Transformer-Based Natural Language Processing Models for Mining Unstructured Oncology Clinical Notes to Improve Drug Matching

Abstract

Transformer-based Natural Language Processing (NLP) models have revolutionized the extraction of insights from unstructured clinical text, offering significant advancements in precision medicine. This review explores the application of these models in mining oncology clinical notes to enhance drug matching and personalized treatment strategies. Oncology clinical documentation, often characterized by high variability and complexity, poses challenges to traditional data processing methods. However, transformer architectures such as BERT, GPT, and their domain-specific variants have demonstrated exceptional capabilities in understanding context, semantics, and clinical terminologies. We review recent literature highlighting the use of these models in identifying relevant patient characteristics, treatment histories, and biomarkers that influence therapeutic decisions. Special attention is given to the integration of these models into electronic health record (EHR) systems and their role in improving drug recommendation systems. Additionally, we address current limitations, including model interpretability, data privacy, and generalizability across diverse patient populations. The review concludes by outlining future directions for research, emphasizing the potential of transformer-based NLP in driving more accurate and efficient drug matching in oncology care through better utilization of clinical narratives.