Study of the Drug-related Adverse Events with the Help of Electronic Health Records and Natural Language Processing

Surveillance of pharmacovigilance, also known as drug safety surveillance, involves the monitoring and evaluation drug-related adverse events or side effects to ensure safe effective use medications. Pharmacovigilance is an essential component healthcare systems worldwide plays a crucial role in identifying managing concerns. Natural language processing (NLP) can play surveillance activities within pharmacovigilance by analyzing extracting information from various sources, such clinical trial reports, electronic health records, social media, scientific literature. It important note that while NLP be powerful tool it should always used conjunction with human expertise. algorithms assist identification extraction relevant information, but final assessment decision-making involve knowledge judgment trained professionals. In this paper, we intend train test our models using dataset Medication, Indication, Adverse Drug Events challenge. This will include patient notes well entity categories ADE, sorts relationships between these entities. Because ADE-related two-stage process, outcome second step (i.e., relation extraction) utilized compare all models. The analysis records automated approaches considerably increase effectiveness extraction, although depends on methodology, data, other aspects. Our findings help ADE detection research.