Journal of the American Medical Informatics Association : JAMIA
Mar 1, 2021
OBJECTIVE: We sought to demonstrate the feasibility of utilizing deep learning models to extract safety signals related to the use of dietary supplements (DSs) in clinical text.
MOTIVATION: Adverse drug reaction (ADR) or drug side effect studies play a crucial role in drug discovery. Recently, with the rapid increase of both clinical and non-clinical data, machine learning methods have emerged as prominent tools to support a...
Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan
Jan 1, 2021
Industrial reforms utilizing artificial intelligence (AI) have advanced remarkably in recent years. The application of AI to big data analysis in the medical information field has also been advancing and is expected to be used to find drug adverse ef...
Drug-drug interactions may occur when combining two or more drugs may cause some adverse events such as cardiotoxicity, central neurotoxicity, hepatotoxicity, etc. However, a large number of researchers who are proficient in pharmacokinetics and phar...
Drug-drug interactions (DDIs) are crucial for drug research and pharmacovigilance. These interactions may cause adverse drug effects that threaten public health and patient safety. Therefore, the DDIs extraction from biomedical literature has been wi...
Journal of the American Medical Informatics Association : JAMIA
Feb 1, 2020
OBJECTIVE: Real-world data (RWD) are increasingly used for pharmacoepidemiology and regulatory innovation. Our objective was to compare adverse drug event (ADE) rates determined from two RWD sources, electronic health records and administrative claim...
Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan
Jan 1, 2020
Toxicity testing is critical for new drug and chemical development process. A clinical study, experimental animal models, and in vitro study are performed to evaluate the safety of a new drug. The limitations of these methods include extensive time f...