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A survey on detecting healthcare concept drift in AI/ML models from a finance perspective
Data is incredibly significant in today's digital age because data represents facts and numbers from our regular life transactions. Data is no longer arriving in a static form; it is now arriving in a streaming fashion. Data streams are the arrival of limitless, continuous, and rapid data. The...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10150933/ https://www.ncbi.nlm.nih.gov/pubmed/37139355 http://dx.doi.org/10.3389/frai.2022.955314 |
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author | M. S., Abdul Razak C. R., Nirmala B. R., Sreenivasa Lahza, Husam Lahza, Hassan Fareed M. |
author_facet | M. S., Abdul Razak C. R., Nirmala B. R., Sreenivasa Lahza, Husam Lahza, Hassan Fareed M. |
author_sort | M. S., Abdul Razak |
collection | PubMed |
description | Data is incredibly significant in today's digital age because data represents facts and numbers from our regular life transactions. Data is no longer arriving in a static form; it is now arriving in a streaming fashion. Data streams are the arrival of limitless, continuous, and rapid data. The healthcare industry is a major generator of data streams. Processing data streams is extremely complex due to factors such as volume, pace, and variety. Data stream classification is difficult owing to idea drift. Concept drift occurs in supervised learning when the statistical properties of the target variable that the model predicts change unexpectedly. We focused on solving various forms of concept drift problems in healthcare data streams in this research, and we outlined the existing statistical and machine learning methodologies for dealing with concept drift. It also emphasizes the use of deep learning algorithms for concept drift detection and describes the various healthcare datasets utilized for concept drift detection in data stream categorization. |
format | Online Article Text |
id | pubmed-10150933 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-101509332023-05-02 A survey on detecting healthcare concept drift in AI/ML models from a finance perspective M. S., Abdul Razak C. R., Nirmala B. R., Sreenivasa Lahza, Husam Lahza, Hassan Fareed M. Front Artif Intell Artificial Intelligence Data is incredibly significant in today's digital age because data represents facts and numbers from our regular life transactions. Data is no longer arriving in a static form; it is now arriving in a streaming fashion. Data streams are the arrival of limitless, continuous, and rapid data. The healthcare industry is a major generator of data streams. Processing data streams is extremely complex due to factors such as volume, pace, and variety. Data stream classification is difficult owing to idea drift. Concept drift occurs in supervised learning when the statistical properties of the target variable that the model predicts change unexpectedly. We focused on solving various forms of concept drift problems in healthcare data streams in this research, and we outlined the existing statistical and machine learning methodologies for dealing with concept drift. It also emphasizes the use of deep learning algorithms for concept drift detection and describes the various healthcare datasets utilized for concept drift detection in data stream categorization. Frontiers Media S.A. 2023-04-17 /pmc/articles/PMC10150933/ /pubmed/37139355 http://dx.doi.org/10.3389/frai.2022.955314 Text en Copyright © 2023 M. S., C. R., B. R., Lahza and Lahza. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Artificial Intelligence M. S., Abdul Razak C. R., Nirmala B. R., Sreenivasa Lahza, Husam Lahza, Hassan Fareed M. A survey on detecting healthcare concept drift in AI/ML models from a finance perspective |
title | A survey on detecting healthcare concept drift in AI/ML models from a finance perspective |
title_full | A survey on detecting healthcare concept drift in AI/ML models from a finance perspective |
title_fullStr | A survey on detecting healthcare concept drift in AI/ML models from a finance perspective |
title_full_unstemmed | A survey on detecting healthcare concept drift in AI/ML models from a finance perspective |
title_short | A survey on detecting healthcare concept drift in AI/ML models from a finance perspective |
title_sort | survey on detecting healthcare concept drift in ai/ml models from a finance perspective |
topic | Artificial Intelligence |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10150933/ https://www.ncbi.nlm.nih.gov/pubmed/37139355 http://dx.doi.org/10.3389/frai.2022.955314 |
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