The psychosis analysis in real-world on a cohort of large-scale patients with schizophrenia
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RESEARCH
Open Access
The psychosis analysis in real-world on a cohort of large-scale patients with schizophrenia Wenyan Tan1†, Haicheng Lin1†, Baoxin Lei2, Aihua Ou3, Zehui He3, Ning Yang4, Fujun Jia1*, Heng Weng3* and Tianyong Hao2* From 5th China Health Information Processing Conference Guangzhou, China. 22-24 November 2019
Abstract Background: With China experiencing unprecedented economic development and social change over the past three decades, Chinese policy makers and health care professionals have come to view mental health as an important outcome to monitor. Our study conducted an epidemiological study of psychosis in Guangdong province, with 20 million real-world follow-up records in the last decade. Methods: Data was collected from Guangdong mental health information platform from 2010 to 2019, which had standardized disease registration and follow-up management for nearly 600,000 patients with six categories of mental diseases and 400,000 patients with schizophrenia. We conducted clinical staging for the disease course of the patients and divided the data with various factors into different stages of disease. Quantitative analysis was utilized to investigate the high relevant indicators to the disease. The results were projected on geography map for regional distribution analysis. (Continued on next page)
* Correspondence: [email protected]; [email protected]; [email protected] † Wenyan Tan and Haicheng Lin contributed equally to this work. 1 Guangdong Mental Health Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Science, Guangzhou, China 3 Department of Big Data Research of Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, China 2 School of Computer Science, South China Normal University, Guangzhou, China Full list of author information is available at the end of the article © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
Tan et al. BMC Medical Informatics and Decision Making 2020, 20(Suppl 3):132
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