IMPORTANCE: There has been wide interest in using artificial intelligence (AI)-based grading of retinal images to identify diabetic retinopathy, but such a system has never been deployed and evaluated in clinical practice.
OBJECTIVE: To generate new knowledge that describes and explains the views and understanding, regarding midwifery and normal birth, that newly enrolled midwifery students hold at the commencement of their midwifery education.
AIMS: Machine learning (ML) is widely believed to be able to learn complex hidden interactions from the data and has the potential in predicting events such as heart failure (HF) readmission and death. Recent studies have revealed conflicting results...
Quantification of stillbirth risk has potential to support clinical decision-making. Studies that have attempted to quantify stillbirth risk have been hampered by small event rates, a limited range of predictors that typically exclude obstetric histo...
BACKGROUND: The ability to predict transfusions arising during hospital admission might enable economized blood supply management and might furthermore increase patient safety by ensuring a sufficient stock of red blood cells (RBCs) for a specific pa...
Health information management : journal of the Health Information Management Association of Australia
39051460
BACKGROUND: Hospital-acquired complications (HACs) have an adverse impact on patient recovery by impeding their path to full recovery and increasing healthcare costs.
BACKGROUND: Perinatal depression and anxiety significantly impact maternal and infant health, potentially leading to severe outcomes like preterm birth and suicide. Aboriginal women, despite their resilience, face elevated risks due to the long-term ...
OBJECTIVE: Diabetic retinopathy (DR) screening rates are poor in remote Western Australia where communities rely on outdated primary care-based retinal cameras. Deep learning systems (DLS) may improve access to screening, however, require validation ...