Aligning Language Models with Clinical Expertise: DPO for Heart Failure Nursing Documentation in Critical Care
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arXiv
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| Format: | Preprint |
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2025
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| author | Fan, Junyi Sun, Li Ashrafi, Negin Alaei, Kamiar Pishgar, Maryam |
| author_facet | Fan, Junyi Sun, Li Ashrafi, Negin Alaei, Kamiar Pishgar, Maryam |
| contents | Nursing documentation in intensive care units (ICUs) provides essential clinical intelligence but often suffers from inconsistent terminology, informal styles, and lack of standardization, challenges that are particularly critical in heart failure care. This study applies Direct Preference Optimization (DPO) to adapt Mistral-7B, a locally deployable language model, using 8,838 heart failure nursing notes from the MIMIC-III database and 21,210 preference pairs derived from expert-verified GPT outputs, model generations, and original notes. Evaluation across BLEU, ROUGE, BERTScore, Perplexity, and expert qualitative assessments demonstrates that DPO markedly enhances documentation quality. Specifically, BLEU increased by 84% (0.173 to 0.318), BERTScore improved by 7.6% (0.828 to 0.891), and expert ratings rose across accuracy (+14.4 points), completeness (+14.5 points), logical consistency (+14.1 points), readability (+11.1 points), and structural clarity (+6.0 points). These results indicate that DPO can align lightweight clinical language models with expert standards, supporting privacy-preserving, AI-assisted documentation within electronic health record systems to reduce administrative burden and improve ICU patient safety. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_05410 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Aligning Language Models with Clinical Expertise: DPO for Heart Failure Nursing Documentation in Critical Care Fan, Junyi Sun, Li Ashrafi, Negin Alaei, Kamiar Pishgar, Maryam Computation and Language Machine Learning Nursing documentation in intensive care units (ICUs) provides essential clinical intelligence but often suffers from inconsistent terminology, informal styles, and lack of standardization, challenges that are particularly critical in heart failure care. This study applies Direct Preference Optimization (DPO) to adapt Mistral-7B, a locally deployable language model, using 8,838 heart failure nursing notes from the MIMIC-III database and 21,210 preference pairs derived from expert-verified GPT outputs, model generations, and original notes. Evaluation across BLEU, ROUGE, BERTScore, Perplexity, and expert qualitative assessments demonstrates that DPO markedly enhances documentation quality. Specifically, BLEU increased by 84% (0.173 to 0.318), BERTScore improved by 7.6% (0.828 to 0.891), and expert ratings rose across accuracy (+14.4 points), completeness (+14.5 points), logical consistency (+14.1 points), readability (+11.1 points), and structural clarity (+6.0 points). These results indicate that DPO can align lightweight clinical language models with expert standards, supporting privacy-preserving, AI-assisted documentation within electronic health record systems to reduce administrative burden and improve ICU patient safety. |
| title | Aligning Language Models with Clinical Expertise: DPO for Heart Failure Nursing Documentation in Critical Care |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2510.05410 |