A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism

Washington, Peter; Wall, Dennis P.

doi:10.1146/annurev-biodatasci-020722-125454

Cited by 30 publications

(8 citation statements)

References 125 publications

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“…The profound shift in society’s reliance on social media for information, in contrast to traditional news sources, along with the immense volume of generated data, has resulted in an increased focus on the use of natural language processing for text analytics. While research tools using facial expressions [ 6 , 66 - 75 ] and eye gazing for phenotyping ASD [ 76 , 77 ] are consistently reliable, there exists a current deficiency in standardizing precise methods for assessing deficits in social interaction. Therefore, linguistic and behavioral markers extracted from Twitter conversations can serve as valuable resources to investigate textual variations and social dynamics in naturalistic settings.…”

Section: Discussionmentioning

confidence: 99%

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Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study

Jaiswal,

Washington

2024

JMIR Form Res

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Background The increasing use of social media platforms has given rise to an unprecedented surge in user-generated content, with millions of individuals publicly sharing their thoughts, experiences, and health-related information. Social media can serve as a useful means to study and understand public health. Twitter (subsequently rebranded as “X”) is one such social media platform that has proven to be a valuable source of rich information for both the general public and health officials. We conducted the first study applying Twitter data mining to autism screening. Objective This study used Twitter as the primary source of data to study the behavioral characteristics and real-time emotional projections of individuals identifying with autism spectrum disorder (ASD). We aimed to improve the rigor of ASD analytics research by using the digital footprint of an individual to study the linguistic patterns of individuals with ASD. Methods We developed a machine learning model to distinguish individuals with autism from their neurotypical peers based on the textual patterns from their public communications on Twitter. We collected 6,515,470 tweets from users’ self-identification with autism using “#ActuallyAutistic” and a separate control group to identify linguistic markers associated with ASD traits. To construct the data set, we targeted English-language tweets using the search query “#ActuallyAutistic” posted from January 1, 2014, to December 31, 2022. From these tweets, we identified unique users who used keywords such as “autism” OR “autistic” OR “neurodiverse” in their profile description and collected all the tweets from their timeline. To build the control group data set, we formulated a search query excluding the hashtag, “-#ActuallyAutistic,” and collected 1000 tweets per day during the same time period. We trained a word2vec model and an attention-based, bidirectional long short-term memory model to validate the performance of per-tweet and per-profile classification models. We also illustrate the utility of the data set through common natural language processing tasks such as sentiment analysis and topic modeling. Results Our tweet classifier reached a 73% accuracy, a 0.728 area under the receiver operating characteristic curve score, and an 0.71 F1-score using word2vec representations fed into a logistic regression model, while the user profile classifier achieved an 0.78 area under the receiver operating characteristic curve score and an F1-score of 0.805 using an attention-based, bidirectional long short-term memory model. This is a promising start, demonstrating the potential for effective digital phenotyping studies and large-scale intervention using text data mined from social media. Conclusions Textual differences in social media communications can help researchers and clinicians conduct symptomatology studies in natural settings.

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Section: Discussionmentioning

confidence: 99%

“…Such nonclinical data hold considerable potential for clinicians and researchers to extract meaningful insights through a less intrusive approach. This digital footprint can be analyzed to study the behavioral symptoms of ASD and other mental health disorders [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study

Jaiswal,

Washington

2024

JMIR Form Res

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“…Such non-clinical data holds considerable potential for the research community to extract meaningful insights through a less intrusive approach and improve the rigor of ASD analytics research. The digital footprint of an individual can be analyzed to study behavioral symptoms of ASD and other mental health disorders 6 .…”

Section: Background and Summarymentioning

confidence: 99%

#ActuallyAutistic Twitter dataset for precision diagnosis of Autism Spectrum Disorder (ASD)

Jaiswal,

Washington

2023

Preprint

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The increasing usage of social media platforms has given rise to an unprecedented surge in user-generated content with millions of users sharing their thoughts, experiences, and health-related information. Because of this social media has turned out to be a useful means to study and understand public health. Twitter is one such platform that has proven to be a valuable source of such information for both public and health officials. We present a novel dataset consisting of 6,515,470 tweets collected from users self identifying with autism using "#ActuallyAutistic" and a control group. The dataset also has supporting information such as posting dates, follower count, geographical location, and interaction metrics. We illustrate the utility of the dataset through common Natural Language Processing (NLP) applications such as sentiment analysis, tweet and user classification, and topic modeling. The textual differences in social media communications can help researchers and clinicians to conduct symptomatology studies, in natural settings, by establishing effective biomarkers to distinguish an autistic individual from their typical peers. For better accessibility, reusability and new research insights, we have released the dataset publicly.

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“…Machine methods have also been applied to neuroimaging data to develop more precise and reliable approaches for characterizing and predicting ASD and ADHD in a binary way to distinguish from TD [11, 13, 19, 20, 21, 22, 23, 24]. Most studies in the literature are primarily focused on distinguishing individuals with a specific condition from typically developing (TD) individuals, resulting in a binary classification problem.…”

Section: Introductionmentioning

confidence: 99%

Multiclass Classification of Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, and Typically Developed Individuals Using fMRI Functional Connectivity Analysis

Alves,

Martinelli,

Sallum

et al. 2024

Preprint

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Neurodevelopmental conditions, such as Autism Spectrum Disorder (ASD) and Attention Deficit Hyperactivity Disorder (ADHD), present unique challenges due to overlapping symptoms, making an accurate diagnosis and targeted intervention difficult. Our study employs advanced machine learning techniques to analyze functional magnetic resonance imaging (fMRI) data from individuals with ASD, ADHD, and typically developed (TD) controls, totaling 120 subjects in the study. Leveraging multiclass classification (ML) algorithms, we achieve superior accuracy in distinguishing between ASD, ADHD, and TD groups, surpassing existing benchmarks with an area under the ROC curve near 98%. Our analysis reveals distinct neural signatures associated with ASD and ADHD: individuals with ADHD exhibit altered connectivity patterns of regions involved in attention and impulse control, whereas those with ASD show disruptions in brain regions critical for social and cognitive functions. The observed connectivity patterns, on which the ML classification rests, agree with established diagnostic approaches based on clinical symptoms. Furthermore, complex network analyses highlight differences in brain network integration and segregation among the three groups. Our findings pave the way for refined, ML-enhanced diagnostics in accordance with established practices, offering a promising avenue for developing trustworthy clinical decision-support systems.

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A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism

Cited by 30 publications

References 125 publications

Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study

Using #ActuallyAutistic on Twitter for Precision Diagnosis of Autism Spectrum Disorder: Machine Learning Study

#ActuallyAutistic Twitter dataset for precision diagnosis of Autism Spectrum Disorder (ASD)

Multiclass Classification of Autism Spectrum Disorder, Attention Deficit Hyperactivity Disorder, and Typically Developed Individuals Using fMRI Functional Connectivity Analysis

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