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

Effectively serving students with autism spectrum disorders (ASDs) requires professionals to possess specialized knowledge, skills, and understanding. When students with ASDs are from culturally or linguistically diverse (CLD) families, the professionals assessing and providing services to the students need the additional dimension of how cultural and linguistic differences may affect identification, assessment, and treatment strategies.

The international conference on Applied Scientific Computational Intelligence using Data Science (ASCI-2020) has been grappled its importance to discuss the research challenges in the field of Data Science by applying scientific techniques in terms of computational intelligence. The initiative has been taken by Department of Computer Applications, Manipal University Jaipur, Rajasthan, India. Manipal University Jaipur (MUJ) was launched in 2011 on an invitation from the Government of Rajasthan, as a self-financed State University. MUJ has redefined academic excellence in the region, with the Manipal way of learning; one that inspires students of all disciplines to learn and innovate through hands on practical experience. In line with Manipal University’s legacy of providing quality education to its students, the campus uses the latest in technology to impart education. Thus, ASCI-2020 drives this legacy of MUJ to join forces and showcase exorbitant research and industry inventiveness, and to recognize and hear from experts in the field of Data Science. Data science is a huge diverted field. Different kind of algorithms, scientific techniques, processes and systems are used in data science to pull out knowledge and insights from Big Data i.e. structured and unstructured data. It is a concept to data analysis; unify statistics, machine learning and their related methods. This conference aims to reveal into advanced methodologies, prototypes, systems, tools, and techniques of data science from academia, industry and government agencies scientists and practitioners. Whether it is information technology or hardware, banking to healthcare, automation and innovation are revolutionary in almost everything. Cities and infrastructure are becoming smarter, health care is being integrated and education is becoming super-focused. The conference will bring together all topics of interest to those who are inclined towards computing and data science using intelligence techniques.ASCI-2020 proceeding has tried to fetch innovative facts and information from academician, research scholars and scientists in terms of their research results and key findings from all the aspects of data science and computational intelligence. The manuscripts of ASCI-2020 has been called for three tracks of data science and computational intelligence. The first track focused on Big Data Management. Then, second track emphasized the Computational Intelligence Techniques, and third track concentrated on Data Science Applications. Subsequently, these tracks has been formulated on the sub themes of Data Science and Computational Intelligence evolved with emerging fields in this present scenario. The sub themes of first track described Heuristic and Nature Inspired Search, Fuzzy and Rough Sets, Reinforcement Learning, ANN and Deep Neural Networks, Auto Encoder, GAN, Transfer Learning, Data Optimization, Data and Network Outsourcing Services. In addition, the second track includes the topics related to Algorithms and Models, Cognitive Computing Development, Business Intelligence and Strategies, Machine Learning and Statistics, Machine Learning Tools and Techniques, Fielded Applications, Generalization as Search, Machine Translation, Data Communication and Intelligence and Natural Language Processing. Finally, track three has been intended on interdisciplinary topics such as Predictive and Statistical Analysis, Application in Computer Vision, Natural Language Processing, Time Series Data, Computational Mathematics, Drug Discovery and Genomic Sequencing, Data Analysis for Improving Defence Security, Cyber Security Analytics, Data Recommendation in Social Networks, Data Analysis of Customer Need in E-commerce, Search Keyword Analysis, Web and Digital Media and Business Analytics in Agriculture.This conference received 235 papers from across the world such as United States, United Kingdom, United Arab Emirates, Norway, Russia, Saudi Arabia, Turkey, Ukraine, Ethiopia, Canada, Morocco, Poland, Uzbekistan, Ghana, Ecuador and Bangladesh etc. Out of 235 manuscripts 91 high quality papers are selected with 38 % accepting ratio. As a whole, 35 international authors submitted their papers, and 350 authors submitted their manuscript from the country. All the manuscripts have been discussed the new findings in the field of data science and computational intelligence. Authors of the proceedings focused on latest trends such as different data models for classification and prediction in various applications. Nevertheless, this proceeding also bring together different theories and paradigm of neural network, fuzzy systems for computational intelligence.List of Editors are available in the pdf.

