Abstract Background: Natural Language Processing (NLP) is widely used to extract clinical
insights from Electronic Health Records (EHRs). However, the lack of annotated data …

This narrative literature review undertakes a comprehensive examination of the burgeoning
field, tracing the development of artificial intelligence (AI)-powered tools for depression and …

The Covid-19 pandemic has pushed the Intensive Care Units (ICUs) into significant
operational disruptions. The rapid evolution of this disease, the bed capacity constraints, the …

Objective: Clinical applications of machine learning are promising as a tool to improve
patient outcomes through assisting diagnoses, treatment, and analyzing risk factors for …

Psychiatric practice faces many challenges in the first quarter of 21st century. Society has
transformed, as have training requirements and patient expectations, underlining an urgent …

Background As the ageing population continues to grow in many countries, the prevalence
of geriatric diseases is on the rise. In response, healthcare providers are exploring novel …

Background Smoking is a risk factor for many chronic diseases. Multiple smoking status
ascertainment algorithms have been developed for population-based electronic health …

Electronic medical records (EMRs) have many benefits in clinical research in gerontology,
enabling data analysis, development of prognostic tools and disease risk prediction. EMRs …

Objectives To (1) explore trends of risk of bias (ROB) in prediction research over time
following key methodological publications, using the Prediction model Risk Of Bias …

Background Poor functional status is a key marker of morbidity, yet is not routinely captured
in clinical encounters. We developed and evaluated the accuracy of a machine learning …