珊瑚:专家策划的瘤学报告提前进行语言模型推断
Madhumita Sushil1, Vanessa E Kennedy2, Divneet Mandair2
1Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco.
NEJM AI
|April 21, 2025
概括
大型语言模型 (LLM) 显示出从临床笔记中提取瘤学信息的前景,GPT-4在新的基准数据集中表现最好. 在广泛临床应用之前,复杂的医学推理需要进一步改进.
科学领域:
- 计算瘤学是一种计算瘤学.
- 医学中的自然语言处理 (NLP)
- 医疗保健中的人工智能 (AI)
背景情况:
- 准确了解患者的疾病进展和治疗史在瘤治疗和研究中至关重要.
- 临床笔记包含广泛但非结构化的瘤学信息.
- 在瘤学工作流程中评估大型语言模型 (LLM) 的实用性很重要,但由于缺乏全面的瘤学信息方案和注释数据集,目前存在局限性.
研究的目的:
- 在瘤学临床笔记中评估最近的大型语言模型 (LLM) 的零射击信息提取能力.
- 开发一个精细的,专家标记的数据集和基准来评估瘤学LLM.
- 确定LLM用于提取详细的瘤信息的优点和局限性.
主要方法:
- 一个新的数据集由40个未被识别的乳腺和胰腺癌进展记录组成,经过策划和专家标记.
- 三种LLM (GPT-4,GPT-3.5-turbo,FLAN-UL2) 被评估为对瘤学信息的零射击提取.
- 使用BLEU-4,ROUGE-1和精确匹配 (EM) F1得分指标量化表现.
主要成果:
- 总共有9028个实体,9986个修改器和5312个关系被注释.
- 在所有指标中,GPT-4获得了最高的表现 (平均蓝色:0.73,红色:0.72,EM F1:0.51).
- 尽管GPT-4在提取瘤特征和药物方面表现出色,并且在推断症状和未来药物考虑方面表现出色,尽管存在部分反应和幻觉等一些错误.
结论:
- 开发的图表和基准显示了当前LLM在瘤学信息提取方面的能力和局限性.
- 士学位的表现各不相同,GPT-4表现最有前途,但仍然需要在复杂的医学推理方面进行改进.
- 为了使用LLMs进行可靠的临床研究,人口管理和患者护理文档,需要进一步的进展.
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