LGBM Beats LLMs in Discharge Prediction
💡Traditional ML outperforms fine-tuned LLMs in clinical tasks—key for efficient healthcare AI
⚡ 30-Second TL;DR
What Changed
Compared 13 models: TF-IDF/XGBoost/LGBM vs. DistilGPT-2, Bio_ClinicalBERT fine-tuned with LoRA.
Why It Matters
Challenges LLM dominance in healthcare AI, promoting simpler models for resource-limited settings. Enables faster, cheaper deployment in hospitals with imbalanced data.
What To Do Next
Benchmark TF-IDF + LGBM against LoRA-tuned LLMs on your imbalanced clinical datasets.
Key Points
- •Compared 13 models: TF-IDF/XGBoost/LGBM vs. DistilGPT-2, Bio_ClinicalBERT fine-tuned with LoRA.
- •LGBM + TF-IDF tops with F1 0.47, recall 0.51, AUC-ROC 0.80 for discharge class.
- •LoRA boosted DistilGPT-2 recall but transformers/generatives underperformed overall.
- •Suggests traditional ML for real-world imbalanced clinical prediction.
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Original source: ArXiv AI ↗
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