[HTML][HTML] A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluation
Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …
belonging to one class is lower than the other. Ensemble learning combines multiple models …
Intelligent telehealth in pharmacovigilance: a future perspective
Pharmacovigilance improves patient safety by detecting and preventing adverse drug
events. However, challenges exist that limit adverse drug event detection, resulting in many …
events. However, challenges exist that limit adverse drug event detection, resulting in many …
Impact of digital advancements on accounting, auditing and reporting literature: insights, practice implications and future research directions
MR Rabbani - Journal of Accounting & Organizational Change, 2024 - emerald.com
Purpose The study aims to use bibliometric and scientometric analysis to conduct a detailed
investigation on the impact of disruptive technologies in accounting and reporting literature …
investigation on the impact of disruptive technologies in accounting and reporting literature …
Artificial intelligence in pharmacovigilance: an introduction to terms, concepts, applications, and limitations
JK Aronson - Drug Safety, 2022 - Springer
The tools of artificial intelligence (AI) have enormous potential to enhance activities in
pharmacovigilance. Pharmacovigilance experts need not be AI experts, but they should …
pharmacovigilance. Pharmacovigilance experts need not be AI experts, but they should …
Validation of artificial intelligence to support the automatic coding of patient adverse drug reaction reports, using nationwide pharmacovigilance data
GL Martin, J Jouganous, R Savidan, A Bellec… - Drug Safety, 2022 - Springer
Introduction Adverse drug reaction reports are usually manually assessed by
pharmacovigilance experts to detect safety signals associated with drugs. With the recent …
pharmacovigilance experts to detect safety signals associated with drugs. With the recent …
Application of artificial intelligence in a real-world research for predicting the risk of liver metastasis in T1 colorectal cancer
T Han, J Zhu, X Chen, R Chen, Y Jiang, S Wang… - Cancer Cell …, 2022 - Springer
Background Liver is the most common metastatic site of colorectal cancer (CRC) and liver
metastasis (LM) determines subsequent treatment as well as prognosis of patients …
metastasis (LM) determines subsequent treatment as well as prognosis of patients …
Industry perspective on artificial intelligence/machine learning in pharmacovigilance
R Kassekert, N Grabowski, D Lorenz, C Schaffer… - Drug Safety, 2022 - Springer
TransCelerate reports on the results of 2019, 2020, and 2021 member company (MC)
surveys on the use of intelligent automation in pharmacovigilance processes. MCs …
surveys on the use of intelligent automation in pharmacovigilance processes. MCs …
Will the future of pharmacovigilance be more automated?
F Salvo, J Micallef, A Lahouegue… - Expert Opinion on …, 2023 - Taylor & Francis
Introduction: Artificial intelligence (AI) based tools offer new opportunities for
pharmacovigilance (PV) activities. Nevertheless, their contribution to PV needs to be tailored …
pharmacovigilance (PV) activities. Nevertheless, their contribution to PV needs to be tailored …
[HTML][HTML] A hybrid machine learning and natural language processing model for early detection of acute coronary syndrome
Acute coronary syndrome (ACS) is a leading cause of mortality and morbidity. Predicting the
associated risks of patients with chest pain using electronic health record data can help …
associated risks of patients with chest pain using electronic health record data can help …
An artificial intelligence algorithm for co‐clustering to help in pharmacovigilance before and during the COVID‐19 pandemic
Aims Monitoring drug safety in real‐world settings is the primary aim of pharmacovigilance.
Frequent adverse drug reactions (ADRs) are usually identified during drug development …
Frequent adverse drug reactions (ADRs) are usually identified during drug development …