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充分利用错误:利用神经成像数据的机器学习模型产生的错误分类来捕捉疾病异质性
Sarah M Olshan1, Corey J Richier1, Kyle A Baacke2
1Department of Psychology, University of Illinois Urbana-Champaign.
Journal of psychopathology and clinical science
|October 31, 2024
概括
分析精神分裂症 (SCZ),双相情感障碍 (BD) 和注意力缺陷/多动性障碍 (ADHD) 的诊断错误,揭示了共同的神经生物学特征. 分类错误突出了导致异质性和疾病重叠的临床和神经模式.
科学领域:
- 神经科学是一个神经科学.
- 精神病学是一个精神病学.
- 计算精神病学是一种计算精神病学.
- 机器学习在医学中的应用
背景情况:
- 精神疾病中的异质性使神经生物学绘制诊断类别变得复杂.
- 通常同时出现的疾病表现出重叠的症状和潜在的共同的神经支.
- 了解错误分类模式可以阐明跨诊断的共同特征.
研究的目的:
- 在具有重叠特征的疾病中探索诊断分类错误.
- 调查大脑功能连接如何与错误分类模式相关.
- 确定临床症状和特定的大脑网络是否导致错误分类率.
主要方法:
- 使用支持矢量分类器对来自UCLA现象学研究的全脑基于任务的功能连接数据进行了分类.
- 分析了精神分裂症 (SCZ),双相情感障碍 (BD) 和注意力缺陷/多动症障碍 (ADHD) 之间的错误分类模式.
- 研究了临床症状和特定大脑网络对分类错误的贡献.
主要成果:
- 该分类模型的表现明显优于随机 (44%的准确率).
- 在SCZ和BD之间发生了对称的错误分类 (38%和36%).
- 注意缺陷/多动障碍 (ADHD) 最常被错误分类为BD (46%),SCZ和ADHD显示最少的错误分类 (15-22%).
- 临床症状显著解释了SCZ-BD错误分类中的差异.
- 特定的大脑网络对每个疾病的分类准确性产生了不同影响.
结论:
- 分类错误分析有效地复制了已知的疾病重叠,并确定了共享的神经生物学特征.
- 临床和神经特征有助于错误分类和疾病内的异质性.
- 这种方法支持在诊断群体内的神经生物学知情的表型差异化.
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