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

Background & AimsLiver cancer primarily develops in patients with chronic liver disease (CLD), yet most cases are diagnosed at an advanced stage with poor prognosis. While clinical surveillance of patients with CLD generates extensive longitudinal data, its unstructured free-text nature hinders large-scale research. To unlock this real-world evidence, we developed a scalable framework using open-source Large Language Models (LLMs) to transform unstructured clinical text into structured data. MethodsWe conducted a multi-stage evaluation of LLM-based extraction from multi-source clinical documentation of liver transplant recipients. A calibration set comprising 507 reports (414 radiology, 65 pathology, and 28 liver transplant assessment reports) from 30 patients was manually annotated to benchmark four open-source LLMs (Llama 3.1 8B, Llama 3.3 70B, Open-BioLLM 70B, DeepSeek R1 8B) against a regular expression baseline across 73 tasks. To ensure structured outputs, we compared constrained decoding (Guidance and Ollama packages) against unconstrained prompting across 5,590 prompt-output pairs. The finalised pipeline was then applied to the full cohort of 835 patients transplanted in our centre over the past decade. ResultsAmong the models tested, Llama 3.3 70B performed best, exceeding 90% accuracy on 59/73 tasks, outperforming both a medically fine-tuned model (OpenBioLLM 70B) and a smaller variant (Llama 3.1 8B). Constrained decoding achieved >99.9% format adherence, far surpassing unconstrained prompting (87.4%). Applied to the full cohort, the pipeline successfully analysed 22,493 reports to generate 37,125 datapoints (45 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 longitudinal disease timelines. ConclusionsThis work provides a scalable blueprint for transforming real-world clinical free-text into structured formats, paving the way for accelerated, data-driven research into complex pre-cancerous diseases like CLD.