Mak 2025 ; 25 (1): 248 Nephropedia Template TP {{tp|p=40611154|t=2025. Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study |pdf=|usr=}}{{40611154}} gab.com Text Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study JO: BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=40611154 #FOAvip BACKGROUND: Early detection of malnutrition in critically ill patients is crucial for timely intervention and improved clinical outcomes. However, identifying individuals at risk remains challenging due to the complexity and variability of patient conditions. This study aimed to develop and externally validate machine learning models for predicting malnutrition within 24 h of intensive care unit (ICU) admission, culminating in a web-based malnutrition prediction tool for clinical decision support. METHODS: A total of 1006 critically ill adult patients (aged >/= 18 years) were included in the model development group, and 300 adult patients comprised the external validation group. The development data were partitioned into training (80%) and testing (20%) sets. Hyperparameters were optimized via 5-fold cross-validation on the training set, eliminating the need for a separate validation set while ensuring internal validation. External validation was performed on an independent group to assess generalizability. Predictors were selected using random forest recursive feature elimination; seven machine learning models-Extreme Gradient Boosting (XGBoost), random forest, decision tree, support vector machine (SVM), Gaussian naive Bayes, k-nearest neighbor (k-NN), and logistic regression-were trained and evaluated for accuracy, precision, recall, F1 score, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PR). Model interpretability was analyzed using SHapley Additive exPlanations (SHAP) to quantify feature contributions. RESULTS: In the development phase, among 1006 patients, 34.0% had moderate malnutrition and 17.9% severe malnutrition. The XGBoost model achieved superior predictive accuracy with an accuracy of 0.90 (95% CI = 0.86-0.94), precision of 0.92 (95% CI = 0.88-0.95), recall of 0.92 (95% CI = 0.89-0.95), F1 score of 0.92 (95% CI = 0.89-0.95), AUC-ROC of 0.98 (95% CI = 0.96-0.99), and AUC-PR of 0.97 (95% CI = 0.95-0.99) on the testing set. External validation confirmed robust performance with an accuracy of 0.75 (95% CI: 0.70-0.79), precision of 0.79 (95% CI: 0.75-0.83), recall of 0.75 (95% CI: 0.70-0.79), F1 score of 0.74 (95% CI: 0.69-0.78), AUC-ROC of 0.88 (95% CI: 0.86-0.91), and AUC-PR of 0.77 (95% CI: 0.73-0.80). CONCLUSIONS: Machine learning models, particularly XGBoost, demonstrated promising performance in early malnutrition prediction in ICU settings. The resultant web-based tool offers valuable resource for clinical decision support. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2200058286 ( https://www.chictr.org.cn/bin/project/edit? pid=248690 ). Registered 4th April 2022. Prospectively registered. Twit Text FOAVip Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study ????BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=40611154 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=40611154 #FOAvip English Wikipedia <ref>{{Cite pmid|40611154}}</ref> Development and external validation of machine learning models for the early prediction of malnutrition in critically ill patients: a prospective observational study #MMPMID40611154 Liu Y; Xu Y; Guo L; Chen Z; Xia X; Chen F; Tang L; Jiang H; Xie C BMC Med Inform Decis Mak 2025[Jul]; 25 (1): 248 PMID40611154show ga BACKGROUND: Early detection of malnutrition in critically ill patients is crucial for timely intervention and improved clinical outcomes. However, identifying individuals at risk remains challenging due to the complexity and variability of patient conditions. This study aimed to develop and externally validate machine learning models for predicting malnutrition within 24 h of intensive care unit (ICU) admission, culminating in a web-based malnutrition prediction tool for clinical decision support. METHODS: A total of 1006 critically ill adult patients (aged >/= 18 years) were included in the model development group, and 300 adult patients comprised the external validation group. The development data were partitioned into training (80%) and testing (20%) sets. Hyperparameters were optimized via 5-fold cross-validation on the training set, eliminating the need for a separate validation set while ensuring internal validation. External validation was performed on an independent group to assess generalizability. Predictors were selected using random forest recursive feature elimination; seven machine learning models-Extreme Gradient Boosting (XGBoost), random forest, decision tree, support vector machine (SVM), Gaussian naive Bayes, k-nearest neighbor (k-NN), and logistic regression-were trained and evaluated for accuracy, precision, recall, F1 score, Area Under the Receiver Operating Characteristic Curve (AUC-ROC), Area Under the Precision-Recall Curve (AUC-PR). Model interpretability was analyzed using SHapley Additive exPlanations (SHAP) to quantify feature contributions. RESULTS: In the development phase, among 1006 patients, 34.0% had moderate malnutrition and 17.9% severe malnutrition. The XGBoost model achieved superior predictive accuracy with an accuracy of 0.90 (95% CI = 0.86-0.94), precision of 0.92 (95% CI = 0.88-0.95), recall of 0.92 (95% CI = 0.89-0.95), F1 score of 0.92 (95% CI = 0.89-0.95), AUC-ROC of 0.98 (95% CI = 0.96-0.99), and AUC-PR of 0.97 (95% CI = 0.95-0.99) on the testing set. External validation confirmed robust performance with an accuracy of 0.75 (95% CI: 0.70-0.79), precision of 0.79 (95% CI: 0.75-0.83), recall of 0.75 (95% CI: 0.70-0.79), F1 score of 0.74 (95% CI: 0.69-0.78), AUC-ROC of 0.88 (95% CI: 0.86-0.91), and AUC-PR of 0.77 (95% CI: 0.73-0.80). CONCLUSIONS: Machine learning models, particularly XGBoost, demonstrated promising performance in early malnutrition prediction in ICU settings. The resultant web-based tool offers valuable resource for clinical decision support. TRIAL REGISTRATION: Chinese Clinical Trial Registry ChiCTR2200058286 ( https://www.chictr.org.cn/bin/project/edit? pid=248690 ). Registered 4th April 2022. Prospectively registered. |*Critical Illness[MESH] |*Machine Learning/standards[MESH] |*Malnutrition/diagnosis[MESH] |Adult[MESH] |Aged[MESH] |Female[MESH] |Humans[MESH] |Intensive Care Units[MESH] |Male[MESH] |Middle Aged[MESH]Development and external validation of machine learning models for the(epmc).pdf DeepDyve Pubget Overpricing