"I have never felt so alone and vulnerable" - A qualitative study of bereaved people's experiences of end-of-life cancer care during the Covid-19 pandemic.

Background: COVID-19 drastically affected healthcare services world-wide. In the UK, many cancer services were overwhelmed as oncology staff were reassigned, and cancer diagnoses and treatments were delayed. The impact of these pressures on end-of-life care for patients with advanced cancer and their relatives is not well understood.

Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.

Background: Blood transfusion (BT) is a critical aspect of medical care for surgical patients in the Intensive Care Unit (ICU). Timely and accurate identification of BT needs can enhance patient outcomes and healthcare resource management.

Methods: This study aims to determine whether a machine learning (ML) model can be trained to predict the need for blood transfusion (BT) in patients on the ICU after a wide range of surgeries, utilizing only data from the ICU.

Association between intraoperative hypotension and postoperative nausea and vomiting: a retrospective cohort study.

Objective: Postoperative nausea and vomiting (PONV) occurs in up to 30% of patients and its pathophysiology and mechanisms have not been completely described. Hypotension and a decrease in cardiac output are suspected to induce nausea. The hypothesis that intraoperative hypotension might influence the incidence of PONV was investigated.

Quantifying Nocturnal Scratch in Atopic Dermatitis: A Machine Learning Approach Using Digital Wrist Actigraphy.

Nocturnal scratching substantially impairs the quality of life in individuals with skin conditions such as atopic dermatitis (AD). Current clinical measurements of scratch rely on patient-reported outcomes (PROs) on itch over the last 24 h. Such measurements lack objectivity and sensitivity.

Predicting Hypoxia Using Machine Learning: Systematic Review.

Background: Hypoxia is an important risk factor and indicator for the declining health of inpatients. Predicting future hypoxic events using machine learning is a prospective area of study to facilitate time-critical interventions to counter patient health deterioration.

Objective: This systematic review aims to summarize and compare previous efforts to predict hypoxic events in the hospital setting using machine learning with respect to their methodology, predictive performance, and assessed population.