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DC Field | Value | Language |
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dc.contributor.author | Yousefi, L | - |
dc.contributor.author | Swift, S | - |
dc.contributor.author | Arzoky, M | - |
dc.contributor.author | Saachi, L | - |
dc.contributor.author | Chiovato, L | - |
dc.contributor.author | Tucker, A | - |
dc.date.accessioned | 2020-03-30T13:34:24Z | - |
dc.date.available | 2020-03-30T13:34:24Z | - |
dc.date.issued | 2020-03-29 | - |
dc.identifier | ORCID iDs: Leila Yousefi https://orcid.org/0000-0003-1952-0674; Stephen Swift https://orcid.org/0000-0001-8918-3365; Mahir Arzoky https://orcid.org/0000-0002-2721-643X; Allan Tucker https://orcid.org/0000-0001-5105-3506. | - |
dc.identifier.citation | Yousefi, L. et al.. (2021) 'Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules. Computational Intelligence', 37 (4), pp. 1460 - 1498. doi: 10.1111/coin.12313. | en_US |
dc.identifier.issn | 0824-7935 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/20607 | - |
dc.description.abstract | Copyright © 2020 The Authors. It is widely considered that approximately 10% of the population suffers from type 2 diabetes. Unfortunately, the impact of this disease is underestimated. Patient's mortality often occurs due to complications caused by the disease and not the disease itself. Many techniques utilized in modeling diseases are often in the form of a “black box” where the internal workings and complexities are extremely difficult to understand, both from practitioners' and patients' perspective. In this work, we address this issue and present an informative model/pattern, known as a “latent phenotype,” with an aim to capture the complexities of the associated complications' over time. We further extend this idea by using a combination of temporal association rule mining and unsupervised learning in order to find explainable subgroups of patients with more personalized prediction. Our extensive findings show how uncovering the latent phenotype aids in distinguishing the disparities among subgroups of patients based on their complications patterns. We gain insight into how best to enhance the prediction performance and reduce bias in the models applied using uncertainty in the patients' data. | en_US |
dc.format.extent | 1460 - 1498 | - |
dc.format.medium | Print-Electronic | - |
dc.language | English | - |
dc.language.iso | en | en_US |
dc.publisher | Wiley | en_US |
dc.rights | Copyright © 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. | - |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | latent variable discovery | en_US |
dc.subject | patient personalisation | en_US |
dc.subject | temporal phenotype | en_US |
dc.subject | time series clustering | en_US |
dc.subject | diabetes associated complication rules | en_US |
dc.title | Opening the black box: Personalizing type 2 diabetes patients based on their latent phenotype and temporal associated complication rules | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1111/coin.12313 | - |
dc.relation.isPartOf | Computational Intelligence | - |
pubs.issue | 4 | - |
pubs.publication-status | Published | - |
pubs.volume | 37 | - |
dc.identifier.eissn | 1467-8640 | - |
dc.rights.holder | The Authors | - |
Appears in Collections: | Dept of Computer Science Research Papers |
Files in This Item:
File | Description | Size | Format | |
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FullText.pdf | Copyright © 2020 The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. | 5.07 MB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License