Resource-Conscious Modeling for Next- Day Discharge Prediction Using Clinical Notes

• URL: http://arxiv.org/abs/2604.03498v1
• Date: Fri, 03 Apr 2026 22:42:50 GMT
• Title: Resource-Conscious Modeling for Next- Day Discharge Prediction Using Clinical Notes
• Abstract summary: Timely discharge prediction is essential for optimizing bed turnover and resource allocation in elective spine surgery units.<n>This study evaluates the feasibility of lightweight, fine-tuned large language models (LLMs) and traditional text-based models for predicting next-day discharge.
• Score: 39.44014654945035
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Timely discharge prediction is essential for optimizing bed turnover and resource allocation in elective spine surgery units. This study evaluates the feasibility of lightweight, fine-tuned large language models (LLMs) and traditional text-based models for predicting next-day discharge using postoperative clinical notes. We compared 13 models, including TF-IDF with XGBoost and LGBM, and compact LLMs (DistilGPT-2, Bio_ClinicalBERT) fine-tuned via LoRA. TF-IDF with LGBM achieved the best balance, with an F1-score of 0.47 for the discharge class, a recall of 0.51, and the highest AUC-ROC (0.80). While LoRA improved recall in DistilGPT2, overall transformer-based and generative models underperformed. These findings suggest interpretable, resource-efficient models may outperform compact LLMs in real-world, imbalanced clinical prediction tasks.

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