BackgroundAttention-deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are among the most prevalent mental disorders among school-aged youth in South Korea and may play a role in the increasing pressures on teachers and school-based special education programming. A lack of support for special education; tensions between teachers, students, and parents; and limited backup for teacher absences are common complaints among Korean educators. New innovations in technology to screen and treat ADHD and ASD may offer relief to students, parents, and teachers through earlier and efficient diagnosis; access to treatment options; and ultimately, better-managed care and expectations.ObjectiveThis narrative literature review provides an account of medical device use and development in South Korea for the diagnosis and management of ADHD and ASD and highlights research gaps.MethodsA narrative review was conducted across 4 databases (PubMed, Korean National Assembly Library, Scopus, and PsycINFO). Journal articles, dissertations, and government research and development reports were included if they discussed medical devices for ADHD and ASD. Only Korean or English papers were included. Resources were excluded if they did not correspond to the research objective or did not discuss at least 1 topic about medical devices for ADHD and ASD. Journal articles were excluded if they were not peer reviewed. Resources were limited to publications between 2013 and July 22, 2024.ResultsA total of 1794 records about trends in Korean medical device development were categorized into 2 major groups: digital therapeutics and traditional therapy. Digital therapeutics resulted in 5 subgroups: virtual reality and artificial intelligence, machine learning and robot, gaming and visual contents, eye-feedback and movement intervention, and electroencephalography and neurofeedback. Traditional therapy resulted in 3 subgroups: cognitive behavioral therapy and working memory; diagnosis and rating scale; and musical, literary therapy, and mindfulness-based stress reduction. Digital therapeutics using artificial intelligence, machine learning, and electroencephalography technologies account for the biggest portions of development in South Korea, rather than traditional therapies. Most resources, 94.15% (1689/1794), were from the Korean National Assembly Library.ConclusionsLimitations include small sizes of populations to conclude findings in many articles, a lower number of articles discussing medical devices for ASD, and a majority of articles being dissertations. Emerging digital medical devices and those integrated with traditional therapies are important solutions to reducing the prevalence rates of ADHD and ASD in South Korea by promoting early diagnosis and intervention. Furthermore, their application will relieve pressures on teachers and school-based special education programming by providing direct supporting resources to students with ADHD or ASD. Future development of medical devices for ADHD and ASD is predicted to heavily rely on digital technologies, such as those that sense people’s behaviors, eye movement, and brainwaves.

Tracing the Origins of Autism: A Spectrum of New Studies

The goal of this communication is to provide clinicians and behavioral scientists with a scoping perspective on the diverse array of impacts of the COVID-19 pandemic on individuals with intellectual and developmental disabilities (IDD) in the U.S. It is our hope that this will stimulate subsequent scientific and advocacy efforts to ameliorate the disproportionate burden of the pandemic on people with IDD.We begin with the assertion that among non-infected persons in the U.S. few are more adversely affected by COVID-19 than individuals with IDD, given that a vast proportion require in-person care or critical therapeutic support within their living environments, with little back-up or systematic coverage for prolonged interruption of services.Many have temporarily lost access to trained caregivers or community service providers, and now face evolving threats to the return of baseline service, given uncertainties in State and agency budgets.Therefore, a first priority relates to restoration of in-person support services or comparable alternatives.There have been emerging guidelines on the safe care and support of individuals with IDD during the COVID pandemic-see Supplementary Table (ST) 1 which lists resources and documentation of early success of such strategies, however guidance is still evolving, has not permeated all reaches of the community where the information is desperately needed, and is not always presented in ways that can be fully comprehended by those with IDD.It must be

Status and Potential of Community-Engaged Research to Investigate Physical Activity Interventions for Children with Autism Spectrum Disorder in Chinese-American Communities

