Annual review of pharmacology and toxicology
Dec 12, 2014
This article reviews several techniques from machine learning that can be used to study the problem of identifying a small number of features, from among tens of thousands of measured features, that can accurately predict a drug response. Prediction ...
It is well-known that a spontaneous reporting system suffers from significant under-reporting of adverse drug reactions from the source population. The existing methods do not adjust for such under-reporting for the calculation of measures of associa...
Adverse drug reactions (ADRs) are noxious and unexpected effects during normal drug therapy. They have caused significant clinical burden and been responsible for a large portion of new drug development failure. Molecular understanding and in silico ...
Journal of the American Medical Informatics Association : JAMIA
Oct 21, 2014
OBJECTIVE: The trade-off between the speed and simplicity of dictionary-based term recognition and the richer linguistic information provided by more advanced natural language processing (NLP) is an area of active discussion in clinical informatics. ...
This study aimed to detect and contrast the adverse drug event (ADE) signals associated with sodium zirconate cyclosilicate (SZC) and Patiromer by leveraging the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS), thereby in...
Naunyn-Schmiedeberg's archives of pharmacology
Nov 1, 2025
Despite the extensive research on medication-related adverse events (MRAEs) in healthcare, the assessment of the present scenario is made more difficult by the high degree of variability in study results. This study's primary goal was to create a cur...
Digestive and liver disease : official journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver
Sep 1, 2025
BACKGROUND: Chemotherapy-induced adverse drug reactions (ADRs) are common in patients with colorectal cancer. We developed four machine learning models to predict chemotherapy-induced ADRs and assessed the performance. These models leverage high-dime...
Studies in health technology and informatics
Aug 7, 2025
In this study, we combined automatically generated labels from large language models (LLMs) with a small number of manual annotations to classify adverse event-related treatment discontinuations in Japanese EHRs. By fine-tuning JMedRoBERTa and T5 on ...
Studies in health technology and informatics
Aug 7, 2025
Extracting nuanced adverse drug reactions (ADRs) from patient self-reported messages using is pivotal but challenging, particularly given HIPAA constraints. We investigate locally deployable small LLMs-Mistral-7B, Llama-3-8B, and Gemma-7B-for ADR ext...
Studies in health technology and informatics
Aug 7, 2025
This study assessed the effectiveness of natural language processing (NLP) in detecting adverse events (AEs) from anticancer agents by analyzing data from over 39,000 cancer patients. A specialized machine learning model identified known AEs from ant...
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