AIMC Topic: Postoperative Complications

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Pediatric cardiac surgery: machine learning models for postoperative complication prediction.

Journal of anesthesia Jul 19, 2024
PURPOSE: Managing children undergoing cardiac surgery with cardiopulmonary bypass (CPB) presents a significant challenge for anesthesiologists. Machine Learning (ML)-assisted tools have the potential to enhance the recognition of patients at risk of ...
Support Vector Machine Area Under Curve Cardiac Surgical Procedures Cardiopulmonary Bypass Infant Child, Preschool Child Logistic Models Female Male Risk Assessment Postoperative Complications Humans Machine Learning
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Effect of dexamethasone pretreatment using deep learning on the surgical effect of patients with gastrointestinal tumors.

PloS one Jul 17, 2024
To explore the application efficacy and significance of deep learning in anesthesia management for gastrointestinal tumors (GITs) surgery, 80 elderly patients with GITs who underwent surgical intervention at our institution between January and Septem...
Length of Stay Aged, 80 and over Humans Cognition Male Female Anesthesia Gastrointestinal Neoplasms Postoperative Complications Aged Dexamethasone Deep Learning
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Utilising intraoperative respiratory dynamic features for developing and validating an explainable machine learning model for postoperative pulmonary complications. Comment on Br J Anaesth 2024; 132: 1315-26.

British journal of anaesthesia Jul 15, 2024
Humans Machine Learning Postoperative Complications Monitoring, Intraoperative Lung Diseases
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An assessment of the value of deep neural networks in genetic risk prediction for surgically relevant outcomes.

PloS one Jul 15, 2024
INTRODUCTION: Postoperative complications affect up to 15% of surgical patients constituting a major part of the overall disease burden in a modern healthcare system. While several surgical risk calculators have been developed, none have so far been ...
Risk Assessment Postoperative Complications Deep Learning Genome-Wide Association Study Risk Factors Atrial Fibrillation Aged Humans Venous Thromboembolism Male ROC Curve Neural Networks, Computer Middle Aged Genetic Predisposition to Disease Female Polymorphism, Single Nucleotide Pneumonia
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Predicting Secondary Vertebral Compression Fracture After Vertebral Augmentation via CT-Based Machine Learning Radiomics-Clinical Model.

Academic radiology Jul 10, 2024
RATIONALE AND OBJECTIVES: Secondary vertebral compression fractures (SVCF) are very common in patients after vertebral augmentation (VA). The aim of this study was to establish a radiomic-based model to predict SVCF and specify appropriate treatment ...
Vertebroplasty Osteoporotic Fractures Aged, 80 and over Tomography, X-Ray Computed Radiomics Humans Postoperative Complications Female Machine Learning Male Nomograms Reproducibility of Results Retrospective Studies Middle Aged Spinal Fractures Aged Fractures, Compression
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Current advances in the use of artificial intelligence in predicting and managing urological complications.

International urology and nephrology Jul 9, 2024
BACKGROUND: Artificial intelligence (AI) has emerged as a promising avenue for improving patient care and surgical outcomes in urological surgery. However, the extent of AI's impact in predicting and managing complications is not fully elucidated.
Artificial Intelligence Postoperative Complications Urologic Diseases Humans Urologic Surgical Procedures
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Predictive modeling of lower extreme deep vein thrombosis following radical gastrectomy for gastric cancer: based on multiple machine learning methods.

Scientific reports Jul 8, 2024
Postoperative venous thromboembolic events (VTEs), such as lower extremity deep vein thrombosis (DVT), are major risk factors for gastric cancer (GC) patients following radical gastrectomy. Accurately predicting and managing these risks is crucial fo...
Logistic Models Venous Thrombosis Gastrectomy Middle Aged Postoperative Complications Case-Control Studies ROC Curve Humans Machine Learning Retrospective Studies Female Risk Factors Aged Stomach Neoplasms Male
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Machine learning models to predict systemic inflammatory response syndrome after percutaneous nephrolithotomy.

BMC urology Jul 8, 2024
OBJECTIVE: The objective of this study was to develop and evaluate the performance of machine learning models for predicting the possibility of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL).
Humans Adult Male Middle Aged Postoperative Complications Machine Learning Retrospective Studies Female Aged Systemic Inflammatory Response Syndrome Predictive Value of Tests Kidney Calculi Nephrolithotomy, Percutaneous
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Interpretable Machine Learning Models Based on Shapley Additive Explanations for Predicting the Risk of Cerebrospinal Fluid Leakage in Lumbar Fusion Surgery.

Spine Jul 4, 2024
STUDY DESIGN: Retrospective study.
Lumbar Vertebrae Risk Factors Humans Aged Machine Learning Adult Middle Aged Retrospective Studies Male Postoperative Complications Female Cerebrospinal Fluid Leak Spinal Fusion
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Early Postoperative Prediction of Complications and Readmission After Colorectal Cancer Surgery Using an Artificial Neural Network.

Diseases of the colon and rectum Jul 3, 2024
BACKGROUND: Early predictors of postoperative complications can risk-stratify patients undergoing colorectal cancer surgery. However, conventional regression models have limited power to identify complex nonlinear relationships among a large set of v...
Female Aged Colorectal Neoplasms Male Humans Neural Networks, Computer Logistic Models Retrospective Studies Elective Surgical Procedures Middle Aged Patient Readmission Risk Assessment Anastomotic Leak Postoperative Complications ROC Curve
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