Evidence map›Paper›PMID 34844397›Full record

ArticleClinical and experimental pediatrics2022

Big data analysis and artificial intelligence in epilepsy - common data model analysis and machine learning-based seizure detection and forecasting.

Yoon Gi Chung, Yonghoon Jeon, Sooyoung Yoo, Hunmin Kim, Hee Hwang

Abstract read
In one paragraph

Article in Clinical and experimental pediatrics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Machine-learning methods for epilepsy diagnosis and therapeutic prevention: advances, setbacks, and opportunities.Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology · 2026
    Review
  4. Review
  5. Article
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.

Yoon Gi ChungDivision of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea.
Yonghoon JeonR&D Center, ezCaretech Co., Ltd., Seoul, Korea.
Sooyoung YooOffice of eHealth Research and Business, Seoul National University Bundang Hospital, Seongnam, Korea.
Hunmin KimDivision of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea.
Hee HwangDivision of Pediatric Neurology, Department of Pediatrics, Seoul National University Bundang Hospital, Seongnam, Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

There has been significant interest in big data analysis and artificial intelligence (AI) in medicine. Ever-increasing medical data and advanced computing power have enabled the number of big data analyses and AI studies to increase rapidly. Here we briefly introduce epilepsy, big data, and AI and review big data analysis using a common data model. Studies in which AI has been actively applied, such as those of electroencephalography epileptiform discharge detection, seizure detection, and forecasting, will be reviewed. We will also provide practical suggestions for pediatricians to understand and interpret big data analysis and AI research and work together with technical expertise.

Indexed as

Artificial intelligenceBig data analysisDeep learningEpilepsyMachine learning

Identifiers

PMID34844397
PMCPMC9171464

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.