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      Risk Assessment for Parents Who Suspect Their Child Has Autism Spectrum Disorder: Machine Learning Approach

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          Abstract

          Background

          Parents are likely to seek Web-based communities to verify their suspicions of autism spectrum disorder markers in their child. Automated tools support human decisions in many domains and could therefore potentially support concerned parents.

          Objective

          The objective of this study was to test the feasibility of assessing autism spectrum disorder risk in parental concerns from Web-based sources, using automated text analysis tools and minimal standard questioning.

          Methods

          Participants were 115 parents with concerns regarding their child’s social-communication development. Children were 16- to 30-months old, and 57.4% (66/115) had a family history of autism spectrum disorder. Parents reported their concerns online, and completed an autism spectrum disorder-specific screener, the Modified Checklist for Autism in Toddlers-Revised, with Follow-up (M-CHAT-R/F), and a broad developmental screener, the Ages and Stages Questionnaire (ASQ). An algorithm predicted autism spectrum disorder risk using a combination of the parent's text and a single screening question, selected by the algorithm to enhance prediction accuracy.

          Results

          Screening measures identified 58% (67/115) to 88% (101/115) of children at risk for autism spectrum disorder. Children with a family history of autism spectrum disorder were 3 times more likely to show autism spectrum disorder risk on screening measures. The prediction of a child’s risk on the ASQ or M-CHAT-R was significantly more accurate when predicted from text combined with an M-CHAT-R question selected (automatically) than from the text alone. The frequently automatically selected M-CHAT-R questions that predicted risk were: following a point, make-believe play, and concern about deafness.

          Conclusions

          The internet can be harnessed to prescreen for autism spectrum disorder using parental concerns by administering a few standardized screening questions to augment this process.

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          Most cited references31

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          Identification and evaluation of children with autism spectrum disorders.

          Autism spectrum disorders are not rare; many primary care pediatricians care for several children with autism spectrum disorders. Pediatricians play an important role in early recognition of autism spectrum disorders, because they usually are the first point of contact for parents. Parents are now much more aware of the early signs of autism spectrum disorders because of frequent coverage in the media; if their child demonstrates any of the published signs, they will most likely raise their concerns to their child's pediatrician. It is important that pediatricians be able to recognize the signs and symptoms of autism spectrum disorders and have a strategy for assessing them systematically. Pediatricians also must be aware of local resources that can assist in making a definitive diagnosis of, and in managing, autism spectrum disorders. The pediatrician must be familiar with developmental, educational, and community resources as well as medical subspecialty clinics. This clinical report is 1 of 2 documents that replace the original American Academy of Pediatrics policy statement and technical report published in 2001. This report addresses background information, including definition, history, epidemiology, diagnostic criteria, early signs, neuropathologic aspects, and etiologic possibilities in autism spectrum disorders. In addition, this report provides an algorithm to help the pediatrician develop a strategy for early identification of children with autism spectrum disorders. The accompanying clinical report addresses the management of children with autism spectrum disorders and follows this report on page 1162 [available at www.pediatrics.org/cgi/content/full/120/5/1162]. Both clinical reports are complemented by the toolkit titled "Autism: Caring for Children With Autism Spectrum Disorders: A Resource Toolkit for Clinicians," which contains screening and surveillance tools, practical forms, tables, and parent handouts to assist the pediatrician in the identification, evaluation, and management of autism spectrum disorders in children.
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            Validation of the modified checklist for Autism in toddlers, revised with follow-up (M-CHAT-R/F).

