AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Surgical Wound Infection

Showing 1 to 10 of 67 articles

Clear Filters

Systematic evaluation of machine learning models for postoperative surgical site infection prediction.

PloS one
BACKGROUND: Surgical site infections (SSIs) lead to increased mortality and morbidity, as well as increased healthcare costs. Multiple models for the prediction of this serious surgical complication have been developed, with an increasing use of mach...

Meta-lasso: new insight on infection prediction after minimally invasive surgery.

Medical & biological engineering & computing
Surgical site infection (SSI) after minimally invasive lung cancer surgery constitutes an important factor influencing the direct and indirect economic implications, patient prognosis, and the 5-year survival rate for early-stage lung cancer patients...

-A machine learning model to predict surgical site infection after surgery of lower extremity fractures.

International orthopaedics
PURPOSE: This study aimed to develop machine learning algorithms for identifying predictive factors associated with the risk of postoperative surgical site infection in patients with lower extremity fractures.

Precision in Prevention: Tailoring Single-Use Negative Pressure Wound Therapy Utilization Through Artificial Intelligence-Based Surgical Site Complications Risk and Cost Modeling.

Surgical infections
Surgical site complications (SSCs) are common, yet preventable hospital-acquired conditions. Single-use negative pressure wound therapy (sNPWT) has been shown to be effective in reducing rates of these complications. In the era of value-based care, ...

Bowel preparation before elective right colectomy: Multitreatment machine-learning analysis on 2,617 patients.

Surgery
BACKGROUND: In the worldwide, real-life setting, some candidates for right colectomy still receive no bowel preparation, some receive oral antibiotics alone, some receive mechanical bowel preparation alone, and some receive mechanical bowel preparati...

Digital wound monitoring with artificial intelligence to prioritise surgical wounds in cardiac surgery patients for priority or standard review: protocol for a randomised feasibility trial (WISDOM).

BMJ open
INTRODUCTION: Digital surgical wound monitoring for patients at home is becoming an increasingly common method of wound follow-up. This regular monitoring improves patient outcomes by detecting wound complications early and enabling treatment to star...

Application of machine learning algorithms to predict postoperative surgical site infections and surgical site occurrences following inguinal hernia surgery.

Hernia : the journal of hernias and abdominal wall surgery
PURPOSE: This study aimed to develop, validate, and evaluate machine learning (ML) algorithms for predicting Surgical site infections (SSI) and surgical site occurrences (SSO) after elective open inguinal hernia surgery.

Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?

The Journal of international medical research
OBJECTIVE: To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches.

Development, validation, and clinical evaluation of a machine-learning based model for diagnosing early infection after cardiovascular surgery (DEICS): a multi-center cohort study.

International journal of surgery (London, England)
BACKGROUND: This study addresses the critical need for timely and accurate diagnosis of early postoperative infection (EPI) following cardiac surgery. EPI significantly impacts patient outcomes and healthcare costs, making its early detection vital.