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10.1186/s12911-025-03245-8 http://scihub22266oqcxt.onion/10.1186/s12911-025-03245-8 C12551130!12551130 !41131537     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 Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\41131537 .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 ): 392 Nephropedia Template TP {{tp|p=41131537 |t=2025. Prediction of postoperative haemorrhage after cerebral tumour surgery using machine learning algorithms |pdf=|usr=}}{{41131537 }} gab.com Text Prediction of postoperative haemorrhage after cerebral tumour surgery using machine learning algorithms JO: BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=41131537 #FOAvip BACKGROUND: Traditional diagnostic methods used by neurosurgeons are limited in their ability to address complex interactions. These limitations have necessitated the use of advanced artificial intelligence approaches capable of analyzing multidimensional data with greater precision in neurosurgical clinics. Postoperative intracranial hemorrhage is a critical complication following cerebral tumor surgery, often associated with increased morbidity and mortality. This study aimed to predict the risk of postoperative intracerebral hemorrhage in patients undergoing intracranial tumor surgery by employing machine learning (ML) algorithms for risk stratification and identifying key contributing factors. METHODS: This retrospective study included 118 patients monitored in the neurosurgical intensive care unit between January 2024 and January 2025. The primary outcome was postoperative hemorrhage, defined as a radiologically confirmed hematoma???5 ml on brain CT within 24 h. Using a predefined set of clinical and biochemical parameters analyzed with SPSS and R, multiple ML algorithms were developed. To address class imbalance in the training data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Models were evaluated using metrics including Area Under the Curve (AUC), accuracy, and F1-score, with further assessment via calibration plots and Decision Curve Analysis (DCA). RESULTS: The LightGBM model demonstrated a robust and balanced predictive performance, achieving a test AUC of 0.7451, an accuracy of 76.9%, a sensitivity of 77.8%, and an F1-score of 0.700. Platelet count (PLT), serum chloride (Cl), and the change in C-reactive protein from pre- to postoperative state (delta-CRP) emerged as the most influential predictors of hemorrhage. Model explainability was enhanced using SHAP and LIME analyses, and the model showed good calibration with potential clinical net benefit. CONCLUSION: Our study suggests that ML algorithms, particularly LightGBM, show promise for predicting postoperative hemorrhage following brain tumor surgery. Biomarkers such as platelet count, chloride, and delta-CRP offer clinically meaningful insights for early risk detection. Once externally validated, the integration of such models into clinical decision support systems could potentially improve postoperative monitoring and patient outcomes. Twit Text FOAVip Prediction of postoperative haemorrhage after cerebral tumour surgery using machine learning algorithms ????BMC Med Inform Decis Mak http://www.kidney.de/pget.php?&tw=1&pmid=41131537 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=41131537 #FOAvip English Wikipedia <ref>{{Cite pmid|41131537 }}</ref> Prediction of postoperative haemorrhage after cerebral tumour surgery using machine learning algorithms #MMPMID41131537 Göktürk Y ; Ba?arslan SK ; Göktürk ? ; Kocaman H ; Y?ld?r?m H BMC Med Inform Decis Mak 2025[Oct]; 25 (1 ): 392 PMID41131537 show ga BACKGROUND: Traditional diagnostic methods used by neurosurgeons are limited in their ability to address complex interactions. These limitations have necessitated the use of advanced artificial intelligence approaches capable of analyzing multidimensional data with greater precision in neurosurgical clinics. Postoperative intracranial hemorrhage is a critical complication following cerebral tumor surgery, often associated with increased morbidity and mortality. This study aimed to predict the risk of postoperative intracerebral hemorrhage in patients undergoing intracranial tumor surgery by employing machine learning (ML) algorithms for risk stratification and identifying key contributing factors. METHODS: This retrospective study included 118 patients monitored in the neurosurgical intensive care unit between January 2024 and January 2025. The primary outcome was postoperative hemorrhage, defined as a radiologically confirmed hematoma???5 ml on brain CT within 24 h. Using a predefined set of clinical and biochemical parameters analyzed with SPSS and R, multiple ML algorithms were developed. To address class imbalance in the training data, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Models were evaluated using metrics including Area Under the Curve (AUC), accuracy, and F1-score, with further assessment via calibration plots and Decision Curve Analysis (DCA). RESULTS: The LightGBM model demonstrated a robust and balanced predictive performance, achieving a test AUC of 0.7451, an accuracy of 76.9%, a sensitivity of 77.8%, and an F1-score of 0.700. Platelet count (PLT), serum chloride (Cl), and the change in C-reactive protein from pre- to postoperative state (delta-CRP) emerged as the most influential predictors of hemorrhage. Model explainability was enhanced using SHAP and LIME analyses, and the model showed good calibration with potential clinical net benefit. CONCLUSION: Our study suggests that ML algorithms, particularly LightGBM, show promise for predicting postoperative hemorrhage following brain tumor surgery. Biomarkers such as platelet count, chloride, and delta-CRP offer clinically meaningful insights for early risk detection. Once externally validated, the integration of such models into clinical decision support systems could potentially improve postoperative monitoring and patient outcomes. |*Brain Neoplasms/surgery [MESH] |*Machine Learning [MESH] |*Neurosurgical Procedures/adverse effects [MESH] |*Postoperative Hemorrhage/diagnosis/etiology [MESH] |Adult [MESH] |Aged [MESH] |Algorithms [MESH] |Female [MESH] |Humans [MESH] |Male [MESH] |Middle Aged [MESH] |Retrospective Studies [MESH]Prediction of postoperative haemorrhage after cerebral tumour surgery (epmc).pdf DeepDyve Pubget Overpricing