DOI: 10.1101/453951Oct 26, 2018Paper

Highly Predictive Transdiagnostic Features Shared across Schizophrenia, Bipolar Disorder, and ADHD Identified Using a Machine Learning Based Approach

BioRxiv : the Preprint Server for Biology
Yuelu LiuParvez Ahammad


The Diagnostic and Statistical Manual of Mental Disorders (DSM) has been the standard for diagnosing psychiatric disorders in the United States. Yet, evidence has suggested that symptoms in psychiatric disorders are not restricted to the boundaries between DSM categories, implicating an underlying latent transdiagnostic structure of psychopathology. Here, we applied an importance-guided machine learning technique for model selection to item-level data from self-reported instruments contained within the Consortium for Neuropsychiatric Phenomics dataset. From 578 questionnaire items, we identified a set of phenotypic features which consisted of 85 items that were shared across diagnoses of schizophrenia (SCZ), bipolar disorder (BD), and attention deficit/hyperactivity disorder (ADHD). A transdiagnostic classifier trained on the shared phenotypic features reliably distinguished the patient group as a whole from healthy controls (classification AUC = 0.95) and only 10 items were needed to attain the performance level of AUC being 0.90. A sum score created from the items produced high separability between patients and healthy controls (Cohen's d = 2.85), and it outperformed predefined sum scores and sub-scores within the instruments...Continue Reading

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