Background Machine learning (ML) is increasingly being used in many areas of health care.
Its use in infection management is catching up as identified in a recent review in this journal …

Background Medical researchers and clinicians have shown much interest in develo**
machine learning (ML) algorithms to detect/predict surgical site infections (SSIs). However …

Background: Surgical site infections (SSIs) have a major role in the evolution of medical
care. Despite centuries of medical progress, the management of surgical infection remains a …

Infections encompass a set of medical conditions of very diverse kinds that can pose a
significant risk to health, and even death. As with many other diseases, early diagnosis can …

Objective Much has been invested in big data analytics to improve health and reduce costs.
However, it is unknown whether these investments have achieved the desired goals. We …

Background Identification of patients at high risk of surgical-site infection may allow
clinicians to target interventions and monitoring to minimize associated morbidity. The aim of …

Background: There has been tremendous growth in the amount of new surgical site infection
(SSI) data generated. Key challenges exist in understanding the data for robust clinical …

Objective: The incidence of surgical site infections may be underreported if the data are not
routinely validated for accuracy. Our goal was to investigate the communicated SSI rate from …

Surgical site infections are hospital-acquired infections resulting in severe risk for patients
and significantly increased costs for healthcare providers. In this work, we show how to …

Surgical site infections impact hospital readmission rates, length of stay, and patient and
hospital expense. The use of computational intelligence methods can help to predict the risk …