Overcoming data scarcity of Twitter: using tweets as bootstrap with application to autism-related topic content analysis

Notwithstanding recent work which has demonstrated the potential of using Twitter messages for content-specific data mining and analysis, the depth of such analysis is inherently limited by the scarcity of data imposed by the 140 character tweet limit. In this paper we describe a novel approach for targeted knowledge exploration which uses tweet content analysis as a preliminary step. This step is used to bootstrap more sophisticated data collection from directly related but much richer content sources. In particular we demonstrate that valuable information can be collected by following URLs included in tweets. We automatically extract content from the corresponding web pages and treating each web page as a document linked to the original tweet show how a temporal topic model based on a hierarchical Dirichlet process can be used to track the evolution of a complex topic structure of a Twitter community. Using autism-related tweets we demonstrate that our method is capable of capturing a much more meaningful picture of information exchange than user-chosen hashtags.Comment: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, 201

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

Longitudinal EEG power in the first postnatal year differentiates autism outcomes

An aim of autism spectrum disorder (ASD) research is to identify early biomarkers that inform ASD pathophysiology and expedite detection. Brain oscillations captured in electroencephalography (EEG) are thought to be disrupted as core ASD pathophysiology. We leverage longitudinal EEG power measurements from 3 to 36 months of age in infants at low- and high-risk for ASD to test how and when power distinguishes ASD risk and diagnosis by age 3-years. Power trajectories across the first year, second year, or first three years postnatally were submitted to data-driven modeling to differentiate ASD outcomes. Power dynamics during the first postnatal year best differentiate ASD diagnoses. Delta and gamma frequency power trajectories consistently distinguish infants with ASD diagnoses from others. There is also a developmental shift across timescales towards including higher-frequency power to differentiate outcomes. These findings reveal the importance of developmental timing and trajectory in understanding pathophysiology and classifying ASD outcomes.R01 DC010290 - NIDCD NIH HHS; T32 MH112510 - NIMH NIH HHS; U54 HD090255 - NICHD NIH HHSPublished versio

Children With Autism Spectrum Disorder at a Pediatric Hospital: A Systematic Review of the Literature

This review of literature describes the behaviors of hospitalized children with autism spectrum disorder (ASD) that health care providers find challenging. It also identifies strategies used to address these challenging behaviors. The systematic review of literature identified 34 articles from databases on health care of challenging behaviors of children with ASD. The review identified four categories of challenging behaviors (non-compliance, hyperactivity, sensory defensiveness, self-injury) and several strategies for reducing these behaviors. Partnering with parents to develop strategies is important for children with ASD to deliver timely and safe care

Early intervention for obsessive compulsive disorder : An expert consensus statement

© 2019 Elsevier B.V.and ECNP. All rights reserved.Obsessive-compulsive disorder (OCD) is common, emerges early in life and tends to run a chronic, impairing course. Despite the availability of effective treatments, the duration of untreated illness (DUI) is high (up to around 10 years in adults) and is associated with considerable suffering for the individual and their families. This consensus statement represents the views of an international group of expert clinicians, including child and adult psychiatrists, psychologists and neuroscientists, working both in high and low and middle income countries, as well as those with the experience of living with OCD. The statement draws together evidence from epidemiological, clinical, health economic and brain imaging studies documenting the negative impact associated with treatment delay on clinical outcomes, and supporting the importance of early clinical intervention. It draws parallels between OCD and other disorders for which early intervention is recognized as beneficial, such as psychotic disorders and impulsive-compulsive disorders associated with problematic usage of the Internet, for which early intervention may prevent the development of later addictive disorders. It also generates new heuristics for exploring the brain-based mechanisms moderating the ‘toxic’ effect of an extended DUI in OCD. The statement concludes that there is a global unmet need for early intervention services for OC related disorders to reduce the unnecessary suffering and costly disability associated with under-treatment. New clinical staging models for OCD that may be used to facilitate primary, secondary and tertiary prevention within this context are proposed.Peer reviewe

A Machine Learning and Integration Based Architecture for Cognitive Disorder Detection Used for Early Autism Screening

About 15% of the world’s population suffers from some form of disability. In developed countries, about 1.5% of children are diagnosed with autism. Autism is a developmental disorder distinguished mainly by impairments in social interaction and communication and by restricted and repetitive behavior. Since the cause of autism is still unknown, there have been many studies focused on screening for autism based on behavioral features. Thus, the main purpose of this paper is to present an architecture focused on data integration and analytics, allowing the distributed processing of input data. Furthermore, the proposed architecture allows the identification of relevant features as well as of hidden correlations among parameters. To this end, we propose a methodology able to integrate diverse data sources, even data that are collected separately. This methodology increases the data variety which can lead to the identification of more correlations between diverse parameters. We conclude the paper with a case study that used autism data in order to validate our proposed architecture, which showed very promising results.This work was partially funded by Grant RTI2018-094283-B-C32, ECLIPSE-UA (Spanish Ministry of Education and Science)