NLPADADE: Leveraging Natural Language Processing for Automated Detection of Adverse Drug Effects

Pharmacovigilance is a systematic and scientifically rigorous discipline that assumes responsibility for the safety of pharmaceuticals, with its primary objective being the mitigation of risks while optimizing the benefits associated with medication usage. This mission-critical undertaking plays an indispensable role in preserving public health. At its core, pharmacovigilance entails the methodical collection and proficient management of data pertaining to medication safety. Additionally, these activities encompass the vigilant scrutiny of data to detect emerging "signals" indicative of new or evolving safety concerns. The expert evaluation of this data facilitates well-informed decision-making regarding matters of drug safety. Furthermore, proactive risk management strategies are deployed to effectively mitigate potential associated risks. In the pursuit of proactive health protection, regulatory actions are swiftly executed. Concurrently, the World Health Organization (WHO) underscores the global importance of establishing a robust pharmacovigilance framework. It advocates for the establishment of a comprehensive pharmacovigilance system, defined as encompassing "the science and activities related to the detection, assessment, understanding, and prevention of adverse effects or any other problem related to drugs or any other healthcare product." In this dynamic landscape, a pivotal question arises: "How can the automated identification and extraction of references to diseases, medications, and adverse effects from clinical notes and biomedical literature be achieved?" Central to this discourse are adverse drug effects (ADEs), which present a formidable public health challenge, manifesting as a significant source of patient morbidity and mortality. To expedite the utilization of real-world data (RWD) for the enhancement of pharmacovigilance practices, our focus has gravitated towards the development of a high-performance natural language processing (NLP) model. This model aims to facilitate the rapid detection of potential ADEs linked to medications. Our innovative system, designed for the extraction of diseases and ADEs, leverages the synergy of an open-source NLP component system. The pinnacle of our achievement is the model obtained, which boasts a remarkable Score of 0.97 at step 1800. With a Precision of 1.00, a Recall of 0.9412, and an F-Score of 0.9697, our NLP model showcases its efficiency in extracting and identifying pertinent information from textual data. These results underscore the effectiveness of our approach in recognizing diseases and adverse effects related to medications, setting a new benchmark for pharmacovigilance practices.