Evidence map›Paper›PMID 35913654›Full record

ArticleJournal of autism and developmental disorders2023

Use of Oculomotor Behavior to Classify Children with Autism and Typical Development: A Novel Implementation of the Machine Learning Approach.

Zhong Zhao, Jiwei Wei, Jiayi Xing, Xiaobin Zhang, Xingda Qu, Xinyao Hu, Jianping Lu

Abstract read
PubMed Publisher
In one paragraph

Article in Journal of autism and developmental disorders, 2023. 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
–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

2 citing papers in PubMed.

  1. Article
  2. [Machine learning algorithms for identifying autism spectrum disorder through eye-tracking in different intention videos].Zhongguo dang dai er ke za zhi = Chinese journal of contemporary pediatrics · 2024
    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

7 authors.

Zhong ZhaoInstitute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China.
Jiwei WeiInstitute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China.
Jiayi XingInstitute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China.
Xiaobin ZhangShenzhen Guangming District Center for Disease Control and Prevention, 67 Fengjingbei Avenue, Shenzhen, Guangdong, China. azhangxiaobin@126.com.
Xingda QuInstitute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China.
Xinyao HuInstitute of Human Factors and Ergonomics, College of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, China.
Jianping LuDepartment of Child Psychiatry of Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, China.

Funding

Key medical discipline of GuangMing Shenzhen 12 EpidemiologyKey Realm R&D Program of Guangdong Province 2019B030335001National Natural Science Foundation of China 82171539Sanming Project of Medicine in Shenzhen SZSM201612079Science and Technology Innovation Committee of Shenzhen JCYJ20190808115205498Shenzhen Double Chain Grant [2018]256Shenzhen Key Medical Discipline Construction Fund SZXK042Shenzhen University 860-000002110259
6 · The paper itself

Abstract

This study segmented the time series of gaze behavior from nineteen children with autism spectrum disorder (ASD) and 20 children with typical development in a face-to-face conversation. A machine learning approach showed that behavior segments produced by these two groups of participants could be classified with the highest accuracy of 74.15%. These results were further used to classify children using a threshold classifier. A maximum classification accuracy of 87.18% was achieved, under the condition that a participant was considered as 'ASD' if over 46% of the child's 7-s behavior segments were classified as ASD-like behaviors. The idea of combining the behavior segmentation technique and the threshold classifier could maximally preserve participants' data, and promote the automatic screening of ASD.

Indexed as

Autism Spectrum DisorderAutistic DisorderChildCommunicationEye MovementsHumansMachine LearningAutismBehavior segmentationEntropyEye-trackingMachine learningOculomotor

Identifiers

PMID35913654

What OpenQuestion holds

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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.