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

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.

Rumour detection using deep learning and filter-wrapper feature selection in benchmark twitter dataset.

Microblogs have become a customary news media source in recent times. But as synthetic text or scale up the online disinformation operation, unsubstantiated pieces of information on social media platforms can cause significant havoc by misleading people. It is essential to develop models that can detect rumours and curtail its cascading effect and virality.