scholarly journals White Matter Connectome Edge Density in Children with Autism Spectrum Disorders: Potential Imaging Biomarkers Using Machine-Learning Models

2019 ◽  
Vol 9 (2) ◽  
pp. 209-220 ◽  
Author(s):  
Seyedmehdi Payabvash ◽  
Eva M. Palacios ◽  
Julia P. Owen ◽  
Maxwell B. Wang ◽  
Teresa Tavassoli ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorders ◽  
White Matter ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Imaging Biomarkers ◽  
Edge Density ◽  
Learning Models ◽  
Spectrum Disorders ◽  
Machine Learning Models

scholarly journals A machine learning approach to inform developmental milestone achievement for children with autism (Preprint)

2021 ◽  
Author(s):  
Munirul M. Haque ◽  
Masud Rabbani ◽  
Dipranjan Das Dipal ◽  
Md Ishrak Islam Zarif ◽  
Anik Iqbal ◽  
...  
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Children With Autism ◽  
Autism Spectrum ◽  
The Other ◽  
Supervised Machine Learning ◽  
Learning Models ◽  
Children With Asd ◽  
Socio Demographic Factors ◽  
Machine Learning Models

BACKGROUND Care for children with autism spectrum disorder (ASD) can be challenging for families and medical care systems. This is especially true in Low-and-Middle-Income-countries (LMIC) like Bangladesh. To improve family-practitioner communication and developmental monitoring of children with ASD, [spell out] (mCARE) was developed. Within this study, mCARE was used to track child milestone achievement and family socio-demographic assets to inform mCARE feasibility/scalability and family-asset informed practitioner recommendations. OBJECTIVE The objectives of this paper are three-fold. First, document how mCARE can be used to monitor child milestone achievement. Second, demonstrate how advanced machine learning models can inform our understanding of milestone achievement in children with ASD. Third, describe family/child socio-demographic factors that are associated with earlier milestone achievement in children with ASD (across five machine learning models). METHODS Using mCARE collected data, this study assessed milestone achievement in 300 children with ASD from Bangladesh. In this study, we used four supervised machine learning (ML) algorithms (Decision Tree, Logistic Regression, k-Nearest Neighbors, Artificial Neural Network) and one unsupervised machine learning (K-means Clustering) to build models of milestone achievement based on family/child socio-demographic details. For analyses, the sample was randomly divided in half to train the ML models and then their accuracy was estimated based on the other half of the sample. Each model was specified for the following milestones: Brushes teeth, Asks to use the toilet, Urinates in the toilet or potty, and Buttons large buttons. RESULTS This study aimed to find a suitable machine learning algorithm for milestone prediction/achievement for children with ASD using family/child socio-demographic characteristics. For, Brushes teeth, the three supervised machine learning models met or exceeded an accuracy of 95% with Logistic Regression, KNN, and ANN as the most robust socio-demographic predictors. For Asks to use toilet, 84.00% accuracy was achieved with the KNN and ANN models. For these models, the family socio-demographic predictors of “family expenditure” and “parents’ age” accounted for most of the model variability. The last two parameters, Urinates in toilet or potty and Buttons large buttons had an accuracy of 91.00% and 76.00%, respectively, in ANN. Overall, the ANN had a higher accuracy (Above ~80% on average) among the other algorithms for all the parameters. Across the models and milestones, “family expenditure”, “family size/ type”, “living places” and “parent’s age and occupation” were the most influential family/child socio-demographic factors. CONCLUSIONS mCARE was successfully deployed in an LMIC (i.e., Bangladesh), allowing parents and care-practitioners a mechanism to share detailed information on child milestones achievement. Using advanced modeling techniques this study demonstrates how family/child socio-demographic elements can inform child milestone achievement. Specifically, families with fewer socio-demographic resources reported later milestone attainment. Developmental science theories highlight how family/systems can directly influence child development and this study provides a clear link between family resources and child developmental progress. Clinical implications for this work could include supporting the larger family system to improve child milestone achievement. CLINICALTRIAL We took the IRB from Marquette University Institutional Review Board on July 9, 2020, with the protocol number HR-1803022959, and titled “MOBILE-BASED CARE FOR CHILDREN WITH AUTISM SPECTRUM DISORDER USING REMOTE EXPERIENCE SAMPLING METHOD (MCARE)” for recruiting a total of 316 subjects, of which we recruited 300. (Details description of participants in Methods section)

Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders

2017 ◽  
Vol 124 (5) ◽  
pp. 961-973 ◽  
Author(s):  
Yasushi Nakai ◽  
Tetsuya Takiguchi ◽  
Gakuyo Matsui ◽  
Noriko Yamaoka ◽  
Satoshi Takada
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorders ◽  
False Negative ◽  
False Negative Rate ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Single Word ◽  
Voice Analysis ◽  
Spectrum Disorders ◽  
Speech Therapists

Abnormal prosody is often evident in the voice intonations of individuals with autism spectrum disorders. We compared a machine-learning-based voice analysis with human hearing judgments made by 10 speech therapists for classifying children with autism spectrum disorders ( n = 30) and typical development ( n = 51). Using stimuli limited to single-word utterances, machine-learning-based voice analysis was superior to speech therapist judgments. There was a significantly higher true-positive than false-negative rate for machine-learning-based voice analysis but not for speech therapists. Results are discussed in terms of some artificiality of clinician judgments based on single-word utterances, and the objectivity machine-learning-based voice analysis adds to judging abnormal prosody.

Disfluency Characteristics Observed in Young Children With Autism Spectrum Disorders: A Preliminary Report

2010 ◽  
Vol 20 (2) ◽  
pp. 42-50 ◽  
Author(s):  
Laura W. Plexico ◽  
Julie E. Cleary ◽  
Ashlynn McAlpine ◽  
Allison M. Plumb
Keyword(s):  
Autism Spectrum Disorders ◽  
Young Children ◽  
Children With Autism ◽  
Descriptive Study ◽  
Autism Spectrum ◽  
Typically Developing ◽  
Children With Asd ◽  
Young Children With Autism ◽  
Spectrum Disorders ◽  
Developmental Stuttering

This descriptive study evaluates the speech disfluencies of 8 verbal children between 3 and 5 years of age with autism spectrum disorders (ASD). Speech samples were collected for each child during standardized interactions. Percentage and types of disfluencies observed during speech samples are discussed. Although they did not have a clinical diagnosis of stuttering, all of the young children with ASD in this study produced disfluencies. In addition to stuttering-like disfluencies and other typical disfluencies, the children with ASD also produced atypical disfluencies, which usually are not observed in children with typically developing speech or developmental stuttering. (Yairi & Ambrose, 2005).

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