不透明的本体学:英国生物库中的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.
bioRxiv : the preprint server for biology
|April 25, 2024
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ICD-10诊断组. 这表明当前疾病分类系统对基于大脑的预测研究存在局限性.
科学领域:
- 神经科学是一个神经科学.
- 医疗信息学 医疗信息学
- 机器学习 机器学习
背景情况:
- 使用神经成像对诊断病例进行分类的机器学习是常见的.
- 这些分类的跨诊断性比较缺乏,阻碍了特异性评估和基准测试.
- 了解分类模型的特异性对于评估国际疾病分类第10版 (ICD-10) 等诊断本体学至关重要.
研究的目的:
- 通过神经成像和社会人口统计数据,调查17个ICD-10诊断组的病例控制分类的准确性.
- 与年龄预测和不同的机器学习模型对比分类性能.
- 评估ICD-10诊断组在基于大脑的预测研究中作为目标的实用性.
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特征或社会人口统计数据进行训练的随机森林分类模型.
- 采用严格的混合分割来估计稳定性和准确性,与年龄分类和KNN/线性SVM模型进行比较.
主要成果:
- 在所有特征集中,年龄分类准确度很高.
- 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.
结论:
- 这些发现表明,当前的ICD-10疾病分类系统存在挑战.
- 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.
- 强调需要改善神经成像研究的疾病分类系统.
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