LLM-based reconstruction of longitudinal clinical trajectories in chronic liver disease

Abstract Liver cancer primarily develops in patients with chronic liver disease (CLD), yet most cases are diagnosed at advanced stages with poor prognosis. While CLD surveillance generates extensive longitudinal data, its free-text nature hinders large-scale research. To address this, we developed a scalable framework using open-source LLMs with constrained decoding to process unstructured text across radiology, pathology, and transplant assessment domains. A calibration set comprising 507 reports from 30 patients was manually annotated to benchmark four LLMs against a regular expression baseline across 70 tasks. Llama-3.3-70B performed best, exceeding 90% accuracy on 59/70 tasks, outperforming Llama-3.1-8B (a smaller variant), OpenBioLLM-70B (a medically fine-tuned model), and DeepSeek-R1-8B. Constrained decoding achieved >99.9% format adherence, far surpassing unconstrained prompting (87.4%). Applied to the full cohort, the pipeline analysed 22,493 reports to generate a patient-level database of 29,225 datapoints (35 variables, 835 patients) without manual annotation. Further analysis confirmed known liver cancer risk factors (male sex, viral hepatitis, smoking, diabetes), and allowed for reconstruction of individualised disease timelines. This work provides a scalable blueprint for transforming real-world clinical free-text into structured formats and personalised patient trajectories. Future applications have the potential to accelerate data-driven research into early cancer detection within complex pre-cancerous diseases like CLD.