ArcTEX—a novel clinical data enrichment pipeline to support real-world evidence oncology studies

Journal article

Data stored within electronic health records (EHRs) offer a valuable source of information for real-world evidence (RWE) studies in oncology. However, many key clinical features are only available within unstructured notes. We present ArcTEX, a novel data enrichment pipeline developed to extract oncological features from NHS unstructured clinical notes with high accuracy, even in resource-constrained environments where availability of GPUs might be limited. By design, the predicted outcomes of ArcTEX are free of patient-identifiable information, making this pipeline ideally suited for use in Trust environments. We compare our pipeline to existing discriminative and generative models, demonstrating its superiority over approaches such as Llama3/3.1/3.2 and other BERT based models, with a mean accuracy of 98.67% for several essential clinical features in endometrial and breast cancer. Additionally, we show that as few as 50 annotated training examples are needed to adapt the model to a different oncology area, such as lung cancer, with a different set of priority clinical features, achieving a comparable mean accuracy of 95% on average.

Authors: Tait, K; Cronin, J; Wiper, O; Wallis, J; Davies, J

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Frontiers Media
• Journal: Frontiers in Digital Health
• Volume: 7
• Article number: 1561358
• Publication date: 2025-05-09
• Acceptance date: 2025-04-23
• DOI: 10.3389/fdgth.2025.1561358
• EISSN: 2673-253X
• Language: English
• Keywords: natural language processing; real-world data; oncology; data enrichment; electronic health records