AI Medical Compendium Topic:
Postoperative Complications

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PPCRKB: a risk factor knowledge base of postoperative pulmonary complications.

Database : the journal of biological databases and curation
Postoperative pulmonary complications (PPCs) are highly heterogeneous disorders with diverse risk factors frequently occurring after surgical interventions, resulting in significant financial burdens, prolonged hospitalization and elevated mortality ...

Utility and Comparative Performance of Current Artificial Intelligence Large Language Models as Postoperative Medical Support Chatbots in Aesthetic Surgery.

Aesthetic surgery journal
BACKGROUND: Large language models (LLMs) have revolutionized the way plastic surgeons and their patients can access and leverage artificial intelligence (AI).

KAMLN: A Knowledge-aware Multi-label Network for Lung Cancer Complication Prediction.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Surgical resection is now the only curative approach for early stage lung cancer patients. However, postoperative complications pose a significant threat to the health and life of patients. The current complication prediction methods usually ignore t...

Establishing and clinically validating a machine learning model for predicting unplanned reoperation risk in colorectal cancer.

World journal of gastroenterology
BACKGROUND: Colorectal cancer significantly impacts global health, with unplanned reoperations post-surgery being key determinants of patient outcomes. Existing predictive models for these reoperations lack precision in integrating complex clinical d...

Introducing a machine learning algorithm for delirium prediction-the Supporting SURgery with GEriatric Co-Management and AI project (SURGE-Ahead).

Age and ageing
INTRODUCTION: Post-operative delirium (POD) is a common complication in older patients, with an incidence of 14-56%. To implement preventative procedures, it is necessary to identify patients at risk for POD. In the present study, we aimed to develop...

An interpretable machine learning model to predict off-pump coronary artery bypass grafting-associated acute kidney injury.

Advances in clinical and experimental medicine : official organ Wroclaw Medical University
BACKGROUND: Off-pump coronary artery bypass grafting-associated acute kidney injury (OPCAB-AKI) is related to 30-day perioperative mortality. Existing mathematical models cannot be applied to help clinicians make early diagnosis and intervention deci...

Explainable Machine Learning Model to Preoperatively Predict Postoperative Complications in Inpatients With Cancer Undergoing Major Operations.

JCO clinical cancer informatics
PURPOSE: Preoperative prediction of postoperative complications (PCs) in inpatients with cancer is challenging. We developed an explainable machine learning (ML) model to predict PCs in a heterogenous population of inpatients with cancer undergoing s...

Feasibility and safety of robotic liver resection for huge (≥10 cm) hepatocellular carcinoma in a single centre: A propensity score-matched single-surgeon study.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: The applicability of robot-assisted resection for huge hepatocellular carcinoma (HCC) of ≥10 cm remains contentious with limited available data.

Surgical proficiency in laparoscopic radical cystectomy with extracorporeal urinary diversion and its adequacy for the execution of robot-assisted radical cystectomy with intracorporeal urinary diversion.

Asian journal of endoscopic surgery
INTRODUCTION: The number of facilities adopting intracorporeal urinary diversion (ICUD) using robots instead of extracorporeal urinary diversion (ECUD) is increasing. However, guidance on how to introduce robot-assisted radical cystectomy (RARC) + IC...