Enhancing Drug Discovery and Patient Care through Advanced Analytics with The Power of NLP and Machine Learning in Pharmaceutical Data Interpretation

R, Nagalakshmi; Khan, Surbhi Bhatia; kumar, Ananthoju Vijay; T R, Mahesh; Alojail, Mohammad; Sangwan, Saurabh Raj; Saraee, Mo

Abstract

This study delves into the transformative potential of Machine Learning (ML) and Natural Language Processing (NLP) within the pharmaceutical industry, spotlighting their significant impact on enhancing medical research methodologies and optimizing healthcare service delivery. Utilizing a vast dataset sourced from a well-established online pharmacy, this research employs sophisticated ML algorithms and cutting-edge NLP techniques to critically analyze medical descriptions and optimize recommendation systems for drug prescriptions and patient care management. Key technological integrations include BERT embeddings, which provide nuanced contextual understanding of complex medical texts, and cosine similarity measures coupled with TF-IDF vectorization to significantly enhance the precision and reliability of text-based medical recommendations. By meticulously adjusting the cosine similarity thresholds from 0.2 to 0.5, our tailored models have consistently achieved a remarkable accuracy rate of 97%, illustrating their effectiveness in predicting suitable medical treatments and interventions. These results not only highlight the revolutionary capabilities of NLP and ML in harnessing data-driven insights for healthcare but also lay a robust groundwork for future advancements in personalized medicine and bespoke treatment pathways. Comprehensive analysis demonstrates the scalability and adaptability of these technologies in real-world healthcare settings, potentially leading to substantial improvements in patient outcomes and operational efficiencies within the healthcare system.

• Journal Article Type: Article
• Acceptance Date: Dec 22, 2024
• Online Publication Date: Dec 24, 2024
• Deposit Date: Dec 28, 2024
• Publicly Available Date: Jan 3, 2025
• Journal: SLAS Technology
• Peer Reviewed: Peer Reviewed
• Article Number: 100238
• DOI: https://doi.org/10.1016/j.slast.2024.100238