Evidence map›Paper›PMID 33357232›Full record

ArticleBMC medical informatics and decision making2020

A community effort for automatic detection of postictal generalized EEG suppression in epilepsy.

Yejin Kim, Xiaoqian Jiang, Samden D Lhatoo, Guo-Qiang Zhang, Shiqiang Tao, Licong Cui, Xiaojin Li, Robert D Jolly, Luyao Chen, Michael Phan and 3 more

Open access · goldAbstract readEditorial
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.1field-weighted citation impact, top 54% of its field
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

2 citing papers in PubMed, 4 citations in OpenAlex.

  1. Article
  2. 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

13 authors at 1 institution in 1 country.

Yejin KimSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA. yejin.kim@uth.tmc.edu.
Xiaoqian JiangSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
Samden D LhatooDepartment of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
Guo-Qiang ZhangSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
Shiqiang TaoDepartment of Neurology, McGovern Medical School, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
Licong CuiSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
Xiaojin LiSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
Robert D JollySchool of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
Luyao ChenSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
Michael PhanSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
Cung HaSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
Marijane DetranaltesSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
Jiajie ZhangSchool of Biomedical Informatics, University of Texas Health Science Center at Houston, 7000 Fannin Street, 77030, Houston, TX, USA.
The University of Texas Health Science Center at Houston · US

Funding

The Center for SUDEP Research (CSR), Informatics and Data Analytics Core (IDAC)U01NS090408 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI ZHANG, GUO-QIANG · 2014 to 2018
$3.8M
The Administrative Core of the Center of SUDEP ResearchU01NS090405 · NINDS · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI LHATOO, SAMDEN, NOEBELS, JEFFREY · 2014 to 2018
$3.2M
NINDS NIH HHS U01 NS090405NINDS NIH HHS U01 NS090408
6 · The paper itself

Abstract

Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinical knowledge and biomedical data. However, medicine and data science are not synchronized. Oftentimes, researchers with a strong data science background do not understand the clinical challenges, while on the other hand, physicians do not know the capacity and limitation of state-of-the-art machine learning methods. The difficulty boils down to the lack of a common interface between two highly intelligent communities due to the privacy concerns and the disciplinary gap. The School of Biomedical Informatics (SBMI) at UTHealth is a pilot in connecting both worlds to promote interdisciplinary research. Recently, the Center for Secure Artificial Intelligence For hEalthcare (SAFE) at SBMI is organizing a series of machine learning healthcare hackathons for real-world clinical challenges. We hosted our first Hackathon themed centered around Sudden Unexpected Death in Epilepsy and finding ways to recognize the warning signs. This community effort demonstrated that interdisciplinary discussion and productive competition has significantly increased the accuracy of warning sign detection compared to the previous work, and ultimately showing a potential of this hackathon as a platform to connect the two communities of data science and medicine.

Indexed as

Artificial IntelligenceEpilepsyDeath, SuddenElectroencephalographyHumansMachine LearningDetectionElectroencephalographicIntermittent slow wavePhysiological signalPostictal Generalized electroencephalographic suppressionPrediction

Identifiers

PMID33357232
PMCPMC7758923
OpenAlexW3116097222

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.