Exploring the Experiences of Parents Whose Child has Received a Diagnosis of Autistic Spectrum Disorder in Adulthood

There is a growing trend of adult diagnosis of Autism Spectrum Disorder (ASD). Research has found that diagnosis can prompt a process of sense-making which may be disrupted by lack of post-diagnostic support. Given the continued involvement of many parents in supporting their adult son or daughter with ASD, it is vital to understand their experiences to meet their needs in adapting to the diagnosis. Eleven parents of recently diagnosed adults participated in semi-structured interviews which were analysed thematically. Findings demonstrate that the new knowledge of diagnosis facilitates changes in attributions, interactions and relationships, but can result in unmet emotional and relational support needs. Findings are relevant to those involved in adult diagnosis, and the provision of post-diagnostic support.

Estimating the Male to Female Ratio in Autistic Spectrum Disorder, Defining and Calculating the Biases and Finding the True Number of Girls.

The ratio of males to females with ASD is generally quoted as 4:1 though it is believed that there are biases preventing females being diagnosed and that the true ratio is lower. These biases have not been clearly identified or quantified. Starting with a clinical dataset of 1711 children <18 years old four different methods were employed in an inductive study to identify and quantify the biases and calculate the proportion of females missed. A mathematical model was constructed to compare the findings with current published data. The true male to female ratio appears to be 3:4. Eighty per cent of females remain undiagnosed at age 18 which has serious consequences for the mental health of young women.

Classification of Autistic Spectrum Disorder Using Deep Neural Network With Particle Swarm Optimization

In this paper, Feature Selection Technique (FST) namely Particle Swarm Optimization (PSO) has been used. The filter based PSO is a search method with Correlation-based Feature Selection (CBFS) as a fitness function. The FST has two key goals of improving classification efficiency and reducing feature counts. Artificial Neural Network (ANN) Based Multilayer Perceptron Network (MLP) and Deep Learning (DL) have been considered the classification methods on 2 benchmark Autistic Spectrum Disorder (ASD) dataset. The experimental result was compared to the non-reduced features and reduced feature of ASD datasets. The reduced feature give up enhanced results in both classifiers MLP and DL. In addition, an experimental study on the exhibitions of these methodologies has been conducted. Finally, a new trend of PSO-MLP and PSO-DL based classification model is proposed.

Autistic Spectrum Disorder and Current Laws in Brazil

The objective of the work was to analyze the autistic spectrum disorder and the current laws in Brazil. In addition to the concept of TEA, a special law composed of a total of 8 (eight) articles establishes the inherent rights of these people, and in its 2nd article, establishes guidelines for national policies, including the intersectoriality of service actions of development; community participation; comprehensive attention to the needs of autistic patients, etc. The type of study is a systematic review, research of this type has the primary objective of exposing the attributes of a given phenomenon or statement among its variables. Thus, it is recommended that it presents characteristics such as: analyzing the atmosphere as a direct source of data and the researcher as a switch instrument; not to broker the use of statistical artifices and methods, having as a greater apprehension the interpretation of phenomena and the imputation of results, the method should be the main focus for the approach and not the result or the fruit, the appreciation of the data should be achieved from intuitively and inductively through the researcher