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