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

Computer Science > Artificial Intelligence [Submitted on 3 Apr 2026] Resource-Conscious Modeling for Next- Day Discharge Prediction Using Clinical Notes Ha Na Cho, Sairam Sutari, Alexander Lopez, Hansen Bow, Kai Zheng 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. Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2604.03498 [cs.AI]   (or arXiv:2604.03498v1 [cs.AI] for this version)   https://doi.org/10.48550/arXiv.2604.03498 Focus to learn more Submission history From: Ha Na Cho [view email] [v1] Fri, 3 Apr 2026 22:42:50 UTC (373 KB) Access Paper: view license Current browse context: cs.AI < prev   |   next > new | recent | 2026-04 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)