Adverse drug events (ADEs) are responsible for a significant number of hospital admissions
and fatalities. Machine learning models have been developed to assess individual patient …

The digitization of healthcare has facilitated the sharing and re-using of medical data but has
also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Portability …

Background: Therapeutic drug monitoring (TDM) and model-informed precision dosing
(MIPD) have greatly benefitted from computational and mathematical advances over the …

Background The reporting of machine learning (ML) prognostic and diagnostic modeling
studies is often inadequate, making it difficult to understand and replicate such studies. To …

Data mining (DM) and machine learning (ML) applications in medical diagnostic systems
are budding. Data privacy is essential in these systems as healthcare data are highly …

Abstract Background: Electronic Clinical Narratives (ECNs) store valuable individual's health
information. However, there are few available open-source data. Besides, ECNs can be …

Machine unlearning (MU) is a field that is gaining increasing attention due to the need to
remove or modify predictions made by machine learning (ML) models. While training models …

How can we improve the quality of data collected in general practice? | The BMJ Skip to main
content Intended for healthcare professionals Access provided by Google Indexer Subscribe …

Prevention and treatment of complications are the backbone of medical care, particularly in
critical care settings. Early detection and prompt intervention can potentially prevent …

Simple Summary Obtaining and structuring information about the characteristics, treatments,
and outcomes of people living with cancer for research purposes is difficult and resource …