Early prediction of colorectal adenoma risk: leveraging large-language model for clinical electronic medical record data.

Objective: To develop a non-invasive, radiation-free model for early colorectal adenoma prediction using clinical electronic medical record (EMR) data, addressing limitations in current diagnostic approaches for large-scale screening. Design: Retrospective analysis utilized 92,681 cases with EMR, spanning from 2012 to 2022, as the training cohort. Testing was performed on an independent test cohort of 19,265 cases from 2023. Several classical machine learning algorithms were applied in combination with the BGE-M3 large-language model (LLM) for enhanced semantic feature extraction. Area under the receiver operating characteristic curve (AUC) is the major metric for evaluating model performance. The Shapley additive explanations (SHAP) method was employed to identify the most influential risk factors. Results: XGBoost algorithm, integrated with BGE-M3, demonstrated superior performance (AUC = 0.9847) in the validation cohort. Notably, when applied to the independent test cohort, XGBoost maintained its strong predictive ability with an AUC of 0.9839 and an average advance prediction time of 6.88 hours, underscoring the effectiveness of the BGE-M3 model. The SHAP analysis further identified 16 high-impact risk factors, highlighting the interplay of genetic, lifestyle, and environmental influences on colorectal adenoma risk. Conclusion: This study developed a robust machine learning-based model for colorectal adenoma risk prediction, leveraging clinical EMR and LLM. The proposed model demonstrates high predictive accuracy and has the potential to enhance early detection, making it well-suited for large-scale screening programs. By facilitating early identification of individuals at risk, this approach may contribute to reducing the incidence and mortality associated with colorectal cancer.