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

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 machine learning (ML) tools.

Objective: The aim of this systematic review was to assess the performance as well as the methodological quality of validated ML models for the prediction of SSIs.

Targeted antimicrobial regimens for Gram-negative prosthetic joint infections: a prospective multicenter study.

Fluoroquinolones (FQs) are considered the most effective antimicrobial treatment for Gram-negative prosthetic joint infection (GN-PJI). Alternatives are needed due to increasing FQ resistance and side effects. We aimed to compare different targeted antimicrobial strategies for GN-PJI managed by debridement, antibiotics, and implant retention (DAIR) or one-stage revision surgery (1SR) and to review the literature of oral treatment options for GN-PJI.

Handling missing data in variational autoencoder based item response theory.

Recently Variational Autoencoders (VAEs) have been proposed as a method to estimate high dimensional Item Response Theory (IRT) models on large datasets. Although these improve the efficiency of estimation drastically compared to traditional methods, they have no natural way to deal with missing values. In this paper, we adapt three existing methods from the VAE literature to the IRT setting and propose one new method.

Rate of nosocomial MRSA transmission evaluated via contact screening.

Background: The prevention of methicillin-resistant S. aureus (MRSA) transmission in the healthcare setting is a priority in Infection Control practices. A cornerstone of this policy is contact tracing of nosocomial contacts after an unexpected MRSA finding.

Use of proxy indicators for automated surveillance of severe acute respiratory infection, the Netherlands, 2017 to 2023: a proof-of-concept study.

BackgroundEffective pandemic preparedness requires robust severe acute respiratory infection (SARI) surveillance. However, identifying SARI patients based on symptoms is time-consuming. Using the number of reverse transcription (RT)-PCR tests or contact and droplet precaution labels as a proxy for SARI could accurately reflect the epidemiology of patients presenting with SARI.