Multi-task deep learning-based survival analysis on the prognosis of late AMD using the longitudinal data in AREDS
Autor: | Zhiyong Lu, Kun Chen, Fei Wang, Mingquan Lin, Tiarnan D L Keenan, Qingyu Chen, Yifan Peng, Gregory C. Ghahramani, Emily Y. Chew, Matthew Brendel |
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Rok vydání: | 2021 |
Předmět: |
genetic structures
Fundus Oculi Longitudinal data Computer science Patient risk Machine learning computer.software_genre Convolutional neural network Task (project management) Macular Degeneration Deep Learning medicine Humans Survival analysis business.industry Deep learning Articles Macular degeneration Prognosis medicine.disease Survival Analysis eye diseases Disease Progression sense organs Artificial intelligence business computer |
Zdroj: | AMIA Annu Symp Proc |
Popis: | Age-related macular degeneration (AMD) is the leading cause of vision loss. Some patients experience vision loss over a delayed timeframe, others at a rapid pace. Physicians analyze time-of-visit fundus photographs to predict patient risk of developing late-AMD, the most severe form of AMD. Our study hypothesizes that 1) incorporating historical data improves predictive strength of developing late-AMD and 2) state-of-the-art deep-learning techniques extract more predictive image features than clinicians do. We incorporate longitudinal data from the Age-Related Eye Disease Studies and deep-learning extracted image features in survival settings to predict development of late-AMD. To extract image features, we used multi-task learning frameworks to train convolutional neural networks. Our findings show 1) incorporating longitudinal data improves prediction of late-AMD for clinical standard features, but only the current visit is informative when using complex features and 2) “deep-features” are more informative than clinician derived features. We make codes publicly available at https://github.com/bionlplab/AMD_prognosis_amia2021. |
Databáze: | OpenAIRE |
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