            This study validates the Modified Checklist for Autism in Toddlers, Revised with Follow-up (M-CHAT-R/F), a screening tool for low-risk toddlers, and demonstrates improved utility compared with the original M-CHAT. Toddlers (N = 16,071) were screened during 18- and 24-month well-child care visits in metropolitan Atlanta and Connecticut. Parents of toddlers at risk on M-CHAT-R completed follow-up; those who continued to show risk were evaluated. The reliability and validity of the M-CHAT-R/F were demonstrated, and optimal scoring was determined by using receiver operating characteristic curves. Children whose total score was ≥ 3 initially and ≥ 2 after follow-up had a 47.5% risk of being diagnosed with autism spectrum disorder (ASD; confidence interval [95% CI]: 0.41-0.54) and a 94.6% risk of any developmental delay or concern (95% CI: 0.92-0.98). Total score was more effective than alternative scores. An algorithm based on 3 risk levels is recommended to maximize clinical utility and to reduce age of diagnosis and onset of early intervention. The M-CHAT-R detects ASD at a higher rate compared with the M-CHAT while also reducing the number of children needing the follow-up. Children in the current study were diagnosed 2 years younger than the national median age of diagnosis. The M-CHAT-R/F detects many cases of ASD in toddlers; physicians using the 2-stage screener can be confident that most screen-positive cases warrant evaluation and referral for early intervention. Widespread implementation of universal screening can lower the age of ASD diagnosis by 2 years compared with recent surveillance findings, increasing time available for early intervention.
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              Parenthood, information and support on the internet. A literature review of research on parents and professionals online

              Background The aim of this article was to address questions on how parents use the internet to find information and support regarding children, health and family life. Another aim was to find out how professionals use the internet to provide support and information to parents. This was done by a literature review. Methods Articles were searched for in five databases with a search strategy called "building block" approach. Results The review showed that the majority of today's parents search for both information and social support on the internet. However, there are considerable differences due to gender, age and socio-economic differences. First time middle class mothers aged 30–35 are most active in looking up health and parent information on the internet. In the same time, several studies report diminishing class differences on parent web sites. An important reason to the increasing number of parents who turn to the internet for information and interaction has shown to be the weakened support many of today's parents experience from their own parents, relatives and friends. Professionals have recognized the parents' great interest for going online and offer both information and support on the net. Conclusion Many benefits are reported, for example the possibility to reach out to a wider audience and to increase access to organisations without an increase in costs. Other benefits include the possibility for parents to remain anonymous in their contacts with professionals and that parents' perceived need for information can be effectively met around the clock. Interventions for wider groups of parents, such as parent training on the net, are still very rare and more research is needed to evaluate different types of interventions on the net. However, most studies were empirical and lacked theoretical frameworks which leave questions on how we can more fully understand this phenomenon unanswered.
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                Author and article information

                Contributors
                Journal
                J Med Internet Res
                J. Med. Internet Res
                JMIR
                Journal of Medical Internet Research
                JMIR Publications (Toronto, Canada )
                1439-4456
                1438-8871
                April 2018
                24 April 2018
                : 20
                : 4
                : e134
                Affiliations
                [1] 1 Department of Occupational Therapy, Faculty of Social Welfare and Health Sciences University of Haifa Haifa Israel
                [2] 2 AJ Drexel Autism Institute Drexel University Philadelphia, PA United States
                [3] 3 Microsoft Research Herzelia Israel
                Author notes
                Corresponding Author: Ayelet Ben-Sasson asasson@ 123456univ.haifa.ac.il
                Author information
                http://orcid.org/0000-0003-2677-9414
                http://orcid.org/0000-0002-7791-7031
                http://orcid.org/0000-0002-2380-4584
                Article
                v20i4e134
                10.2196/jmir.9496
                5941093
                29691210
                867c0e73-146e-4383-81f9-8c3e9fb4af1f
                ©Ayelet Ben-Sasson, Diana L Robins, Elad Yom-Tov. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 24.04.2018.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

                History
                : 30 November 2017
                : 18 January 2018
                : 4 February 2018
                : 16 February 2018
                Categories
                Original Paper
                Original Paper

                Medicine
                autistic disorder,early diagnosis,screening,parents,child,expression of concern,technology,machine learning

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