AIMC Topic: Postoperative Complications
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A novel generative multi-task representation learning approach for predicting postoperative complications in cardiac surgery patients.
Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2025
OBJECTIVE: Early detection of surgical complications allows for timely therapy and proactive risk mitigation. Machine learning (ML) can be leveraged to identify and predict patient risks for postoperative complications. We developed and validated the...
Machine learning model-based prediction of postpancreatectomy acute pancreatitis following pancreaticoduodenectomy: A retrospective cohort study.
World journal of gastroenterology
Feb 28, 2025
BACKGROUND: The International Study Group of Pancreatic Surgery has established the definition and grading system for postpancreatectomy acute pancreatitis (PPAP). There are no established machine learning models for predicting PPAP following pancrea...
Comparison of Predictive Models for Keloid Recurrence Based on Machine Learning.
Journal of cosmetic dermatology
Feb 1, 2025
OBJECTIVES: To establish, evaluate and compare three recurrence prediction models for keloid patients using machine learning methods.
Machine learning prediction of hepatic encephalopathy for long-term survival after transjugular intrahepatic portosystemic shunt in acute variceal bleeding.
World journal of gastroenterology
Jan 28, 2025
BACKGROUND: Transjugular intrahepatic portosystemic shunt (TIPS) is an effective intervention for managing complications of portal hypertension, particularly acute variceal bleeding (AVB). While effective in reducing portal pressure and preventing re...
Middle Aged
Risk Assessment
Postoperative Complications
Logistic Models
Treatment Outcome
Esophageal and Gastric Varices
Gastrointestinal Hemorrhage
Kaplan-Meier Estimate
Hepatic Encephalopathy
Female
Risk Factors
Aged
Adult
Male
Portasystemic Shunt, Transjugular Intrahepatic
Humans
Hypertension, Portal
Machine Learning
Retrospective Studies
Artificial Intelligence in Predicting Ocular Hypertension After Descemet Membrane Endothelial Keratoplasty.
Investigative ophthalmology & visual science
Jan 2, 2025
PURPOSE: Descemet membrane endothelial keratoplasty (DMEK) has emerged as a novel approach in corneal transplantation over the past two decades. This study aims to identify predisposing risk factors for post-DMEK ocular hypertension (OHT) and develop...
Machine learning for early prediction of the infection in patients with urinary stone after treatment of holmium laser lithotripsy.
PloS one
Jan 1, 2025
Patients after holmium laser lithotripsy have a certain probability of getting postoperative infection. An early and accurate diagnosis of postoperative infection allows a timely administration of appropriate antibiotic treatment. However, doctors ca...
Unveiling the Immune Landscape of Delirium through Single-Cell RNA Sequencing and Machine Learning: Towards Precision Diagnosis and Therapy.
Psychogeriatrics : the official journal of the Japanese Psychogeriatric Society
Jan 1, 2025
BACKGROUND: Postoperative delirium (POD) poses significant clinical challenges regarding its diagnosis and treatment. Identifying biomarkers that can predict and diagnose POD is crucial for improving patient outcomes.
Investigating surgeon performance metrics as key predictors of robotic herniorrhaphy outcomes using iterative machine learning models: retrospective study.
BJS open
Dec 30, 2024
BACKGROUND: Robotic data streams allow for capture of objective performance indicators, providing the ability to quantify and analyse operator technique and movement in optimizing postoperative outcomes. This study provided proof-of-concept demonstra...
Advances in artificial intelligence for predicting complication risks post-laparoscopic radical gastrectomy for gastric cancer: A significant leap forward.
World journal of gastroenterology
Nov 21, 2024
In a recent paper, Hong developed an artificial intelligence (AI)-driven predictive scoring system for potential complications following laparoscopic radical gastrectomy for gastric cancer patients. They demonstrated that integrating AI with random ...
Development of machine learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma.
Medicine
Nov 1, 2024
The aim of this study was to develop a machine-learning prediction model for AKI after craniotomy and evacuation of hematoma in craniocerebral trauma. We included patients who underwent craniotomy and evacuation of hematoma due to traumatic brain inj...
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