Authors
Rajvir Kaur, Jeewani Anupama Ginige, Oliver Obst
Published in
BMC medical informatics and decision making. Volume 26. Issue 1. Jul 24, 2026. Epub Jul 24, 2026.
Abstract
Clinical codes assigned from discharge summaries support clinical research, reimbursement, health resource allocation, and service planning. In Australia, ICD-10-AM and ACHI are used to code diagnoses and interventions for admitted episodes of care, but manual coding is time-consuming, labour-intensive, and subject to variability. This study investigates large language models for automated ICD-10-AM and ACHI code assignment from discharge summaries. We first normalised a public ICD-9-CM coded dataset into the Australian coding system using General Equivalence Mappings and semantic similarity over code descriptions, with validation by a domain expert clinical coder. We then fine-tuned Meta-Llama-3-8B and compared full-parameter fine-tuning with a parameter-efficient QLoRA baseline. Finally, we applied Direct Preference Optimisation (DPO) using gold-standard-guided preference pairs, where the gold-standard ICD-10-AM/ACHI code list was treated as the preferred response and the supervised fine-tuned model output as the non-preferred response. Experiments were conducted on a mapped MIMIC-III subset and a de-identified Australian hospital dataset. Among the evaluated model variants, the DPO-aligned full-parameter model achieved the highest score, with micro-F1 scores of 0.7397 on the mapped dataset and 0.7834 on the Australian hospital dataset. These findings suggest that gold-standard-guided DPO can improve agreement with reference code sets compared with supervised fine-tuning alone. However, the results are limited to high-frequency diagnosis and procedure codes, short-document evaluation settings, and DPO preference pairs reviewed by one annotator.
PMID:
42902234
Bibliographic data and abstract were imported from PubMed on 21 Oct 2026.
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