Data-Driven Approaches in Antimicrobial Resistance: Machine Learning Solutions.

The emergence of antimicrobial resistance (AMR) due to the misuse and overuse of antibiotics has become a critical threat to global public health. There is a dire need to forecast AMR to understand the underlying mechanisms of resistance for the development of effective interventions. This paper explores the capability of machine learning (ML) methods, particularly unsupervised learning methods, to enhance the understanding and prediction of AMR.

The Synergy of Machine Learning and Epidemiology in Addressing Carbapenem Resistance: A Comprehensive Review.

Background/objectives: Carbapenem resistance poses a significant threat to public health by undermining the efficacy of one of the last lines of antibiotic defense. Addressing this challenge requires innovative approaches that can enhance our understanding and ability to combat resistant pathogens. This review aims to explore the integration of machine learning (ML) and epidemiological approaches to understand, predict, and combat carbapenem-resistant pathogens.

Open versus Transcutaneous (Ultrasound-Guided and Based on Anatomic Landmarks) Tunneled Venous Access to the Right Internal Jugular Vein in Children: A Prospective Single-Center Study.

Background: The purpose of this study was to compare the immediate and long-term complications that are associated with the utilized techniques for the insertion of indwelling central venous catheters, that is the open surgical technique, the ultrasound-guided technique, and the transcutaneous technique based on external anatomical landmarks in the right internal jugular vein, to a pediatric population.

Methods: This was a prospective randomized trial based on a pediatric patient population under 16 years of age of a tertiary pediatric-oncological hospital. The procedure was performed by a medical team with varying experience regarding the percutaneous and open insertion methods.

Automated ML Techniques for Predicting COVID-19 Mortality in the ICU.

The COVID-19 infection is still a serious threat to public health and healthcare systems. Numerous practical machine learning applications have been investigated in this context to support clinical decision-making, forecast disease severity and admission to the intensive care unit, as well as to predict the demand for hospital beds, equipment, and staff in the future. We retrospectively analyzed demographics, and routine blood biomarkers from consecutive Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, during a 17-month period, relative to the outcome, in order to build a prognostic model.

Prediction of COVID-19 Mortality in the Intensive Care Unit Using Machine Learning.

Since its emergence, the COVID-19 pandemic still poses a major global health threat. In this setting, a number of useful machine learning applications have been explored to assist clinical decision-making, predict the severity of disease and admission to the intensive care unit, and also to estimate future demand for hospital beds, equipment, and staff. The present study examined demographic data, hematological and biochemical markers routinely measured in Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, in relation to the ICU outcome, during the second and third Covid-19 waves, from October 2020 until February 2022.

Using Machine Learning to Predict Antimicrobial Resistance-A Literature Review.

Machine learning (ML) algorithms are increasingly applied in medical research and in healthcare, gradually improving clinical practice. Among various applications of these novel methods, their usage in the combat against antimicrobial resistance (AMR) is one of the most crucial areas of interest, as increasing resistance to antibiotics and management of difficult-to-treat multidrug-resistant infections are significant challenges for most countries worldwide, with life-threatening consequences. As antibiotic efficacy and treatment options decrease, the need for implementation of multimodal antibiotic stewardship programs is of utmost importance in order to restrict antibiotic misuse and prevent further aggravation of the AMR problem.

Setting up an Easy-to-Use Machine Learning Pipeline for Medical Decision Support: A Case Study for COVID-19 Diagnosis Based on Deep Learning with CT Scans.

Coronavirus disease (COVID-19) constitutes an ongoing global health problem with significant morbidity and mortality. It usually presents characteristic findings on a chest CT scan, which may lead to early detection of the disease. A timely and accurate diagnosis of COVID-19 is the cornerstone for the prompt management of the patients.

Evaluation of quality of life outcomes following palliative radiotherapy in bone metastases: A literature review.

Purpose: To assess the quality of life (QoL) following palliative radiotherapy (RT) in patients with painful bone metastases.

Methods: A literature search limited to English-written publications was carried out, through the Cochrane Central Register of Controlled Trials (November 2018), OvidSP and PubMedCentral (1940-November 2018) databases. Subject headings and keywords included "quality of life"(QoL), "bone metastases", "palliative therapy", "pain" and "radiotherapy".