Psychosis high-risk identification model based on extreme gradient boosting algorithm

• Main Authors: WU JIAXIN, QI ANSI, SUN YAORU, LIU FEI, GUAN XIAOFENG, LONG XIANGYUN, LU ZHENG, WANG ZIJIAN
• Format: Patent
• Language: Chinese; English
• Published: 28.02.2020
• Subjects: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATIONTECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING ORPROCESSING OF MEDICAL OR HEALTHCARE DATA; INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTEDFOR SPECIFIC APPLICATION FIELDS; PHYSICS

The invention relates to a psychosis high-risk identification model based on an extreme gradient boosting algorithm. A method comprises the steps: establishing a psychosis high-risk identification model and identifying psychosis high-risk condition by utilizing the established model, wherein the step of establishing the psychosis high-risk identification model comprises the substeps: obtaining thedata of a screening tool as training features, normalizing the training features, training an XGBoost model, screening the features and simplifying the features; the step of identifying the psychosishigh-risk condition by using the established model comprises the following substeps: acquiring screening data of a subject, extracting specified features, and sending the specified features to the established identification model for identification. The method has the advantages that the schizophrenia is a severe mental disease, and the social function of a patient suffering from the schizophrenia is severely reduced, and