mCodeGPT: Bridging the Gap between Unstructured Medical Text and Structured Cancer Ontologies

Abstract

Abstract The rapidly growing collection of clinical free text data about cancer provides an unprecedented opportunity to harness rich, real-world insights for advancing cancer research and treatment. At the same time, there is a formidable challenge in efficiently extracting, standardizing, and structuring this vast and unstructured information to derive meaningful conclusions. In this paper, we address the problem of information extraction to standardize and structure such free text using large language models (LLMs) following the minimal Common Oncology Data Elements (mCODE™) structure. To this end, we implement mCodeGPT, an approach that ingests the cancer ontology knowledgebase and hierarchically generates prompts to extract relevant information from clinical notes. The generated output is subsequently organized and displayed in a tabular and structured format for improved analysis and interpretation. Our approach is annotation-free and eliminates the need for model training. mCodeGPT achieved over 95% accuracy for named entity recognition on mCODE concepts,