Kalmady Sunil Vasu, Paul Animesh Kumar, Greiner Russell, Agrawal Rimjhim, Amaresha Anekal C, Shivakumar Venkataram, Narayanaswamy Janardhanan C, Greenshaw Andrew J, Dursun Serdar M, Venkatasubramanian Ganesan
Alberta Machine Intelligence Institute, University of Alberta, Edmonton, AB, Canada.
Canadian VIGOUR Centre, University of Alberta, Edmonton, AB, Canada.
NPJ Schizophr. 2020 Nov 6;6(1):30. doi: 10.1038/s41537-020-00119-y.
Recently, we developed a machine-learning algorithm "EMPaSchiz" that learns, from a training set of schizophrenia patients and healthy individuals, a model that predicts if a novel individual has schizophrenia, based on features extracted from his/her resting-state functional magnetic resonance imaging. In this study, we apply this learned model to first-degree relatives of schizophrenia patients, who were found to not have active psychosis or schizophrenia. We observe that the participants that this model classified as schizophrenia patients had significantly higher "schizotypal personality scores" than those who were not. Further, the "EMPaSchiz probability score" for schizophrenia status was significantly correlated with schizotypal personality score. This demonstrates the potential of machine-learned diagnostic models to predict state-independent vulnerability, even when symptoms do not meet the full criteria for clinical diagnosis.
最近，我们开发了一种机器学习算法“EMPaSchiz”，它从一组精神分裂症患者和健康个体的训练集中学习一个模型，该模型基于从个体静息态功能磁共振成像中提取的特征来预测一个新个体是否患有精神分裂症。在本研究中，我们将这个学习到的模型应用于精神分裂症患者的一级亲属，这些亲属被发现没有活动性精神病或精神分裂症。我们观察到，被该模型分类为精神分裂症患者的参与者的“分裂型人格得分”显著高于未被分类为患者的参与者。此外，精神分裂症状态的“EMPaSchiz概率得分”与分裂型人格得分显著相关。这表明，即使症状不符合临床诊断的全部标准，机器学习诊断模型也有潜力预测与状态无关的易感性。
