aceAutomating Injury Severity Score Calculation From Free-Text Discharge Diagnoses Using Domain-Specific Retrieval-Augmented Large Language Models: Retrospective Validation Study
Background: Trauma registries are crucial for evaluating the prognosis of injuries and the quality of trauma care. However, the calculation of Injury Severity Score (ISS) remains a predominantly manual process, which is labor-intensive, potentially error-prone, and difficult to scale. Automating this process is a critical step toward enhancing the efficiency and quality of trauma registry data. Objective: This study conducts a feasibility test on the use of large language models (LLMs) to automatically calculate…
A domain-specific retriever fine-tuned on trauma ICD-10 codes raised LLM exact-match accuracy for Injury Severity Score calculation, reaching 75.3% with GPT-4.1 versus 70.0% for the ICDPIC-R baseline.
Standard retrieval augmentation gave no consistent gains, and baseline LLMs reached only 47.3% to 64.7% exact-match accuracy, so off-the-shelf models are unreliable for this task.
The study is a single-center retrospective feasibility test, so the findings need confirmation across multiple institutions.
Evidence
- Peer-reviewedJMIR Medical Informatics2026-10-13
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Truvace Impact Record TRV-2026-1356, v1: “Automating Injury Severity Score Calculation From Free-Text Discharge Diagnoses Using Domain-Specific Retrieval-Augmented Large Language Models: Retrospective Validation Study.” Truvace, 2026-10-14. /record/TRV-2026-1356 (accessed at citation time). sha256 d48d5b3ba2edf2f6…
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