Data Mining Techniques to Study Therapy Success with Autistic Children

Autism spectrum disorder has become one of the most prevalent developmental disorders, characterized by a wide variety of symptoms. Many children need extensive therapy for years to improve their behavior and facilitate integration in society. However, few systematic evaluations are done on a large scale that can provide insights into how, where, and how therapy has an impact. We describe how data mining techniques can be used to provide insights into behavioral therapy as well as its effect on participants. To this end, we are developing a digital library of coded video segments that contains data on appropriate and inappropriate behavior of autistic children in different social settings during different stages of therapy and. In general, we found that therapy increased appropriate behavior and decreased inappropriate behavior. Decision trees and association rules provided more detailed insights for high and low levels of appropriate and inappropriate behavior. We found that a child\u27s interaction with a parent or therapist led to especially high levels of appropriate behavior and behavior is most predictable while therapy is in progress

The Key Artificial Intelligence Technologies in Early Childhood Education: A Review

Artificial Intelligence (AI) technologies have been applied in various domains, including early childhood education (ECE). Integration of AI educational technology is a recent significant trend in ECE. Currently, there are more and more studies of AI in ECE. To date, there is a lack of survey articles that discuss the studies of AI in ECE. In this paper, we provide an up-to-date and in-depth overview of the key AI technologies in ECE that provides a historical perspective, summarizes the representative works, outlines open questions, discusses the trends and challenges through a detailed bibliometric analysis, and provides insightful recommendations for future research. We mainly discuss the studies that apply AI-based robots and AI technologies to ECE, including improving the social interaction of children with an autism spectrum disorder. This paper significantly contributes to provide an up-to-date and in-depth survey that is suitable as introductory material for beginners to AI in ECE, as well as supplementary material for advanced users.Comment: 39 pages, 9 figures, 4 table

Affective Medicine: a review of Affective Computing efforts in Medical Informatics

Background: Affective computing (AC) is concerned with emotional interactions performed with and through computers. It is defined as “computing that relates to, arises from, or deliberately influences emotions”. AC enables investigation and understanding of the relation between human emotions and health as well as application of assistive and useful technologies in the medical domain. Objectives: 1) To review the general state of the art in AC and its applications in medicine, and 2) to establish synergies between the research communities of AC and medical informatics. Methods: Aspects related to the human affective state as a determinant of the human health are discussed, coupled with an illustration of significant AC research and related literature output. Moreover, affective communication channels are described and their range of application fields is explored through illustrative examples. Results: The presented conferences, European research projects and research publications illustrate the recent increase of interest in the AC area by the medical community. Tele-home healthcare, AmI, ubiquitous monitoring, e-learning and virtual communities with emotionally expressive characters for elderly or impaired people are few areas where the potential of AC has been realized and applications have emerged. Conclusions: A number of gaps can potentially be overcome through the synergy of AC and medical informatics. The application of AC technologies parallels the advancement of the existing state of the art and the introduction of new methods. The amount of work and projects reviewed in this paper witness an ambitious and optimistic synergetic future of the affective medicine field

Mobile Communication and Data Gathering Software for Autistic Children and Their Caregivers

Positive design leads to positive change in our society. In most cases, discussions focus on those who receive the design. However, positive design may also have a positive, but often over-looked, effect on the designers themselves. Learning about difficulties others face and developing solutions is a benefit that can contribute to individual designers’ education and general sense of well-being. Having a broader understanding of alternative views and lifestyles makes one a better person. In addition, positive design may benefit the entire field of information science by improving its ability to renew itself and attract new, young talent

Using Twitter to learn about the autism community

Considering the raising socio-economic burden of autism spectrum disorder (ASD), timely and evidence-driven public policy decision making and communication of the latest guidelines pertaining to the treatment and management of the disorder is crucial. Yet evidence suggests that policy makers and medical practitioners do not always have a good understanding of the practices and relevant beliefs of ASD-afflicted individuals' carers who often follow questionable recommendations and adopt advice poorly supported by scientific data. The key goal of the present work is to explore the idea that Twitter, as a highly popular platform for information exchange, could be used as a data-mining source to learn about the population affected by ASD -- their behaviour, concerns, needs etc. To this end, using a large data set of over 11 million harvested tweets as the basis for our investigation, we describe a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.Comment: Social Network Analysis and Mining, 201

A Narrative Review about Autism Spectrum Disorders and Exclusion of Gluten and Casein from the Diet

This research received funding from CTS549 group of Psiquiatry and Neurosciences from the University of Granada.Objective: Autism spectrum disorders (ASDs) appear in the early stages of neurodevelopment, and they remain constant throughout life. Currently, due to limitations in ASDs treatment, alternative approaches, such as nutritional interventions, have frequently been implemented. The aim of this narrative review is to gather the most relevant and updated studies about dietary interventions related to ASDs etiopathogenesis. Results: Our literature search focused on the gluten- and casein-free (GFCF) diet. The literature found shows the inexistence of enough scientific evidence to support a general recommendation of dietary intervention in children with ASD. Protocols and procedures for assessing risk and safety are also needed. Future lines: Prospective and controlled research studies with larger sample sizes and longer follow-up times are scarce and needed. In addition, studies considering an assessment of intestinal permeability, bacterial population, enzymatic, and inflammatory gastrointestinal activity are interesting to identify possible responders. Besides brain imaging techniques, genetic tests can also contribute as markers to evaluate the comorbidity of gastrointestinal symptoms.University of Granada CTS54

Computational Approaches to Modeling Speaker State in the Medical Domain

Recently, researchers in computer science and engineering have begun to explore the possibility of finding speech-based correlates of various medical conditions using automatic, computational methods. If such language cues can be identified and quantified automatically, this information can be used to support diagnosis and treatment of medical conditions in clinical settings and to further fundamental research in understanding cognition. This chapter reviews computational approaches that explore communicative patterns of patients who suffer from medical conditions such as depression, autism spectrum disorders, schizophrenia, and cancer. There are two main approaches discussed: research that explores features extracted from the acoustic signal and research that focuses on lexical and semantic features. We also present some applied research that uses computational methods to develop assistive technologies. In the final sections we discuss issues related to and the future of this emerging field of research