System for early detection and risk prediction of schizophrenia

Abstract

The invention discloses a system for early detection and risk prediction of schizophrenia. The technical problem that currently, it is hard to accurately and objectively carry out early detection andrisk prediction on schizophrenia can be solved. The system comprises a test module, a collecting and preprocessing module, a data analysis module, a learning classification module and a classificationperformance evaluation method. According to the system for early detection and risk prediction of the schizophrenia, high-risk individuals, ultra-high-risk individuals and initially-attacked people of the schizophrenia are subjected to quantitative and objective detection recognition and risk prediction by comprehensively utilizing cognitive tests, clinical detection, P50 sensory gating electrophysiological characteristics and an XGBoost(the Extreme Gradient Boosting) machine learning method; specifically, by combinedly using cognition, clinic and P50 sensory gating experiments, and adoptingsource location, brain networks, machine learning and other analysis methods, initially-attacked people, high-risk people, ultra-high-risk people and normal contrast people are classified, and the classification accuracy, sensitivity and specificity are improved.

Description

technical field [0001] The invention relates to the technical field of bioinformatics, in particular to a system for early detection and risk prediction of schizophrenia. Background technique [0002] Schizophrenia is a group of severe mental illnesses of unknown etiology, often with cognitive, social and functional impairments, mostly onset in young adults, affecting about 1% of the world's population, and causing economic losses of about 155.7 billion US dollars. There is increasing evidence that schizophrenia is a neurodevelopmental disorder. The neurodevelopmental model divides the developmental trajectory of schizophrenia into four stages: high-risk (HR), ultra-high-risk (UHR) or prodromal, first-episode schizophrenia (FESZ) ) and chronic phases. It is worth pointing out that even with the rapid development of modern medicine, the conclusions about the etiology of schizophrenia are still unclear. To this day, people still cannot predict the development trend of schiz...

AI Technical Summary

Problems solved by technology

However, so far, few studies have comprehensively used neuroelectrophysiological indicators and cognitive clinical test indicators to diagnose schizophrenia

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