Evidence mapPaperPMID 37711468Full record

ArticleInternational journal of clinical and health psychology : IJCHP

Supervised machine learning: A new method to predict the outcomes following exercise intervention in children with autism spectrum disorder.

Zhiyuan Sun, Yunhao Yuan, Xiaoxiao Dong, Zhimei Liu, Kelong Cai, Wei Cheng, Jingjing Wu, Zhiyuan Qiao, Aiguo Chen

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Article in International journal of clinical and health psychology : IJCHP. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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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

9 authors.

Zhiyuan SunCollege of Physical Education, Yangzhou University, Yangzhou 225127, China.
Yunhao YuanSchool of Information Engineering, Yangzhou University, Yangzhou 225127, China.
Xiaoxiao DongCollege of Physical Education, Yangzhou University, Yangzhou 225127, China.
Zhimei LiuCollege of Physical Education, Yangzhou University, Yangzhou 225127, China.
Kelong CaiCollege of Physical Education, Yangzhou University, Yangzhou 225127, China.
Wei ChengCollege of Physical Education, Yangzhou University, Yangzhou 225127, China.
Jingjing WuCollege of Physical Education, Yangzhou University, Yangzhou 225127, China.
Zhiyuan QiaoCollege of Physical Education, Yangzhou University, Yangzhou 225127, China.
Aiguo ChenCollege of Physical Education, Yangzhou University, Yangzhou 225127, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The individual differences among children with autism spectrum disorder (ASD) may make it challenging to achieve comparable benefits from a specific exercise intervention program. A new method for predicting the possible outcomes and maximizing the benefits of exercise intervention for children with ASD needs further exploration. Using the mini-basketball training program (MBTP) studies to improve the symptom performance of children with ASD as an example, we used the supervised machine learning method to predict the possible intervention outcomes based on the individual differences of children with ASD, investigated and validated the efficacy of this method. In a long-term study, we included 41 ASD children who received the MBTP. Before the intervention, we collected their clinical information, behavioral factors, and brain structural indicators as candidate factors. To perform the regression and classification tasks, the random forest algorithm from the supervised machine learning method was selected, and the cross validation method was used to determine the reliability of the prediction results. The regression task was used to predict the social communication impairment outcome following the MBTP in children with ASD, and explainable variance was used to evaluate the predictive performance. The classification task was used to distinguish the core symptom outcome groups of ASD children, and predictive performance was assessed based on accuracy. We discovered that random forest models could predict the outcome of social communication impairment (average explained variance was 30.58%) and core symptom (average accuracy was 66.12%) following the MBTP, confirming that the supervised machine learning method can predict exercise intervention outcomes for children with ASD. Our findings provide a novel and reliable method for identifying ASD children most likely to benefit from a specific exercise intervention program in advance and a solid foundation for establishing a personalized exercise intervention program recommendation system for ASD children.

Indexed as

Autism spectrum disorderExercise interventionOutcomesPredictionSupervised machine learning

Identifiers

PMID37711468
PMCPMC10498172

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.