AIMC Topic: Surgical Wound Infection

Clear Filters Showing 1 to 10 of 77 articles

External validation of the IHXGboost-P model to predict incisional hernia after midline laparotomy.

Hernia : the journal of hernias and abdominal wall surgery
BACKGROUND: Incisional hernia (IH) is a significant complication that occurs after midline laparotomy and is associated with high morbidity and economic impacts. A fundamental goal of preventing IH is to determine which patients are considered low- o...

A comprehensive feature importance analysis of surgical site infection following colorectal cancer surgery.

Scientific reports
Surgical site infection (SSI) after colorectal cancer (CRC) surgery is still a significant healthcare issue. This study aimed to analyze risk factor associated with SSI. A total of 528 consecutive CRC patients who underwent curative resections betwee...

Predicting Surgical Site Infection after Lumbar Laminectomy and Discectomy: A Cutting-edge Algorithmic Approach by Incorporating Ensembled Stacking into the Current State-of-the-art for Automated Machine Learning.

Neurosurgical review
To develop an algorithmic approach for predicting surgical site infections (SSIs) in patients undergoing lumbar laminectomy and discectomy for adult degenerative spinal disease (DSD) by incorporating ensembled stacking into state-of-the-art (SOTA) au...

Evaluating the Performance of State-of-the-Art Artificial Intelligence Chatbots Based on the WHO Global Guidelines for the Prevention of Surgical Site Infection: Cross-Sectional Study.

Journal of medical Internet research
BACKGROUND: Surgical site infection (SSI) is the most prevalent type of health care-associated infection that leads to increased morbidity and mortality and a significant economic burden. Effective prevention of SSI relies on surgeons strictly follow...

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.

Development and Evaluation of Machine Learning Models for the Identification of Surgical Site Infection in Electronic Health Records.

Surgical infections
Surgical site infection (SSI) affects 160,000-300,000 patients per year in the United States, adversely impacting a wide range of patient- and health-system outcomes. Surveillance programs for SSI are essential to quality improvement and public heal...

Choice of Machine Learning Models Is Important to Predict Post-Operative Infections in Surgical Patients.

Surgical infections
Surgical quality datasets are critical to decision-making tools including surgical infection (SI). Machine learning models (MLMs), a branch of artificial intelligence (AI), are increasingly being ingrained within surgical decision-making algorithms....

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...

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...