Investigation of a Data Split Strategy Involving the Time Axis in Adverse Event Prediction Using Machine Learning [PDF]
, 2022 Adverse events are a serious issue in drug development and many prediction methods using machine learning have been developed. The random split cross-validation is the de facto standard for model building and evaluation in machine learning, but care should be taken in adverse event prediction because this approach does not match to the real-world ...
arxiv +1 more source
Multi-Class Multiple Instance Learning for Predicting Precursors to Aviation Safety Events [PDF]
arXiv, 2021 In recent years, there has been a rapid growth in the application of machine learning techniques that leverage aviation data collected from commercial airline operations to improve safety. Anomaly detection and predictive maintenance have been the main targets for machine learning applications.
arxiv
Rationale and Design of the Genetic Contribution to Drug Induced Renal Injury (DIRECT) Study
Kidney International Reports, 2016 Nephrotoxicity from drugs accounts for 18% to 27% of cases of acute kidney injury. Determining a genetic predisposition may potentially be important in minimizing risk.
Linda Awdishu +10 more
doaj +1 more source
Autoimmune hemolytic anemia, adverse event to venetoclax [PDF]
Farmacia Hospitalaria, 2019 -
Carmen Carriles-Fernández +4 more
doaj +1 more source
Survival analysis for AdVerse events with VarYing follow-up times (SAVVY): Rationale and statistical concept of a meta-analytic study [PDF]
Biom. J., 2021, 63, 650-670, 2019 The assessment of safety is an important aspect of the evaluation of new therapies in clinical trials, with analyses of adverse events being an essential part of this. Standard methods for the analysis of adverse events such as the incidence proportion, i.e.
arxiv +1 more source
A Sui Generis QA Approach using RoBERTa for Adverse Drug Event Identification [PDF]
BMC Bioinformatics 22, 330 (2021), 2020 Extraction of adverse drug events from biomedical literature and other textual data is an important component to monitor drug-safety and this has attracted attention of many researchers in healthcare. Existing works are more pivoted around entity-relation extraction using bidirectional long short term memory networks (Bi-LSTM) which does not attain the
arxiv +1 more source
JMIR Medical Informatics, 2014 BackgroundThe Food and Drug Administration’s (FDA) Adverse Event Reporting System (FAERS) is a repository of spontaneously-reported adverse drug events (ADEs) for FDA-approved prescription drugs.
Polepalli Ramesh, Balaji +5 more
doaj +1 more source
Boosting Adverse Drug Event Normalization on Social Media: General-Purpose Model Initialization and Biomedical Semantic Text Similarity Benefit Zero-Shot Linking in Informal Contexts [PDF]
arXiv, 2023 Biomedical entity linking, also known as biomedical concept normalization, has recently witnessed the rise to prominence of zero-shot contrastive models. However, the pre-training material used for these models has, until now, largely consisted of specialist biomedical content such as MIMIC-III clinical notes (Johnson et al., 2016) and PubMed papers ...
arxiv
Frontiers in Pharmacology, 2023 Objective: To investigate adverse events (AEs) associated with denosumab (Dmab) and zoledronic acid (ZA), compare their association strengths, and explore potential applications to provide clinical reference.Methods: We collected data from FAERS from ...
Si Su +8 more
doaj +1 more source
DS4DH at #SMM4H 2023: Zero-Shot Adverse Drug Events Normalization using Sentence Transformers and Reciprocal-Rank Fusion [PDF]
arXiv, 2023 This paper outlines the performance evaluation of a system for adverse drug event normalization, developed by the Data Science for Digital Health (DS4DH) group for the Social Media Mining for Health Applications (SMM4H) 2023 shared task 5. Shared task 5 targeted the normalization of adverse drug event mentions in Twitter to standard concepts of the ...
arxiv