Background Efforts are underway to capitalize on the computational power of the data
collected in electronic medical records (EMRs) to achieve a learning health system (LHS) …

Background Diabetes mellitus (DM) is a major health concern among children with the
widespread adoption of advanced technologies. However, concerns are growing about the …

Background Algorithmic decision-making (ADM) utilises algorithms to collect and process
data and develop models to make or support decisions. Advances in artificial intelligence …

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

Background: The insulin ordering process is an opportunity to provide clinicians with
hypoglycemia risk predictions, but few hypoglycemia models centered around the insulin …

Aim In this review, we investigated how Machine Learning (ML) was utilized to predict all-
cause somatic hospital admissions and readmissions in adults. Methods We searched eight …

Background Despite notable advancements in artificial intelligence (AI) that enable complex
systems to perform certain tasks more accurately than medical experts, the impact on patient …

Context: The diagnostic methods for diabetes mellitus (DM), a chronic metabolic disorder
characterized by elevated blood sugar levels, are rapidly evolving thanks to artificial …

Background and aim It is crucial to identify a diabetes diagnosis early. Create a predictive
model utilizing machine learning (ML) to identify new cases of diabetes in primary health …

Research in various fields is currently experiencing challenges regarding the reproducibility
of results. This problem is also prevalent in machine learning (ML) research. The issue …