Classification of kinetic-related injury in hospital triage data using NLP
This study addresses the classification of kinetic-energy-related injuries in hospital triage texts, tackling three key challenges: data privacy sensitivity, high annotation costs, and constrained edge-computing resources. We propose a lightweight two-stage fine-tuning paradigm: first, preliminary fine-tuning of a pre-trained large language model on 2K open-source samples using GPU; second, efficient secondary adaptation on one thousand anonymized hospital records executed entirely on CPU. This approach eliminates reliance on high-end hardware or extensive expert annotations, substantially lowering deployment barriers. Experimental results demonstrate that the model maintains high classification accuracy under low-resource conditions while ensuring patient data privacy, computational efficiency, and clinical applicability. The method provides a practical, deployable solution for intelligent triage in resource-limited primary healthcare settings.