PurposeData science is a relatively new field which has gained considerable attention in recent years. This new field requires a wide range of knowledge and skills from different disciplines including mathematics and statistics, computer science and information science. The purpose of this paper is to present the results of the study that explored the field of data science from the library and information science (LIS) perspective.Design/methodology/approachAnalysis of research publications on data science was made on the basis of papers published in the Web of Science database. The following research questions were proposed: What are the main tendencies in publication years, document types, countries of origin, source titles, authors of publications, affiliations of the article authors and the most cited articles related to data science in the field of LIS? What are the main themes discussed in the publications from the LIS perspective?FindingsThe highest contribution to data science comes from the computer science research community. The contribution of information science and library science community is quite small. However, there has been continuous increase in articles from the year 2015. The main document types are journal articles, followed by conference proceedings and editorial material. The top three journals that publish data science papers from the LIS perspective are the Journal of the American Medical Informatics Association, the International Journal of Information Management and the Journal of the Association for Information Science and Technology. The top five countries publishing are USA, China, England, Australia and India. The most cited article has got 112 citations. The analysis revealed that the data science field is quite interdisciplinary by nature. In addition to the field of LIS the papers belonged to several other research areas. The reviewed articles belonged to the six broad categories: data science education and training; knowledge and skills of the data professional; the role of libraries and librarians in the data science movement; tools, techniques and applications of data science; data science from the knowledge management perspective; and data science from the perspective of health sciences.Research limitations/implicationsThe limitations of this research are that this study only analyzed research papers in the Web of Science database and therefore only covers a certain amount of scientific papers published in the field of LIS. In addition, only publications with the term “data science” in the topic area of the Web of Science database were analyzed. Therefore, several relevant studies are not discussed in this paper that are not reflected in the Web of Science database or were related to other keywords such as “e-science,” “e-research,” “data service,” “data curation” or “research data management.”Originality/valueThe field of data science has not been explored using bibliographic analysis of publications from the perspective of the LIS. This paper helps to better understand the field of data science and the perspectives for information professionals.

Highlights of the 1st Latin American Conference of Women in Bioinformatics and Data Science

Data science is an interdisciplinary field that deals with a methodical approach to process large volumes of data both structured and unstructured in nature. The very objective is to analyze the data to uncover hidden patterns and extract actionable insights from the data for better managerial decision-making in an organization. Data science has been used in diverse areas such as business and finance, marketing, risk management, operations and planning, disease diagnosis and health care, agriculture, fraud detection, crime investigation, image and speech recognition, gaming, virtual reality, weather and environmental studies, space and defense applications to name a few. Data science is not an entirely new discipline; rather, it has evolved from the existing fields such as data mining and knowledge discovery, business intelligence, data analytics, machine learning, computer science, software engineering, mathematics and statistics, among others. It is an umbrella field to many such fields which make data processing more systematic than ever before and very useful for organizational decision-making. Data science has a lot of potentials to solve complex organizational problems effectively. With the growth of social media, Internet of Things, ubiquitous computing, connectivity, ambient intelligence and above all digital economy, the field of big data has emerged as an opportunity as well as a challenge for many organizations. While big data stores a lot of business opportunities, how to make it useful for the organization is rather challenging. In this context, embracing data science becomes more pertinent for the organization. With the advent of big data, the importance and popularity of data science is accelerating. This chapter will provide a compressive introduction to data science and big data analytics. It will elaborate on the data analytics life cycle. The chapter will delve into the theories and methods such as regression, classification, clustering and association rules used in data science. It will also introduce the relevant technologies such as MapReduce, NoSQL and popular tools such as Hadoop ecosystem. Finally, this chapter will conclude with research challenges in the field of data science and big data analytics.

