O11.2. CHARACTERIZING CANNABINOID INDUCED ACUTE PERSISTENT PSYCHOSIS (CIAPP) AS A POSSIBLE SUBTYPE OF SCHIZOPHRENIA USING DEEP LEARNING

摘要

The schizophrenia syndrome likely encompasses multiple clinically related illness manifestations that result from distinct etio-pathological processes converging onto fewer final common pathways. Exposure to cannabis is known to result in a syndrome that clinically mimics schizophrenia-psychosis, outlasts the acute intoxication, persists for days to weeks, requires clinical intervention, and may recur with cannabis exposure. Characterizing the vulnerability to Cannabinoid Induced Acute and Persistent Psychosis (CIAPP), its clinical and neuro-physiological correlates, and its relationship to schizophrenia may enhance our understanding of the neurobiology of schizophrenia in general and specifically, this subtype. Deep learning is an extremely powerful approach to classify (e.g., cancerous vs healthy cells) and use complex stimuli to anticipate future outcomes (e.g., hurricane path) with high accuracy. Thus, deep learning seems a promising approach to differentiate subtypes of complex syndromes such as schizophrenia.