Evidence map›Paper›PMID 37139355›Full record

SynthesisFrontiers in artificial intelligence2022

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

Abdul Razak M S, Nirmala C R, Sreenivasa B R, Husam Lahza, Hassan Fareed M Lahza

Abstract readSystematic Review
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in Frontiers in artificial intelligence, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

9 citing papers in PubMed.

  1. Article
  2. Review
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  4. Review
  5. Article
  6. Review
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  9. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Abdul Razak M SDepartment of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India.
Nirmala C RDepartment of Computer Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India.
Sreenivasa B RDepartment of Information Science and Engineering, Bapuji Institute of Engineering and Technology, Davangere, India.
Husam LahzaDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia.
Hassan Fareed M LahzaDepartment of Information Systems, College of Computers and Information Systems, Umm Al-Qura University, Makkah, Saudi Arabia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

concept driftdata streamdrift detection methodsfeature (interest) point selectionunsupervised learning

Identifiers

PMID37139355
PMCPMC10150933

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.