Construction method of first episode schizophrenia individualized prediction model

Abstract

The invention belongs to the field of psychiatry, nerve images and artificial intelligence, discloses a construction method of a first episode schizophrenia individualized prediction model, and solvesthe problem of low accuracy of auxiliary diagnosis of an existing SCH brain structure network model. The method comprises the following steps: A, acquiring a diffusion tensor image of a first episodeschizophrenia patient; B, preprocessing the obtained diffusion tensor image; C, constructing a sparse brain structure network based on the preprocessed image; D, constructing each sparse multi-threshold fusion brain structure network of the subject by adopting a similar network fusion method; E, extracting multi-threshold fusion brain structure network topology attribute features, and then carrying out feature screening; F, based on the screened features, adopting a classifier for classification training, and obtaining a first episode schizophrenia individualized prediction model is obtained;and G, performing performance verification and evaluation on the first episode schizophrenia individualized prediction model obtained by training.

Description

technical field [0001] The invention belongs to the fields of psychiatry, neuroimaging and artificial intelligence, and in particular relates to a method for constructing an individualized prediction model of first-episode schizophrenia. Background technique [0002] Schizophrenia (SCH) is a highly disabling and fatal mental disorder. The World Health Organization lists it as one of the top ten diseases in the global burden of disease list. However, its brain mechanism has not yet been fully clarified. The diagnosis lacks objective criteria and the cure rate is low. It has become an urgent clinical problem to seek an objective, effective, convenient and feasible biological marker for early individualized diagnosis and treatment of SCH. Brain structural network changes are an important biological basis for neuroanatomical abnormalities in SCH. As a data-driven prediction and analysis tool, machine learning methods can make full use of the inherent structural information of b...

AI Technical Summary

Problems solved by technology

[0005] The technical problem to be solved by the present invention is: to propose a method for constructing an individualized prediction model of first-episode schizophrenia, and to solve the problem of low accuracy in the auxiliary diagnosis of the existing SCH brain structure network model

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