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An artificial intelligence-enabled pipeline for medical domain: Malaysian breast cancer survivorship cohort as a case study

Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival p...

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Main Authors: Ganggayah, Mogana Darshini, Dhillon, Sarinder Kaur, Islam, Tania, Kalhor, Foad, Chiang, Teh Chean, Kalafi, Elham Yousef, Taib, Nur Aishah
Format: Article
Published: MDPI 2021
Subjects:
R Medicine (General)
RC Internal medicine
Online Access:http://eprints.um.edu.my/28589/
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spelling my.um.eprints.285892022-03-02T07:29:51Z http://eprints.um.edu.my/28589/ An artificial intelligence-enabled pipeline for medical domain: Malaysian breast cancer survivorship cohort as a case study Ganggayah, Mogana Darshini Dhillon, Sarinder Kaur Islam, Tania Kalhor, Foad Chiang, Teh Chean Kalafi, Elham Yousef Taib, Nur Aishah R Medicine (General) RC Internal medicine Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer. MDPI 2021-08 Article PeerReviewed Ganggayah, Mogana Darshini and Dhillon, Sarinder Kaur and Islam, Tania and Kalhor, Foad and Chiang, Teh Chean and Kalafi, Elham Yousef and Taib, Nur Aishah (2021) An artificial intelligence-enabled pipeline for medical domain: Malaysian breast cancer survivorship cohort as a case study. Diagnostics, 11 (8). ISSN 2075-4418, DOI https://doi.org/10.3390/diagnostics11081492 <https://doi.org/10.3390/diagnostics11081492>. 10.3390/diagnostics11081492
institution Universiti Malaya
building UM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaya
content_source UM Research Repository
url_provider http://eprints.um.edu.my/
topic R Medicine (General)
RC Internal medicine
spellingShingle R Medicine (General)
RC Internal medicine
Ganggayah, Mogana Darshini
Dhillon, Sarinder Kaur
Islam, Tania
Kalhor, Foad
Chiang, Teh Chean
Kalafi, Elham Yousef
Taib, Nur Aishah
An artificial intelligence-enabled pipeline for medical domain: Malaysian breast cancer survivorship cohort as a case study
description Automated artificial intelligence (AI) systems enable the integration of different types of data from various sources for clinical decision-making. The aim of this study is to propose a pipeline to develop a fully automated clinician-friendly AI-enabled database platform for breast cancer survival prediction. A case study of breast cancer survival cohort from the University Malaya Medical Centre was used to develop and evaluate the pipeline. A relational database and a fully automated system were developed by integrating the database with analytical modules (machine learning, automated scoring for quality of life, and interactive visualization). The developed pipeline, iSurvive has helped in enhancing data management as well as to visualize important prognostic variables and survival rates. The embedded automated scoring module demonstrated quality of life of patients whereas the interactive visualizations could be used by clinicians to facilitate communication with patients. The pipeline proposed in this study is a one-stop center to manage data, to automate analytics using machine learning, to automate scoring and to produce explainable interactive visuals to enhance clinician-patient communication along the survivorship period to modify behaviours that relate to prognosis. The pipeline proposed can be modelled on any disease not limited to breast cancer.
format Article
author Ganggayah, Mogana Darshini
Dhillon, Sarinder Kaur
Islam, Tania
Kalhor, Foad
Chiang, Teh Chean
Kalafi, Elham Yousef
Taib, Nur Aishah
author_facet Ganggayah, Mogana Darshini
Dhillon, Sarinder Kaur
Islam, Tania
Kalhor, Foad
Chiang, Teh Chean
Kalafi, Elham Yousef
Taib, Nur Aishah
author_sort Ganggayah, Mogana Darshini
title An artificial intelligence-enabled pipeline for medical domain: Malaysian breast cancer survivorship cohort as a case study
title_short An artificial intelligence-enabled pipeline for medical domain: Malaysian breast cancer survivorship cohort as a case study
title_full An artificial intelligence-enabled pipeline for medical domain: Malaysian breast cancer survivorship cohort as a case study
title_fullStr An artificial intelligence-enabled pipeline for medical domain: Malaysian breast cancer survivorship cohort as a case study
title_full_unstemmed An artificial intelligence-enabled pipeline for medical domain: Malaysian breast cancer survivorship cohort as a case study
title_sort artificial intelligence-enabled pipeline for medical domain: malaysian breast cancer survivorship cohort as a case study
publisher MDPI
publishDate 2021
url http://eprints.um.edu.my/28589/
_version_ 1735409560902959104
score 13.211869

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