Towards Visualizing Electronic Medical Records via Natural Language Queries
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912431767289856 |
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| author | Zhang, Haodi Ning, Siqi Zheng, Qiyong Nie, Jinyin Zhang, Liangjie Wang, Weicheng Song, Yuanfeng |
| author_facet | Zhang, Haodi Ning, Siqi Zheng, Qiyong Nie, Jinyin Zhang, Liangjie Wang, Weicheng Song, Yuanfeng |
| contents | Electronic medical records (EMRs) contain essential data for patient care and clinical research. With the diversity of structured and unstructured data in EHR, data visualization is an invaluable tool for managing and explaining these complexities. However, the scarcity of relevant medical visualization data and the high cost of manual annotation required to develop such datasets pose significant challenges to advancing medical visualization techniques. To address this issue, we propose an innovative approach using large language models (LLMs) for generating visualization data without labor-intensive manual annotation. We introduce a new pipeline for building text-to-visualization benchmarks suitable for EMRs, enabling users to visualize EMR statistics through natural language queries (NLQs). The dataset presented in this paper primarily consists of paired text medical records, NLQs, and corresponding visualizations, forming the first large-scale text-to-visual dataset for electronic medical record information called MedicalVis with 35,374 examples. Additionally, we introduce an LLM-based approach called MedCodeT5, showcasing its viability in generating EMR visualizations from NLQs, outperforming various strong text-to-visualization baselines. Our work facilitates standardized evaluation of EMR visualization methods while providing researchers with tools to advance this influential field of application. In a nutshell, this study and dataset have the potential to promote advancements in eliciting medical insights through visualization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12837 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards Visualizing Electronic Medical Records via Natural Language Queries Zhang, Haodi Ning, Siqi Zheng, Qiyong Nie, Jinyin Zhang, Liangjie Wang, Weicheng Song, Yuanfeng Databases Electronic medical records (EMRs) contain essential data for patient care and clinical research. With the diversity of structured and unstructured data in EHR, data visualization is an invaluable tool for managing and explaining these complexities. However, the scarcity of relevant medical visualization data and the high cost of manual annotation required to develop such datasets pose significant challenges to advancing medical visualization techniques. To address this issue, we propose an innovative approach using large language models (LLMs) for generating visualization data without labor-intensive manual annotation. We introduce a new pipeline for building text-to-visualization benchmarks suitable for EMRs, enabling users to visualize EMR statistics through natural language queries (NLQs). The dataset presented in this paper primarily consists of paired text medical records, NLQs, and corresponding visualizations, forming the first large-scale text-to-visual dataset for electronic medical record information called MedicalVis with 35,374 examples. Additionally, we introduce an LLM-based approach called MedCodeT5, showcasing its viability in generating EMR visualizations from NLQs, outperforming various strong text-to-visualization baselines. Our work facilitates standardized evaluation of EMR visualization methods while providing researchers with tools to advance this influential field of application. In a nutshell, this study and dataset have the potential to promote advancements in eliciting medical insights through visualization. |
| title | Towards Visualizing Electronic Medical Records via Natural Language Queries |
| topic | Databases |
| url | https://arxiv.org/abs/2506.12837 |