Mislabeled learning for psychiatric disorder detection

Mislabeled learning for high-dimensional data is essentially important in AI health and relevant fields but rarely investigated in machine learning. In this study, we address the challenge by proposing a novel mislabeled learning algorithm for high-dimensional data: psychiatric map diagnosis and applying it to solve a long-time bipolar disorder and schizophrenia misdiagnosis in psychiatry. The proposed algorithm converts each input high-dimensional SNP sample into a corresponding 2D characteristic image called a psychiatric map through feature self-organizing learning. It can automatically detect mislabeled observations and relabel them with the most likely ground truth before reproducible machine learning besides providing informative visualization for mislabeling detection. Our method attains more accurate and reproducible psychiatry diagnoses, besides discovering latent psychiatry subtypes not reported before. It works well for those datasets with a limited number of samples and achieves leading advantages over the deep learning peers. This study also presents new insight into the pathology of psychiatric disorders by constructing the devolution path of psychiatric states via relative entropy analysis that discloses latent internal transfer and devolution road maps between different psychiatric states. To the best of our knowledge, it is the first study to solve mislabeled learning for high-dimensional data and will inspire more future work in this field.