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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2505.08704 |
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| _version_ | 1866908377887539200 |
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| author | Islam, K M Sajjadul Nipu, Ayesha Siddika Wu, Jiawei Madiraju, Praveen |
| author_facet | Islam, K M Sajjadul Nipu, Ayesha Siddika Wu, Jiawei Madiraju, Praveen |
| contents | Electronic Health Records (EHRs) are digital records of patient information, often containing unstructured clinical text. Named Entity Recognition (NER) is essential in EHRs for extracting key medical entities like problems, tests, and treatments to support downstream clinical applications. This paper explores prompt-based medical entity recognition using large language models (LLMs), specifically GPT-4o and DeepSeek-R1, guided by various prompt engineering techniques, including zero-shot, few-shot, and an ensemble approach. Among all strategies, GPT-4o with prompt ensemble achieved the highest classification performance with an F1-score of 0.95 and recall of 0.98, outperforming DeepSeek-R1 on the task. The ensemble method improved reliability by aggregating outputs through embedding-based similarity and majority voting. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_08704 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LLM-based Prompt Ensemble for Reliable Medical Entity Recognition from EHRs Islam, K M Sajjadul Nipu, Ayesha Siddika Wu, Jiawei Madiraju, Praveen Artificial Intelligence Computation and Language Electronic Health Records (EHRs) are digital records of patient information, often containing unstructured clinical text. Named Entity Recognition (NER) is essential in EHRs for extracting key medical entities like problems, tests, and treatments to support downstream clinical applications. This paper explores prompt-based medical entity recognition using large language models (LLMs), specifically GPT-4o and DeepSeek-R1, guided by various prompt engineering techniques, including zero-shot, few-shot, and an ensemble approach. Among all strategies, GPT-4o with prompt ensemble achieved the highest classification performance with an F1-score of 0.95 and recall of 0.98, outperforming DeepSeek-R1 on the task. The ensemble method improved reliability by aggregating outputs through embedding-based similarity and majority voting. |
| title | LLM-based Prompt Ensemble for Reliable Medical Entity Recognition from EHRs |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2505.08704 |