This study investigates how prioritisation in health services have influenced speech and language therapy (SLT) services in Scotland in prioritising their caseload. Additionally, it evaluates how current prioritisation systems may contribute towards the development of the SLT service in Malaysia. Health, education and social agencies in Malaysia were contacted and a literature review was conducted. Information on prioritisation systems used in Malaysia was unavailable. Prioritisation systems from seven SLT departments within Scotland were investigated, of which three SLT managers took part in semi-structured interviews. The findings show that prioritisation is influenced by a combination of factors and that the principles could only be applied to the Malaysian SLT service if consideration is given to the political, economical, social, geographical and cultural factors.
Matched MeSH terms: Language Therapy/organization & administration*
Autism Spectrum Disorder (ASD), according to DSM-5 in the American Psychiatric Association, is a neurodevelopmental disorder that includes deficits of social communication and social interaction with the presence of restricted and repetitive behaviors. Children with ASD have difficulties in joint attention and social reciprocity, using non-verbal and verbal behavior for communication. Due to these deficits, children with autism are often socially isolated. Researchers have emphasized the importance of early identification and early intervention to improve the level of functioning in language, communication, and well-being of children with autism. However, due to limited local assessment tools to diagnose these children, limited speech-language therapy services in rural areas, etc., these children do not get the rehabilitation they need until they get into compulsory schooling at the age of seven years old. Hence, efficient approaches towards early identification and intervention through speedy diagnostic procedures for ASD are required. In recent years, advanced technologies like machine learning have been used to analyze and investigate ASD to improve diagnostic accuracy, time, and quality without complexity. These machine learning methods include artificial neural networks, support vector machines, a priori algorithms, and decision trees, most of which have been applied to datasets connected with autism to construct predictive models. Meanwhile, the selection of features remains an essential task before developing a predictive model for ASD classification. This review mainly investigates and analyzes up-to-date studies on machine learning methods for feature selection and classification of ASD. We recommend methods to enhance machine learning's speedy execution for processing complex data for conceptualization and implementation in ASD diagnostic research. This study can significantly benefit future research in autism using a machine learning approach for feature selection, classification, and processing imbalanced data.