Large Language Models for Heterogeneous Data Mining in Liver Disease: Framework Development and Retrospective Validation Study

Abstract

Abstract Background Differentiating among liver disease entities such as autoimmune liver disease (AILD), drug-induced liver injury (DILI), and chronic hepatitis B (CHB) remains clinically challenging due to overlapping clinical manifestations and nonspecific laboratory findings. Conventional machine learning (ML) approaches rely mainly on structured laboratory data, whereas free-text clinical reports and other heterogeneous electronic medical record data are often underused. Large language models (LLMs) may provide a strategy for encoding heterogeneous clinical information, yet their usefulness for liver disease classification remains insufficiently evaluated. Objective This study aimed to evaluate the usefulness of LLM-derived embeddings for clinical data mining in liver disease and to determine whether integrating these embeddings with laboratory variables improves classification across broad disease categories and closely related subtypes. Methods We retrospectively analyzed electronic medical record data from 7543 patients with nonoverlapping liver disease etiologies treated at Beijing Youan Hospital, Capital Medical University, between 2010 and 2025. Three LLMs (Qwen3, Huatuo-o1, and II-Medical) generated semantic embeddings from standardized clinical text, combining free-text examination reports, and structured clinical observations. Performance was assessed in a 3-class etiological task (AILD, DILI, and CHB) and a 4-class task further subclassifying AILD into autoimmune hepatitis and primary biliary cholangitis. We compared embedding-only models, LLM-integrated ML models, and an ML-only baseline using the same structured variable set and preprocessing pipeline, with lightweight natural language processing encoders and zero-shot LLM reasoning as additional comparators. Models were developed using 5-fold cross-validation and evaluated on an internal holdout set using accuracy, macroaveraged precision, recall, and F1-score. Results In the 3-class task, the LLM-integrated ML models achieved macro F1-scores of 0.835‐0.837, compared with 0.791 for the ML-only baseline, with corresponding accuracies of 0.925‐0.929 versus 0.893. In the 4-class task, the LLM-integrated ML models achieved macro F1-scores of 0.717‐0.734, compared with 0.665 for the ML-only baseline, with corresponding accuracies of 0.920‐0.922 versus 0.874. A temporal split sensitivity analysis using cases from 2010 to 2019 for training and cases from 2020 to 2025 for testing showed that the relative advantage of LLM-integrated ML models over the ML-only baseline was preserved. Direct zero-shot LLM reasoning and lightweight natural language processing encoders performed below the embedding-based integrated models. Conclusions In this single-center retrospective cohort of patients with clear-cut, nonoverlapping liver disease etiologies, LLM-derived embeddings provided complementary information to structured laboratory variables for multiclass liver disease classification. The integrated framework showed improved internal validation performance compared with the ML-only model, particularly for non-CHB categories and fine-grained subtype discrimination. Because patients with overlapping liver disease etiologies were excluded, the reported performance may overestimate diagnostic accuracy in broader real-world clinical settings where overlapping syndromes are common. Multicenter external validation and prospective evaluation in more heterogeneous patient populations are needed before clinical implementation.