ArcTEXa novel clinical data enrichment pipeline to support realworld evidence oncology studies

Keiran Tait^1*Joseph Cronin¹Olivia Wiper¹Jamie Wallis¹Jim Davies²Robert Dürichen¹

• ¹Arcturis Data, Oxford, United Kingdom
• ²Department of Computer Science, University of Oxford, Oxford, United Kingdom

Data stored within electronic health records (EHRs) offer a valuable source of information for realworld 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.