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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2506.14101 |
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| _version_ | 1866918061201686528 |
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| author | Landes, Paul Rao, Sitara Chaise, Aaron Jeremy Di Eugenio, Barbara |
| author_facet | Landes, Paul Rao, Sitara Chaise, Aaron Jeremy Di Eugenio, Barbara |
| contents | The Achilles heel of Large Language Models (LLMs) is hallucination, which has drastic consequences for the clinical domain. This is particularly important with regards to automatically generating discharge summaries (a lengthy medical document that summarizes a hospital in-patient visit). Automatically generating these summaries would free physicians to care for patients and reduce documentation burden. The goal of this work is to discover new methods that combine language-based graphs and deep learning models to address provenance of content and trustworthiness in automatic summarization. Our method shows impressive reliability results on the publicly available Medical Information Mart for Intensive III (MIMIC-III) corpus and clinical notes written by physicians at Anonymous Hospital. rovide our method, generated discharge ary output examples, source code and trained models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14101 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Abstract Meaning Representation for Hospital Discharge Summarization Landes, Paul Rao, Sitara Chaise, Aaron Jeremy Di Eugenio, Barbara Computation and Language The Achilles heel of Large Language Models (LLMs) is hallucination, which has drastic consequences for the clinical domain. This is particularly important with regards to automatically generating discharge summaries (a lengthy medical document that summarizes a hospital in-patient visit). Automatically generating these summaries would free physicians to care for patients and reduce documentation burden. The goal of this work is to discover new methods that combine language-based graphs and deep learning models to address provenance of content and trustworthiness in automatic summarization. Our method shows impressive reliability results on the publicly available Medical Information Mart for Intensive III (MIMIC-III) corpus and clinical notes written by physicians at Anonymous Hospital. rovide our method, generated discharge ary output examples, source code and trained models. |
| title | Abstract Meaning Representation for Hospital Discharge Summarization |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2506.14101 |