Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules
Author:
Affiliation:
1. Department of Computer Science Brunel University London London UK
2. Department of Computer Science University of Pavia Pavia Italy
3. Unit of Endocrinology University of Pavia Pavia Italy
Publisher
Wiley
Subject
Artificial Intelligence,Computational Mathematics
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1111/coin.12313
Reference50 articles.
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2. YousefiL TuckerA Al‐luhaybiM SaachiL BellazziR ChiovatoL. Predicting disease complications using a stepwise hidden variable approach for learning dynamic Bayesian networks. Paper presented at: 2018 IEEE 31st International Symposium on Computer‐Based Medical Systems (CBMS); IEEE;2018:106‐111.
3. YousefiL SwiftS ArzokyM SaachiL ChiovatoL TuckerA. Opening the black box: discovering and explaining hidden variables in type 2 diabetic patient modelling. Paper presented at: 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM); IEEE;2018:1040‐1044.
4. YousefiL SwiftS ArzokyM SacchiL ChiovatoL TuckerA. Opening the black box: exploring temporal pattern of type 2 diabetes complications in patient clustering using association rules and hidden variable discovery. Paper presented at: 2019 IEEE 32nd International Symposium on Computer‐Based Medical Systems (CBMS); IEEE;2019:198‐203.
5. U.K. Prospective Diabetes Study 16: Overview of 6 Years' Therapy of Type II Diabetes: A Progressive Disease
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