Glaucoma diagnosis in the Chinese context: An uncertainty information-centric Bayesian deep learning model
作者：
Highlights：
• The proposed BDMSL model in this research is the first Bayesian deep multi-source learning model that provides an information-centric framework for handling multiple medical data sources and decision uncertainty in solving healthcare problems.
• Based on real medical data collected from one of the best eye hospitals in China, our research results show that the BDMSL model achieves better performance in terms of glaucoma detection.
• With the exploration of the health informatics approach in diagnosing glaucoma in China, the research results of this study can be generalized to provide health services globally.
摘要
•The proposed BDMSL model in this research is the first Bayesian deep multi-source learning model that provides an information-centric framework for handling multiple medical data sources and decision uncertainty in solving healthcare problems.•Based on real medical data collected from one of the best eye hospitals in China, our research results show that the BDMSL model achieves better performance in terms of glaucoma detection.•With the exploration of the health informatics approach in diagnosing glaucoma in China, the research results of this study can be generalized to provide health services globally.
论文关键词：Bayesian deep learning,Multisource learning,Glaucoma diagnosis,Medical image analysis,Disease diagnosis
论文评审过程：Received 23 June 2020, Revised 25 November 2020, Accepted 25 November 2020, Available online 5 December 2020, Version of Record 5 December 2020.
论文官网地址：https://doi.org/10.1016/j.ipm.2020.102454
