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10.1186/s12876-025-04373-1 http://scihub22266oqcxt.onion/10.1186/s12876-025-04373-1 C12551288!12551288 !41131471     free     free     free Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 211.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 245.2 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\41131471 .jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117       BMC+Gastroenterol 2025 ; 25 (1 ): 754 Nephropedia Template TP {{tp|p=41131471 |t=2025. Enhancing explainability of random survival forests in predicting stent patency risk for malignant colonic obstruction |pdf=|usr=}}{{41131471 }} gab.com Text Enhancing explainability of random survival forests in predicting stent patency risk for malignant colonic obstruction JO: BMC Gastroenterol http://www.kidney.de/pget.php?&tw=1&pmid=41131471 #FOAvip BACKGROUND: This study aims to enhance the explainability and predictive accuracy of the Random Survival Forest (RSF) algorithm in predicting stent patency risk for patients with malignant colonic obstruction. METHODS: The RSF algorithm was applied to clinical prognostic data of 109 patients with malignant colonic obstruction who underwent self-expandable metallic stent (SEMS) procedures between September 2014 and October 2023. We combined the RSF variable importance and Least Absolute Shrinkage and Selection Operator (Lasso) regression to identify the final predictive variables. And the performance of the RSF model was compared with the Cox Proportional Hazards (CPH) model using both global and local explanation methods. RESULTS: The RSF model demonstrated superior predictive performance, with higher time-dependent AUCs and lower Brier scores compared to the CPH model across various time points. Significant predictors of stent patency identified by the RSF and Lasso models included Diabetes, CA199, Pre-Chemotherapy and Length of obstruction. The partial dependence plots highlighted CA199 and Length of obstruction as critical variables, with SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) analyses further revealing the dynamic, time-varying impact of these variables on individual patient outcomes. CONCLUSIONS: The RSF algorithm, supplemented with comprehensive feature importance analyses and advanced interpretability techniques, offers a robust and reliable framework for predicting stent patency risk in patients with malignant colonic obstruction. Twit Text FOAVip Enhancing explainability of random survival forests in predicting stent patency risk for malignant colonic obstruction ????BMC Gastroenterol http://www.kidney.de/pget.php?&tw=1&pmid=41131471 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41131471 #FOAvip English Wikipedia <ref>{{Cite pmid|41131471 }}</ref> Enhancing explainability of random survival forests in predicting stent patency risk for malignant colonic obstruction #MMPMID41131471 Wan Y ; Zou MS ; Li D ; Li Y ; Cao XZ ; Zhang B ; Wu HH BMC Gastroenterol 2025[Oct]; 25 (1 ): 754 PMID41131471 show ga BACKGROUND: This study aims to enhance the explainability and predictive accuracy of the Random Survival Forest (RSF) algorithm in predicting stent patency risk for patients with malignant colonic obstruction. METHODS: The RSF algorithm was applied to clinical prognostic data of 109 patients with malignant colonic obstruction who underwent self-expandable metallic stent (SEMS) procedures between September 2014 and October 2023. We combined the RSF variable importance and Least Absolute Shrinkage and Selection Operator (Lasso) regression to identify the final predictive variables. And the performance of the RSF model was compared with the Cox Proportional Hazards (CPH) model using both global and local explanation methods. RESULTS: The RSF model demonstrated superior predictive performance, with higher time-dependent AUCs and lower Brier scores compared to the CPH model across various time points. Significant predictors of stent patency identified by the RSF and Lasso models included Diabetes, CA199, Pre-Chemotherapy and Length of obstruction. The partial dependence plots highlighted CA199 and Length of obstruction as critical variables, with SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) analyses further revealing the dynamic, time-varying impact of these variables on individual patient outcomes. CONCLUSIONS: The RSF algorithm, supplemented with comprehensive feature importance analyses and advanced interpretability techniques, offers a robust and reliable framework for predicting stent patency risk in patients with malignant colonic obstruction. |*Algorithms [MESH] |*Colonic Neoplasms/complications [MESH] |*Intestinal Obstruction/etiology/surgery [MESH] |*Self Expandable Metallic Stents [MESH] |Aged [MESH] |Aged, 80 and over [MESH] |Female [MESH] |Humans [MESH] |Male [MESH] |Middle Aged [MESH] |Prognosis [MESH] |Proportional Hazards Models [MESH] |Risk Assessment/methods [MESH]Enhancing explainability of random survival forests in predicting sten(epmc).pdf DeepDyve Pubget Overpricing