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不透明的本体学:英国生物银行ICD-10诊断组的神经成像分类

Ty Easley¹, Xiaoke Luo¹, Kayla Hannon¹

¹Department of Radiology, Washington University School of Medicine, Saint Louis, Missouri 63110, USA.

|February 11, 2025

概括

机器学习模型很难使用神经成像或社会人口统计数据准确地分类诊断群体. 当前的疾病分类系统在用作基于大脑的预测研究的目标时需要谨慎使用.

科学领域:

神经成像和机器学习
计算精神病学是一种计算精神病学.
医疗信息学医疗信息学

背景情况:

使用神经成像进行病例控制分类的机器学习是常见的.
这些分类的跨诊断性比较缺乏.
这种比较对于评估模型特异性和诊断本体学价值至关重要.

研究的目的:

调查17个国际疾病分类第十次修订 (ICD-10) 诊断组的病例控制分类的准确性.
为了比较使用神经成像与社会人口统计特征的分类性能.
建立诊断分类准确性的基准.

主要方法:

利用了17个ICD-10诊断组 (精神,行为,神经系统疾病) 的英国生物库数据.
使用结构/功能MRI或社会人口统计特征进行训练的随机森林分类模型.
与年龄分类和替代模型 (KNN,线性SVM) 相比的基准诊断分类.

主要成果:

很少有ICD-10诊断组被分类得明显高于偶然.
脱线性疾病 (结构性神经影像) 和抑郁症 (社会人口学/功能性神经影像) 显示了一些分类准确性.
年龄分类的准确性在所有特征集中都很高,与诊断分类不同.

结论:

研究结果表明,当前的疾病分类系统面临着挑战.
在基于大脑的预测研究中使用ICD-10诊断组作为目标时建议谨慎.
强调需要在神经成像研究中改进诊断框架.

关键词:

英国生物银行机器学习是机器学习.心理健康障碍精神健康障碍神经系统疾病神经系统疾病神经成像是一种神经成像.

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