DRG-LLaMA : tuning LLaMA model to predict diagnosis-related group for hospitalized patients

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Abstract
In the U.S. inpatient payment system, the Diagnosis-Related Group (DRG) is pivotal, but its assignment process is inefficient. The study introduces , an advanced large language model (LLM) fine-tuned on clinical notes to enhance DRGs assignment. Utilizing LLaMA as the foundational model and optimizing it through Low-Rank Adaptation (LoRA) on 236,192 MIMIC-IV discharge summaries, our -7B model exhibited a noteworthy macro-averaged F1 score of 0.327, a top-1 prediction accuracy of 52.0%, and a macro-averaged Area Under the Curve (AUC) of 0.986, with a maximum input token length of 512. This model surpassed the performance of prior leading models in DRG prediction, showing a relative improvement of 40.3% and 35.7% in macro-averaged F1 score compared to ClinicalBERT and CAML, respectively. Applied to base DRG and complication or comorbidity (CC)/major complication or comorbidity (MCC) prediction, achieved a top-1 prediction accuracy of 67.8% and 67.5%, respectively. Additionally, our findings indicate that ’s performance correlates with increased model parameters and input context lengths.
Funding Information
  • National Science Foundation (IIS-2034479)
  • National Science Foundation (SCH-2014438)

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