Autism spectrum disorder is a neurodevelopmental condition characterized by social communication difficulties and restricted repetitive behaviors. Individuals with autism spectrum disorder are often diagnosed with other psychiatric conditions, including attention deficit hyperactivity disorder, anxiety, and depression. However, research on post-traumatic stress disorder among individuals with autism spectrum disorder is scarce. Nonetheless, studies have shown that those with autism spectrum disorder may face an increased risk of exposure to traumatic events. Separate lines of research in autism spectrum disorder and post-traumatic stress disorder have shown that the two may share several vulnerability factors. One of those is ruminative thinking, that is, one's tendency to re-hash thoughts and ideas, in a repetitive manner. This article examined the role of two rumination types as potential factors connecting autism spectrum disorder and post-traumatic stress disorder: brooding (continuously comparing one's current condition to one's desired condition) and reflection (an introspective effort to cognitively solve one's problems). A total of 34 adults with autism spectrum disorder (with no intellectual impairment) and 66 typically developing adults completed questionnaires assessing post-traumatic stress disorder symptoms and rumination. The results showed increased post-traumatic stress disorder symptoms in adults with autism spectrum disorder, compared to typically developing adults. Brooding rumination was also higher among those with autism spectrum disorder. Finally, brooding, but not reflection, served as a mechanism connecting autism spectrum disorder and post-traumatic stress disorder, that is, those with autism spectrum disorder showed increased brooding, which in turn predicted more post-traumatic stress disorder symptoms. This study has potential clinical implications. Rumination and cognitive inflexibility, which are common in autism spectrum disorder, could exacerbate post-traumatic symptoms among individuals with autism spectrum disorder who experience traumatic events. Interventions targeting brooding rumination and cognitive flexibility may assist in alleviating post-traumatic symptoms in individuals with autism spectrum disorder.

