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

arXiv: https://arxiv.org/abs/2604.03498 · HTML: https://mlsanity.com/paper/2604.03498/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 10 references that are in the map.
- First posted on arXiv 3 Apr 2026.

## Builds on (most cited first, 5 of 5)

- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [The Rise of Small Language Models in Healthcare: A Comprehensive Survey](https://mlsanity.com/paper/2504.17119/) (2025)
- [Lightweight Transformers for Clinical Natural Language Processing](https://mlsanity.com/paper/2302.04725/) (2023)
- [The Foundational Capabilities of Large Language Models in Predicting Postoperative Risks Using Clinical Notes](https://mlsanity.com/paper/2402.17493/) (2024)
- [Examining Imbalance Effects on Performance and Demographic Fairness of Clinical Language Models](https://mlsanity.com/paper/2412.17803/) (2024)

## Abstract (arXiv)

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.