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抓获类型学和发性区域之间的映射的知识图表表示

Shihao Yang¹, Zirui Wen², Wenxin Zhan¹

¹Department of Systems Engineering, Stevens Institute of Technology, Hoboken, NJ, 07030, USA.

Scientific reports

|January 10, 2026

概括

一个新的计算框架使用知识图来系统地分析症状及其与发性区域 (EZ) 的联系. 这种数据驱动的方法为手术规划提供了客观的分析,显示了合理的临床准确性.

科学领域:

神经科学是一个神经科学.
计算生物学计算生物学
医疗信息学医疗信息学

背景情况:

在手术中,精确地定位发性区域 (EZ) 是至关重要的,但往往受到形形象学的主观解释的限制.
在分析症状方面,观察者之间的变异性阻碍了可靠的EZ识别.

研究的目的:

开发和评估一个计算框架,用于系统地分析半学-发性区域 (EZ) 关系.
创建一个客观的,以数据为导向的工具,以帮助EZ定位用于手术规划.

主要方法:

从852个临床病例中构建一个语义-EZ知识图,使用GPT-4o进行自动化术语提取.
应用统计建模 (高斯混合建模,贝叶斯推理) 来量化符号学-EZ关联.
开发SeeKr查询平台,用于EZ本地化预测和专家病学家的评估.

主要成果:

该框架证明了一般的临床可信性,平均正确度得分为2 (在1-4级).
大多数半学到大脑区域映射被专家评为"可能同意".
轻微的不准确性包括多焦点发作中的部分识别和关联强度的轻微错误分类.

结论:

这项研究提出了第一个基于知识图的系统分析,对象学-EZ关系进行分析.
开发的框架提供了一种基于数据的客观方法,用于具有合理临床准确性的半学分析.
该方法显示出作为外科规划的补充诊断工具的潜力,等待进一步的临床验证.

关键词:

是一种病.知识图表知识图表发作发作区是发作开始的区域.发作的隐义学发作的隐义学

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