Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023 Apr 17:5:955314.
doi: 10.3389/frai.2022.955314. eCollection 2022.

A survey on detecting healthcare concept drift in AI/ML models from a finance perspective

Abdul Razak M S  1 , Nirmala C R  1 , Sreenivasa B R  2 , Husam Lahza  3 , Hassan Fareed M Lahza  4
Affiliations

A survey on detecting healthcare concept drift in AI/ML models from a finance perspective

Abdul Razak M S et al. Front Artif Intell. .

Abstract

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.

Keywords: concept drift; data stream; drift detection methods; feature (interest) point selection; unsupervised learning.

PubMed Disclaimer

Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Concept drift.
Figure 2
Figure 2
(A–E) Types of concept drift.
Figure 3
Figure 3
Different ways of occurrence of concept drift.
Figure 4
Figure 4
Systematic review process of concept drift.
Figure 5
Figure 5
An overall framework for concept drift detection (Lu et al., 2019).
Figure 6
Figure 6
Window-based concept drift detection.
Figure 7
Figure 7
Ensemble methods.
Figure 8
Figure 8
Feature distribution monitoring.
Figure 9
Figure 9
Clustering data streams.
See this image and copyright information in PMC

References

    1. Agrahari S., Singh A. K. (2021). Concept drift detection in data stream mining: a literature review. J. King Saud Univ. Comput. Inform. Sci. 34, 9523–9540. 10.1016/j.jksuci.2021.11.006 - DOI
    1. Alippi C., Roveri M. (2008). Just-in-time adaptive classifiers-part I: detecting nonstationary changes. IEEE Trans. Neural Netw. 19, 1145–1153. 10.1109/TNN.2008.2000082 - DOI - PubMed
    1. Baena-Garcia M., del Campo-Avila J., Fidalgo R., Bifet A., Gavalda R., Morales-Bueno R. (2006). “Early drift detection method,” in Proc. 4th Int. Workshop Knowledge Discovery from Data Streams.
    1. Bayram F., Ahmed B. S., Kassler A. (2022). From concept drift to model degradation: an overview on performance-aware drift detectors. Knowledge Based Syst. 245, 108632. 10.1016/j.knosys.2022.108632 - DOI
    1. Beyene A. A., Welemariam T., Persson M., Lavesson N. (2015). Improved concept drift handling in surgery prediction and other applications. Knowledge Inform. Syst. 44, 177–196. 10.1007/s10115-014-0756-9 - DOI

Publication types

LinkOut - more resources