Detecting autism from picture book narratives using deep neural utterance embeddings.
Background: Deficits in the ability to use language in social contexts, including storytelling skills, are observed across the autism spectrum. Development in machine‐learning approaches may contribute to clinical psychology and psychiatry, given its potential to support decisions concerning the dia...
| Publicado en: | International Journal of Language & Communication Disorders (John Wiley & Sons, Inc.) Vol. 57; no. 5; pp. 948 - 963 |
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| Autores principales: | , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
John Wiley & Sons, Inc.
Sep2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=159295220&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159295220 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 14606984 NRNM jtl: International Journal of Language & Communication Disorders (John Wiley & Sons, Inc.) issn: 14606984 maglogo: N pubinfo: dt: Sep2022 vid: 57 iid: 5 pid: 52269 pub: John Wiley & Sons, Inc. artinfo: ui: 159295220 159295220 159295220 10.1111/1460-6984.12731 159295220 ppf: 948 ppct: 15 formats: tig: atl: Detecting autism from picture book narratives using deep neural utterance embeddings. aug: au: Wawer, Aleksander Chojnicka, Izabela affil: Institute of Computer Science, Polish Academy of Sciences, Warsaw, Poland sug: subj: Neural Networks (Computer) Autism Spectrum Disorder Diagnosis Narratives Speech Sample Deep Learning Human Algorithms Clinical Assessment Tools Task Performance and Analysis Sensitivity and Specificity Predictive Value of Tests Questionnaires Child Adolescence Adult Communication Skills Evaluation Books Data Management Child: 6-12 years Adolescent: 13-18 years Adult: 19-44 years ab: Background: Deficits in the ability to use language in social contexts, including storytelling skills, are observed across the autism spectrum. Development in machine‐learning approaches may contribute to clinical psychology and psychiatry, given its potential to support decisions concerning the diagnosis and treatment of psychiatric conditions and disorders. Aims: To evaluate the usefulness of deep neural networks for detecting autism spectrum disorder (ASD) from textual utterances, specifically from narrations produced by individuals with ASD. Methods & Procedures: We examined two text encoders: Embeddings from Language Models (ELMo) and Universal Sentence Encoder (USE), and three classification algorithms: XGBoost, support vector machines, and dense neural network layer. We aimed to classify 25 participants with ASD and 25 participants with typical development (TD) based on their narrations produced during the Autism Diagnostic Observation Schedule, Second Edition (ADOS‐2) picture book task. The results of computational approaches were compared with the results of standardized testing and classifications made by two psychiatrists (raters). The raters were asked to read utterances produced by a participant (without an examiner's statements and additional information) and assign a participant to one of the two groups: ASD or with typical development (TD). Outcomes & Results: The computer‐based models had higher sensitivity, specificity, positive predictive values and negative predictive values than the raters, and lower than the two standardized instruments: ADOS‐2 and Social Communication Questionnaire (SCQ). Conclusions & Implications: Our findings lay the groundwork for future studies involving deep neural network‐based text representation models as tools for augmenting the ASD diagnosis or screening. Both ELMo and USE text encoders provided promising specificities, sensitivities, positive predictive values and negative predictive values. Our results indicate the usefulness of page‐level embeddings for utterance representation in ADOS‐2 picture book task. WHAT THIS PAPER ADDS: What is already known on this subject: Deficits in the use of language in social contexts, and narrative ability in particular, are observed across the autism spectrum. Most research on narrative skills has applied hand‐coding methods. Hitherto, machine‐learning methods were used mostly for image recognition problems and data from screening questionnaires for ASD classification. Detection of mental and developmental disorders from textual input is an emerging field for machine and deep‐learning methods. What this paper adds to existing knowledge: This study explored the ability of several types of deep neural network‐based text representation models to detect ASD. Both ELMo and USE provided the most promising values of specificity, sensitivity, positive predictive values and negative predictive values. What are the potential or actual clinical implications of this work?: Competitive accuracy, repeatability, speed and ease of operation are all advantages of computerized methods. They allow for objective and quantitative assessment of narrative ability and complex language skills. Deep neural network‐based text representation models could in the future support clinicians and augment the decision‐making process related to ASD diagnosis, screening and intervention planning. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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