AIMC Topic: Postoperative Complications
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Prediction of postoperative infection through early-stage salivary microbiota following kidney transplantation using machine learning techniques.
Renal failure
Jul 3, 2025
Kidney transplantation (KT) is an effective treatment for end-stage renal disease; however, the lifelong immunosuppressive regimen increases the risk of infection, presenting significant clinical, and economic challenges. Identifying predictive bioma...
Machine learning and transformer models for prediction of postoperative pneumonia risk in patients with lower limb fractures.
Scientific reports
Jul 1, 2025
Postoperative pneumonia, a prevalent complication arising from lower limb fracture surgery, can significantly prolong hospitalization periods and elevate mortality rates. Consequently, early prevention and identification of this condition are crucial...
Preoperative prediction of major adverse outcomes after total arch replacement in acute type A aortic dissection based on machine learning ensemble.
Scientific reports
Jul 1, 2025
A machine learning model was developed and validated to predict postoperative complications in patients with acute type A aortic dissection (ATAAD) who underwent total arch replacement combined with frozen elephant trunk (TAR + FET), with the goal of...
Developed a knowledge base of risk factors for postoperative cognitive dysfunction: a retrospective database study.
Scientific reports
Jul 1, 2025
Postoperative cognitive dysfunction (POCD), a heterogeneous spectrum of surgery/anesthesia-associated neurocognitive impairments, represents a critical clinical challenge due to its associations with prolonged hospitalization, increased mortality, an...
GenAI exceeds clinical experts in predicting acute kidney injury following paediatric cardiopulmonary bypass.
Scientific reports
Jul 1, 2025
The emergence of large language models (LLMs) opens new horizons to leverage, often unused, information in clinical text. Our study aims to capitalise on this new potential. Specifically, we examine the utility of text embeddings generated by LLMs in...
Establishment of a machine learning-based predictive model with dual-center external validation: investigating the role of robotic surgery in preventing delayed gastric emptying for right-sided colon cancer.
Journal of robotic surgery
Jun 30, 2025
After colorectal surgery, delayed gastric emptying (DGE) is a clinically significant postoperative complication that significantly lowers patients' quality of life. The evolving application of robotic surgery in gastrointestinal oncology continues to...
Predictive modeling for early detection of refractory esophageal stricture following esophageal atresia surgery: insight from a machine learning study.
Pediatric surgery international
Jun 26, 2025
BACKGROUND: Refractory esophageal stricture (RES) presents a challenging complication after esophageal atresia (EA) repair. Earlier identification of patients with RES could help clinical decision-making. However, there are currently limited articles...
Machine learning model for the prediction of postoperative acute kidney injury (AKI) in neonates undergoing digestive surgery.
Pediatric surgery international
Jun 24, 2025
OBJECTIVE: The aim of our study is to determine the main predictors of postoperative AKI in neonates using machine learning models compared with the logistic regression model.
Development of a Machine Learning-Based Predictive Model for Postoperative Delirium in Older Adult Intensive Care Unit Patients: Retrospective Study.
Journal of medical Internet research
Jun 19, 2025
BACKGROUND: Delirium is a prevalent phenomenon among patients admitted to the geriatric intensive care unit (ICU) and can adversely impact prognosis and augment the risk of complications.
Development and validation of a cardiac surgery-associated acute kidney injury prediction model using the MIMIC-IV database.
PloS one
Jun 12, 2025
OBJECTIVE: This study aimed to develop an innovative early prediction model for acute kidney injury (AKI) following cardiac surgery in intensive care unit (ICU) settings, leveraging preoperative and postoperative clinical variables, and to identify k...
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