Children can transmit severe acute respiratory syndrome coronavirus 2 and, although at a lower risk, can experience serious outcomes from infection. Vaccinating children against coronavirus disease 2019 (COVID-19) is essential to protecting their health and establishing higher population immunity. In 2015–2017, 1 in 6 children aged 3 to 17 years had a developmental disability (DD) such as cerebral palsy, autism spectrum disorder (ASD), or intellectual disability (ID).1 DDs are a diverse group of chronic conditions that begin in childhood and can impact functioning throughout life. Despite limited data in public health surveillance systems, in some evidence, it is suggested that some children with DDs might be disproportionately affected by COVID-19, both by the illness itself and the pandemic's impact on receipt of services. Children with DDs often have medical conditions that contribute to higher risk for severe illness from COVID-19,2 can experience barriers to accessing needed health care, and can possess other characteristics increasing their risk from COVID-19, including limited mobility, direct care requirements, and challenges practicing preventive measures and communicating illness symptoms.3 We describe the limited available data relevant for children with DDs and highlight other considerations for COVID-19 vaccination.In a cross-sectional study of >64 million US patients of all ages, COVID-19 incidence was >3 times higher among people with an ID than those without one.4 Among those with COVID-19, twice as many people with an ID were hospitalized, admitted to the ICU, or died, compared with those without an ID. In analyses adjusting for age and comorbidities, ID was the strongest risk factor for COVID-19 diagnosis, and the odds of mortality were almost 6 times higher among patients with COVID-19 who had an ID, compared with those without an ID.Private insurance claims data from the FAIR Health database revealed similar results among patients of all ages for other DDs.5 Among people with COVID-19, those with ASD, ID, learning disabilities, and attention-deficit/hyperactivity-disorder had ∼3 to 9 times higher likelihood of hospitalization (adjusted for age and sex) than those without these conditions and longer hospitalizations. Those who had a DD had an approximately threefold higher odds of mortality than those without.6 Data on 43 465 children ≤18 years revealed that from March 2020 to January 2021 children with neurodevelopmental disorders were 1.6 times as likely to be hospitalized with COVID-19 than children without neurodevelopmental disorders, although severe illness among children hospitalized for COVID-19 with neurodevelopmental disorders was less common than among children with other conditions.7 An analysis of 30 282 patients with COVID-19 from the TriNetX COVID-19 Research Network revealed the COVID-19 fatality rate among children <18 years with ID and other DDs was 13 times higher than among children without these conditions. However, these rates were based on only 2 fatalities among 125 children with DD and 1 fatality among 791 children without DD.2Limitations of the available data should be considered. It is challenging to compare results across studies because of variability in types, severity, and definitions of DDs, which can lead to heterogeneity in risk. In few studies did researchers examine children specifically, and not all researchers account for underlying medical conditions or other confounders. In many studies, researchers used administrative health care data, which are not generalizable to all patients (eg, includes only commercial insurance) and rely on diagnostic or other billing codes, which might result in misclassification of DDs or of COVID-19 as the reason for health care use (versus incidental finding). Finally, not all analyses have undergone peer review.Data on influenza vaccination can be used to inform potential challenges to COVID-19 vaccination among children with DDs. Despite many children with DDs being considered high-risk for influenza complications, vaccination rates in this population are consistently low (see Supplemental Table 1). Reasons for these suboptimal vaccination rates might be related to limited knowledge about the increased risk for severe outcomes, access barriers, or vaccine hesitancy.In an online survey of parents of children with an ID or other neurologic disorder, it was found that their most important source of information regarding vaccines was their child's health care provider.8 However, in a companion survey of physicians likely to treat these children, <50% recognized ID as a high-risk condition for influenza.8Data from studies before 2020 reveal that children with ASD have lower rates of influenza and other vaccinations compared with that of children without ASD, and that parents of children with ASD have higher rates of vaccine hesitancy than parents of children with other DDs or no DDs (see Supplemental Table 1).Although vaccinating siblings of children with DDs might help to reduce COVID-19 transmission within households, data reveal that siblings of children with DDs have lower vaccination rates than their siblings with DDs, and siblings of children with DDs have a lower prevalence of on-time vaccination and a higher prevalence of parent vaccine refusal than siblings of children without DDs (see Supplemental Table 1).People with DDs face long-standing systemic health and social inequities. Children with DDs have greater health care and community-based service use than children without DDs9 yet are more likely to have unmet health care needs. Additional challenges were raised by the COVID-19 pandemic. In a survey of >3000 caregivers of children with ASD, 62% reported moderate to severe negative impacts of COVID-19–related disruption in services on their child's ASD symptoms, and 72% reported experiencing moderate to extreme stress due to these disruptions.10 School closings to in-person learning and modified remote learning options have kept children within the home more, isolating and essentially resegregating many children with DDs from their peers, despite the Individuals with Disabilities Education Act mandate that children with disabilities be educated with children without disabilities to the maximum extent possible in the least restrictive environment. Prioritizing adolescents with DDs aged ≥12 years for COVID-19 vaccination and children aged <12 years when vaccines are authorized for use in this age group is essential to resuming needed educational services within the school setting.Pediatricians can work with other providers to tailor COVID-19 vaccination efforts for children with DDs to overcome issues of access and hesitancy. Children with DDs may have more interactions with health care or other service specialists than with general pediatricians. Collaboration between pediatric hospital systems, pediatric specialists, disability-specific practices and clinics, and occupational, physical, or speech specialists with knowledge of the specific needs of the children with DD in their community will be important for successful vaccination implementation. COVID-19 vaccination can be provided in ways that are easier for children with DDs to accept, such as the option to be vaccinated in their vehicle or quiet areas. Some children with DDs may require more time or sensory modifications during vaccination appointments. In addition, some children with DDs may be unable to wear masks or practice physical distancing, limiting their ability to receive services at many locations. Trusted care providers can work with parents to learn and address specific concerns with vaccination. Collaboration with schools may facilitate parental education and/or leverage school-based clinics.In conclusion, children with DDs are likely at a higher risk of COVID-19 illness because of increased prevalence of underlying health conditions, suboptimal vaccination rates, and systemic inequities. Strategies can be implemented and supported by pediatricians to ensure that children with DDs, their caregivers, family members, and service providers receive the COVID-19 vaccine to reduce negative outcomes. Highlighting the unique considerations for COVID-19 vaccination for children with DDs can support equitable access of vaccination for children with DDs and their families.

