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Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy

Drug component interactions are most likely to trigger unexpected pharmacological effects with unknown causal mechanisms, hence, demanding the discovery of patterns to establish suitable and effective regimens. This paper proposes a novel framework that embeds machine learning (ML) and multidimensio...

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Autores principales: Ekpenyong, Moses E., Etebong, Philip I., Jackson, Tenderwealth C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6656963/
https://www.ncbi.nlm.nih.gov/pubmed/31372545
http://dx.doi.org/10.1016/j.heliyon.2019.e02080
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author Ekpenyong, Moses E.
Etebong, Philip I.
Jackson, Tenderwealth C.
author_facet Ekpenyong, Moses E.
Etebong, Philip I.
Jackson, Tenderwealth C.
author_sort Ekpenyong, Moses E.
collection PubMed
description Drug component interactions are most likely to trigger unexpected pharmacological effects with unknown causal mechanisms, hence, demanding the discovery of patterns to establish suitable and effective regimens. This paper proposes a novel framework that embeds machine learning (ML) and multidimensional scaling (MDS) techniques, for efficient prediction of patient response to antiretroviral therapy (ART). To achieve this, experiment databases were created from two independent sources: a publicly available HIV domain datasets of patients with failed treatment – hosted by the Stanford University, hereinafter referred to as the Stanford HIV database, and locally sourced datasets gathered from 13 prominent healthcare facilities treating HIV patients in Akwa Ibom State of Nigeria, hereinafter referred to as the Akwa-Ibom HIV database: with 5,780 and 3,168 individual treatment change episodes (TCEs) of HIV treatment indicators (baseline CD4 count (BCD4), followup CD4 count (FCD4), baseline viral load (BRNA), followup viral load (FRNA), and drug type combination (DType)), observed from 1,521 and 1,301 unique patient records, respectively. A hybridised (two-stage) classification system consuming the Interval Type-2 Fuzzy Logic (IT2FL) and Deep Neural Network (DNN) was employed to model and optimise patients’ response to ART with appreciable error pruning achieved through MDS. Visualisation of the experiment databases showed remarkable immunological changes in the Akwa-Ibom HIV database, as the FCD4 of TCEs clustered far above the BCD4, compared to the Stanford HIV database, where over 40% of FCD4 clustered below the BCD4. Similar changes were noticed for the RNA, as more FRNA copies clustered below the BRNA for the Akwa-Ibom datasets, compared to the Stamford datasets. DNN classification results for both databases showed best performance metrics for the Levenberg-Marquardt algorithm when compared with the resilient backpropagation algorithm, with improved drug pattern predictions for experiment with MDS. This paper is most likely to evolve an avenue that triggers interesting combination(s) for optimum patient response, while ensuring minimal side effects, as further findings revealed the superiority of the proposed approach over existing approaches.
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spelling pubmed-66569632019-08-01 Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy Ekpenyong, Moses E. Etebong, Philip I. Jackson, Tenderwealth C. Heliyon Article Drug component interactions are most likely to trigger unexpected pharmacological effects with unknown causal mechanisms, hence, demanding the discovery of patterns to establish suitable and effective regimens. This paper proposes a novel framework that embeds machine learning (ML) and multidimensional scaling (MDS) techniques, for efficient prediction of patient response to antiretroviral therapy (ART). To achieve this, experiment databases were created from two independent sources: a publicly available HIV domain datasets of patients with failed treatment – hosted by the Stanford University, hereinafter referred to as the Stanford HIV database, and locally sourced datasets gathered from 13 prominent healthcare facilities treating HIV patients in Akwa Ibom State of Nigeria, hereinafter referred to as the Akwa-Ibom HIV database: with 5,780 and 3,168 individual treatment change episodes (TCEs) of HIV treatment indicators (baseline CD4 count (BCD4), followup CD4 count (FCD4), baseline viral load (BRNA), followup viral load (FRNA), and drug type combination (DType)), observed from 1,521 and 1,301 unique patient records, respectively. A hybridised (two-stage) classification system consuming the Interval Type-2 Fuzzy Logic (IT2FL) and Deep Neural Network (DNN) was employed to model and optimise patients’ response to ART with appreciable error pruning achieved through MDS. Visualisation of the experiment databases showed remarkable immunological changes in the Akwa-Ibom HIV database, as the FCD4 of TCEs clustered far above the BCD4, compared to the Stanford HIV database, where over 40% of FCD4 clustered below the BCD4. Similar changes were noticed for the RNA, as more FRNA copies clustered below the BRNA for the Akwa-Ibom datasets, compared to the Stamford datasets. DNN classification results for both databases showed best performance metrics for the Levenberg-Marquardt algorithm when compared with the resilient backpropagation algorithm, with improved drug pattern predictions for experiment with MDS. This paper is most likely to evolve an avenue that triggers interesting combination(s) for optimum patient response, while ensuring minimal side effects, as further findings revealed the superiority of the proposed approach over existing approaches. Elsevier 2019-07-20 /pmc/articles/PMC6656963/ /pubmed/31372545 http://dx.doi.org/10.1016/j.heliyon.2019.e02080 Text en © 2019 Published by Elsevier Ltd. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Ekpenyong, Moses E.
Etebong, Philip I.
Jackson, Tenderwealth C.
Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy
title Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy
title_full Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy
title_fullStr Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy
title_full_unstemmed Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy
title_short Fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy
title_sort fuzzy-multidimensional deep learning for efficient prediction of patient response to antiretroviral therapy
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6656963/
https://www.ncbi.nlm.nih.gov/pubmed/31372545
http://dx.doi.org/10.1016/j.heliyon.2019.e02080
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