Addressing Inaccurate Nosology in Mental Health: A Multilabel Data Cleansing Approach for Detecting Label Noise From Structural Magnetic Resonance Imaging Data in Mood and Psychosis Disorders.
Addressing Inaccurate Nosology in Mental Health: A Multilabel Data Cleansing Approach for Detecting Label Noise From Structural Magnetic Resonance Imaging Data in Mood and Psychosis Disorders.
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DOI：
10.1016/j.bpsc.2020.05.008
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发表时间：
2020-08
期刊：
影响因子：
--
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Rokham H;Pearlson G;Abrol A;Falakshahi H;Plis S;Calhoun VD
心理健康诊断方法正在寻求识别生物标志物，以与先进的机器学习方法一起工作。当传统的诊断标签本身不一定有效时，很难确定疾病的生物标志物。为了开始解决这个问题，我们使用了从1400多名患有情绪和精神障碍的个体中收集的脑成像数据，这些个体包括健康对照组、精神病患者及其未受影响的一级亲属，我们假设在诊断标签过程中可能存在噪音。我们通过使用支持向量机分类器对数据进行多次分类来检测标签噪声，然后保留所有分类器一致错误标记这些受试者的个体。接下来，我们根据生物学数据，使用迭代数据清理方法为这些个体分配了一个新的诊断标签。仿真结果表明，该方法对标签噪声的识别具有较高的准确性。我们通过深度学习模型评估了我们的方法，该模型显示了模型在清洁数据集上的性能改进。诊断和生物型类别都显示出很大比例的噪声标签，其中最大数量的重新标记发生在健康对照和双相情感障碍和精神分裂症患者以及未受影响的近亲之间。提取的成像特征突出了与每组相关的区域脑变化。总之，这种方法代表了开发方法的第一步，这些方法不需要假设现有的心理健康诊断类别总是有效的，而是允许我们利用这些信息，同时也承认存在错误分配。
Mental health diagnostic approaches are seeking to identify biological markers to work alongside of advanced machine learning approaches. It is difficult to identify a biological marker of disease when the traditional diagnostic labels themselves are not necessarily valid. To begin to address this, we worked with brain imaging data collected from individuals with mood and psychosis disorders from over 1400 individuals comprising healthy controls, psychosis patients and their unaffected first-degree relatives and we assumed there may be noise in the diagnostic labelling process. We detected label noise by classifying the data multiple times using a support vector machine classifier and then we retained those individuals in which all classifiers unanimously mislabeled those subjects. Next we assigned a new diagnostic label to these individuals, based on the biological data, using an iterative data cleansing approach. Simulation results showed our method was highly accurate in identifying label noise. We evaluated our method via a deep learning model which shows performance improvement of model on the cleansed dataset. Both diagnostic and Biotype categories showed a large percentage of noisy labels with the largest amount of relabeling occurring between the healthy control and bipolar and schizophrenia disorder individuals as well as in the unaffected close relatives. Extraction of imaging features highlighted regional brain changes associated with each group. In sum, this approach represents an initial step towards developing approaches that need not assume existing mental health diagnostic categories are always valid, but rather allows us to leverage this information while also acknowledging that there are mis-assignments.
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