The secondary use of electronic health records for data mining: Data characteristics and challenges
The primary objective of implementing Electronic Health Records (EHRs) is to improve the
management of patients' health-related information. However, these records have also been …
management of patients' health-related information. However, these records have also been …
Key use cases for artificial intelligence to reduce the frequency of adverse drug events: a scoping review
Adverse drug events (ADEs) represent one of the most prevalent types of health-care-
related harm, and there is substantial room for improvement in the way that they are …
related harm, and there is substantial room for improvement in the way that they are …
Machine-learning-based adverse drug event prediction from observational health data: A review
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 …
and fatalities. Machine learning models have been developed to assess individual patient …
A systematic review of time series classification techniques used in biomedical applications
Background: Digital clinical measures collected via various digital sensing technologies
such as smartphones, smartwatches, wearables, and ingestible and implantable sensors …
such as smartphones, smartwatches, wearables, and ingestible and implantable sensors …
Informative presence and observation in routine health data: a review of methodology for clinical risk prediction
Objective Informative presence (IP) is the phenomenon whereby the presence or absence of
patient data is potentially informative with respect to their health condition, with informative …
patient data is potentially informative with respect to their health condition, with informative …
Electronic health record-based prediction models for in-hospital adverse drug event diagnosis or prognosis: a systematic review
IAR Yasrebi-de Kom, DA Dongelmans… - Journal of the …, 2023 - academic.oup.com
Objective We conducted a systematic review to characterize and critically appraise
developed prediction models based on structured electronic health record (EHR) data for …
developed prediction models based on structured electronic health record (EHR) data for …
Historical profile will tell? A deep learning-based multi-level embedding framework for adverse drug event detection and extraction
L Xia - Decision Support Systems, 2022 - Elsevier
Analyzing adverse drug events (ADEs) is an integral part of drug safety monitoring, which
plays a significant role in medication decision-making. The increasing prevalence of health …
plays a significant role in medication decision-making. The increasing prevalence of health …
Machine learning in causal inference: Application in pharmacovigilance
Monitoring adverse drug events or pharmacovigilance has been promoted by the World
Health Organization to assure the safety of medicines through a timely and reliable …
Health Organization to assure the safety of medicines through a timely and reliable …
Extracting adverse drug events from clinical Notes: A systematic review of approaches used
S Modi, KA Kasmiran, NM Sharef… - Journal of Biomedical …, 2024 - Elsevier
Background An adverse drug event (ADE) is any unfavorable effect that occurs due to the
use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text …
use of a drug. Extracting ADEs from unstructured clinical notes is essential to biomedical text …
Counterfactual explanations for survival prediction of cardiovascular ICU patients
In recent years, machine learning methods have been rapidly implemented in the medical
domain. However, current state-of-the-art methods usually produce opaque, black-box …
domain. However, current state-of-the-art methods usually produce opaque, black-box …