Studies of families ascertained through a single autistic proband suggest that the genetic liability for autism may be expressed in nonautistic relatives in a phenotype that is milder but qualitatively similar to the defining features of autism. The objective of this study was to examine behaviors that may define this broader phenotype in relatives ascertained through two autistic siblings. The authors used a semistructured family history interview to compare the rates of social and communication deficits and stereotyped behaviors in relatives ascertained through two autistic siblings (families with multiple-incidence autism; 25 families) with the rates in relatives of Down syndrome probands (30 families). Higher rates of social and communication deficits and stereotyped behaviors were found in the relatives in the families with multiple-incidence autism. These data suggest that further studies should be undertaken to delineate the boundaries of the broader autism phenotype and that this broader phenotype should be included in some future genetic analyses of this disorder.

Abstract: This study presents a comprehensive analysis of the application of machine learning techniques for the prediction of autism spectrum disorder (ASD). The dataset used in this research comprises a range of demographic, behavioral, and diagnostic features. The study focuses on the use of various machine learning algorithms, including limited decision trees, support vector machines, and neural networks, to predict the likelihood of ASD in individuals. In addition, engineering and feature selection strategies are investigated to determine the most pertinent characteristics for precise prediction. Metrics like accuracy, sensitivity, specificity, and area under the receiver operating characteristic (ROC) curve are used to assess how well various algorithms perform. Results show promising potential for the utilization of machine learning models in predicting ASD, with certain algorithms exhibiting superior predictive capabilities. The findings of this study provide valuable insights into the potential use of machine learning in the early detection and intervention of autism, ultimately contributing to improved outcomes for individuals on the autism spectrum

Data Science is a field that has developed rapidly in recent years, which utilizes technology and data analysis methods to produce useful information for various fields. In this paper, we will explain the latest trends in the field of Data Science and their role in various fields, including the fields of business, health, finance, and the public sector and discuss the technologies and algorithms used in Data Science, such as Machine Learning, Natural language Processing., data visualization, Big Data Analytics, and provides examples of how this technology can be used to solve problems in various fields. This paper aims to explore that Data Science has a very important role in various fields, and will continue to develop along with technological developments. The literature study methodology was carried out to collect data and information related to this topic, which aims to ensure that the data analysis carried out is accurate, and can be trusted. The results of the study found that Data Science science and technology has helped a number of parties with its application to certain aspects related to their fields. Thus, this paper can be a useful source of information for professionals and researchers who are interested in Data Science and its role in various fields.

Autism Spectrum Disorder(ASD) is a type of neurological disorder that is common among children. The diagnosis of this disorder at an early stage is the key to reducing its effects. The major symptoms include anxiety, lack of communication, and less social interaction. This paper presents a systematic review conducted based on PRISMA guidelines for automated diagnosis of ASD. With rapid development in the field of Data Science, numerous methods have been proposed that can diagnose the disease at an early stage which can minimize the effects of the disorder. Machine learning and deep learning have proven suitable techniques for the automated diagnosis of ASD. These models have been developed on various datasets such as ABIDE I and ABIDE II, a frequently used dataset based on rs-fMRI images. Approximately 26 articles have been reviewed after the screening process. The paper highlights a comparison between different algorithms used and their accuracy as well. It was observed that most researchers used DL algorithms to develop the ASD detection model. Different accuracies were recorded with a maximum accuracy close to 0.99. Recommendations for future work have also been discussed in a later section. This analysis derived a conclusion that AI-emerged DL and ML technologies can diagnose ASD through rs-fMRI images with maximum accuracy. The comparative analysis has been included to show the accuracy range.