AIMC Topic: Postoperative Complications

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Machine learning model based on preoperative MRI and clinical data for predicting pancreatic fistula after pancreaticoduodenectomy.

BMC medical imaging
OBJECTIVE: To establish and validate a machine learning model using preoperative multi-sequence MRI radiomic features and clinical data to predict pancreatic fistula after pancreaticoduodenectomy (PD).

Machine Learning-Based prediction models for postoperative delirium: a systematic review and Meta-Analysis.

BMC psychiatry
BACKGROUND: The number of risk prediction models for postoperative delirium(POD) has increased yearly, but their quality and applicability in clinical practice and future research remain unclear.

Construction of an automated machine learning-based predictive model for postoperative pulmonary complications risk in non-small cell lung cancer patients undergoing thoracoscopic surgery.

PloS one
OBJECTIVE: To develop a predictive framework integrating machine learning and clinical parameters for postoperative pulmonary complications (PPCs) in non-small cell lung cancer (NSCLC) patients undergoing video-assisted thoracic surgery (VATS).

Predicting postoperative fever in culture-negative patients undergoing mini-PCNL using MAP score-augmented machine learning: a retrospective cohort study.

World journal of urology
PURPOSE: Postoperative fever is a common complication following percutaneous nephrolithotomy (PCNL) that occurs even in patients with sterile urine cultures. Traditional risk-assessment tools are insufficient in this subset of patients. This study ai...

Letter to the editor: interpretable machine learning model predicts 1‑year inguinal hernia risk after robot‑assisted radical prostatectomy.

Journal of robotic surgery
We read with interest the recent article by Yu et al., "Interpretable machine learning model predicts 1-year inguinal hernia risk after robot-assisted radical prostatectomy" (DOI: 10.1007/s11701-025-02723-5) , which represents an important step in ap...

Development and validation of an explainable machine learning model for predicting sepsis risk following flexible ureteroscopic lithotripsy.

Urolithiasis
Sepsis is a severe complication of flexible ureteroscopic lithotripsy (fURL), a widely used treatment for kidney stones. This study aimed to develop and validate a predictive model based on machine learning (ML) for assessing the risk of sepsis follo...

Predictive modelling of clinically significant depressive symptoms after coronary artery bypass graft surgery: protocol for a multicentre observational study in two Swiss hospitals (the PsyCor study).

BMJ open
INTRODUCTION: Coronary artery bypass grafting (CABG) remains one of the most commonly performed cardiac surgeries worldwide. Despite surgical advancements, a significant proportion of patients experience psychological distress following surgery, with...

Interpretable machine learning model predicts 1-year inguinal hernia risk after robot-assisted radical prostatectomy.

Journal of robotic surgery
Inguinal hernia represents a clinically significant yet underreported complication of robot-assisted radical prostatectomy (RARP) for localized prostate cancer, with a notably high incidence within the first postoperative year. Despite its adverse im...