CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models

• URL: http://arxiv.org/abs/2509.26136v1
• Date: Tue, 30 Sep 2025 11:56:53 GMT
• Title: CliniBench: A Clinical Outcome Prediction Benchmark for Generative and Encoder-Based Language Models
• Authors: Paul Grundmann, Dennis Fast, Jan Frick, Thomas Steffek, Felix Gers, Wolfgang Nejdl, Alexander Löser,
• Abstract summary: generative large language models (LLMs) are being increasingly investigated for complex medical tasks.<n>Their effectiveness in real-world clinical applications remains underexplored.<n>We present CliniBench, the first benchmark that enables comparisons of encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in MIMIC-IV dataset.
• Score: 39.221038061767324
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: With their growing capabilities, generative large language models (LLMs) are being increasingly investigated for complex medical tasks. However, their effectiveness in real-world clinical applications remains underexplored. To address this, we present CliniBench, the first benchmark that enables comparability of well-studied encoder-based classifiers and generative LLMs for discharge diagnosis prediction from admission notes in MIMIC-IV dataset. Our extensive study compares 12 generative LLMs and 3 encoder-based classifiers and demonstrates that encoder-based classifiers consistently outperform generative models in diagnosis prediction. We assess several retrieval augmentation strategies for in-context learning from similar patients and find that they provide notable performance improvements for generative LLMs.

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