通过大型语言模型增强癌症药物的不良药物事件提取和总结
Sofia Jamil1, Sriparna Saha1, Rajiv Misra1
1Indian Institute of Technology Patna, Bihta, 801106, Bihar, India.
Journal of biomedical informatics
|March 9, 2026
概括
癌症治疗会引起副作用,因此需要更好的不良药物事件 (ADE) 报告. 我们的多任务多模式药监总结框架 (MMPSF) 集成了视觉和文本数据,以准确地总结ADE,提高患者的安全性.
科学领域:
- 药物监督 药物监督 药物监督
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 化学疗法和免疫疗法等癌症疗法虽然有效,但会引起显著的副作用.
- 强大的药物不良事件 (ADE) 报告和总结对于患者安全和治疗优化至关重要.
研究的目的:
- 为癌症患者开发一个多任务多模式药监总结框架 (MMPSF).
- 通过整合视觉和文本信息,提高药物不良事件 (ADE) 摘要的准确性和全面性.
主要方法:
- 提出了MMPSF框架,将图像嵌入到文本潜伏空间中.
- 合并的图像和文本嵌入为多式联运ADE摘要.
- 使用了直接偏好优化 (DPO) 与偏好感知损失函数.
- 介绍了MMCADRS数据集 (1800个叙述,500个图像).
主要成果:
- 现有总结基线的MMPSF表现优于现有的总结基线.
- 主要改进归因于视觉提示集成 (0.20↑),实体提取 (0.190↑) 和DPO (0.267↑).
- 人类评估显示了高临床准确性 (4.18),事实回忆 (0.92) 和低遗漏率 (0.19).
结论:
- MMPSF有效地生成准确的,癌症特异的多式联络ADE摘要.
- 该框架显示了癌症护理中现实世界药物监测的巨大潜力.
- 与印度拉·甘地医学科学研究所合作开发.
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