Cargando…

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...

Descripción completa

Detalles Bibliográficos
Autores principales: M. S., Abdul Razak, C. R., Nirmala, B. R., Sreenivasa, Lahza, Husam, Lahza, Hassan Fareed M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
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
_version_ 1785035439601614848
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
work_keys_str_mv AT msabdulrazak asurveyondetectinghealthcareconceptdriftinaimlmodelsfromafinanceperspective
AT crnirmala asurveyondetectinghealthcareconceptdriftinaimlmodelsfromafinanceperspective
AT brsreenivasa asurveyondetectinghealthcareconceptdriftinaimlmodelsfromafinanceperspective
AT lahzahusam asurveyondetectinghealthcareconceptdriftinaimlmodelsfromafinanceperspective
AT lahzahassanfareedm asurveyondetectinghealthcareconceptdriftinaimlmodelsfromafinanceperspective
AT msabdulrazak surveyondetectinghealthcareconceptdriftinaimlmodelsfromafinanceperspective
AT crnirmala surveyondetectinghealthcareconceptdriftinaimlmodelsfromafinanceperspective
AT brsreenivasa surveyondetectinghealthcareconceptdriftinaimlmodelsfromafinanceperspective
AT lahzahusam surveyondetectinghealthcareconceptdriftinaimlmodelsfromafinanceperspective
AT lahzahassanfareedm surveyondetectinghealthcareconceptdriftinaimlmodelsfromafinanceperspective