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

A new diagnostic autism spectrum disorder (DASD) strategy using ensemble diagnosis methodology based on blood tests

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental disease that impacts a child's way of behavior and social communication. In early childhood, children with ASD typically exhibit symptoms such as difficulty in social interaction, limited interests, and repetitive behavior. Although there are symptoms of ASD disease, most people do not understand these symptoms and therefore do not have enough knowledge to determine whether or not a child has ASD. Thus, early detection of ASD children based on accurate diagnosis model based on Artificial Intelligence (AI) techniques is a critical process to reduce the spread of the disease and control it early. Through this paper, a new Diagnostic Autism Spectrum Disorder (DASD) strategy is presented to quickly and accurately detect ASD children. DASD contains two layers called Data Filter Layer (DFL) and Diagnostic Layer (DL). Feature selection and outlier rejection processes are performed in DFL to filter the ASD dataset from less important features and incorrect data before using the diagnostic or detection method in DL to accurately diagnose the patients. In DFL, Binary Gray Wolf Optimization (BGWO) technique is used to select the most significant set of features while Binary Genetic Algorithm (BGA) technique is used to eliminate invalid training data. Then, Ensemble Diagnosis Methodology (EDM) as a new diagnostic technique is used in DL to quickly and precisely diagnose ASD children. In this paper, the main contribution is EDM that consists of several diagnostic models including Enhanced K-Nearest Neighbors (EKNN) as one of them. EKNN represents a hybrid technique consisting of three methods called K-Nearest Neighbors (KNN), Naïve Bayes (NB), and Chimp Optimization Algorithm (COA). NB is used as a weighed method to convert data from feature space to weight space. Then, COA is used as a data generation method to reduce the size of training dataset. Finally, KNN is applied on the reduced data in weight space to quickly and accurately diagnose ASD children based on new training dataset with small size. ASD blood tests dataset is used to test the proposed DASD strategy against other recent strategies [1]. It is concluded that the DASD strategy is superior to other strategies based on many performance measures including accuracy, error, recall, precision, micro_average precision, macro_average precision, micro_average recall, macro_average recall, F1-measure, and implementation-time with values equal to 0.93, 0.07, 0.83, 0.82, 0.80, 0.83, 0.79, 0.81, 0.79, and 1.5 s respectively.

Who cares? A scoping review about the experiences of parental caregivers of autistic adults

BACKGROUND

The autistic population is growing and ageing and this also applies to the parents. Despite this, research about parental experiences is still relatively scarce. Even though studies show, compared with adults with other disabilities, parents of autistic adolescents and adults report a decrease in well-being and that caregiving is often a lifelong commitment.

METHOD

This scoping review maps, synthesises and identifies gaps in previous research as regards the experiences of parental caregivers of adult autistic children. The review was based on Arksey and Malley's framework and six databases were searched.

RESULTS

The results show how the research has focused on the well-being and consequences of daily caregiving for an American middle-class mother who co-resides with an adult son.

CONCLUSION

To enhance knowledge about formal services and service needs, more research is necessary on different welfare regimes and social contexts.

"Peas in a pod": Oral History Reflections on Autistic Identity in Family and Community by Late-Diagnosed Adults

In this paper, we report on a participatory oral history study documenting the lives of late-diagnosed autistic adults in Australia. We interviewed 26 autistic adults about their life history and the impact of late diagnosis. All were diagnosed after the age of 35, growing up in an era when autism was not well known. Using reflexive thematic analysis, we uncovered a rich body of reflections on shared Autistic identity and identified three major themes within that data set: 'conceptualising the Autistic family', 'creating Autistic community', and 'contesting Autistic identity'. Overall, the study provides insights into the active creation of shared Autistic identity and the importance of Autistic community to these late-diagnosed autistic adults.