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In the U.S. inpatient payment system, the Diagnosis-Related Group (DRG) ispivotal, but its assignment process is inefficient. The study introducesDRG-LLaMA, an advanced large language model (LLM) fine-tuned on clinical notesto enhance DRGs assignment. Utilizing LLaMA as the foundational model andoptimizing it through Low-Rank Adaptation (LoRA) on 236,192 MIMIC-IV dischargesummaries, our DRG-LLaMA-7B model exhibited a noteworthy macro-averaged F1score of 0.327, a top-1 prediction accuracy of 52.0%, and a macro-averaged AreaUnder the Curve (AUC) of 0.986, with a maximum input token length of 512. Thismodel surpassed the performance of prior leading models in DRG prediction,showing a relative improvement of 40.3% and 35.7% in macro-averaged F1 scorecompared to ClinicalBERT and CAML, respectively. Applied to base DRG andcomplication or comorbidity (CC)/major complication or comorbidity (MCC)prediction, DRG-LLaMA achieved a top-1 prediction accuracy of 67.8% and 67.5%,respectively. Additionally, our findings indicate that DRG-LLaMA's performancecorrelates with increased model parameters and input context lengths.

DRG-LLaMA : Tuning LLaMA Model to Predict Diagnosis-related Group for Hospitalized Patients