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Mon-Ju, W., et al. (2017) Identification and Individualized Prediction of Clinical Phenotypes in Bipolar Disorders Using Neurocognitive Data, Neuroimage Scans and Machine Learning. Neuroimage, 145, 254-264.
https://doi.org/10.1016/j.neuroimage.2016.02.016

has been cited by the following article:

• TITLE: Bipolar and Schizophrenia Disorders Diagnosis Using Artificial Neural Network

  AUTHORS: Mateus Beck Fonseca, Renan Soares de Andrades, Suelen de Lima Bach, Carolina David Wiener, Jean Pierre Oses

  KEYWORDS: Bipolar Disorder, Schizophrenia Disorder, Biomarkers, Artificial Neural Network

  JOURNAL NAME: Neuroscience and Medicine, Vol.9 No.4, December 27, 2018

  ABSTRACT: Motivation: Bipolar disorder (BD) and schizophrenia (SZ) has a difficult diagnosis, so the main objective of this article is to propose the use of Artificial Neural Networks (ANNs) to classify (diagnose) groups of patients with BD or SZ from a control group using sociodemographic and biochemical variables. Methods: Artificial neural networks are used as classifying tool. The data from this study were obtained from the array collection from Stanley Neuropathology Consortium databank. Inflammatory markers and characteristics of the sampled population were the inputs variables. Results: Our findings suggest that an artificial neural network could be trained with more than 90% accuracy, aiming the classification and diagnosis of bipolar, schizophrenia and control healthy group. Conclusion: Trained ANNs could be used to improve diagnosis in Schizophrenia and Bipolar disorders.