Abstract
Adult referrals to specialist autism spectrum disorder diagnostic services have increased in recent years, placing strain on existing services and illustrating the need for the development of a reliable screening tool, in order to identify and prioritize patients most likely to receive an ASD diagnosis. In this work a detailed overview of existing approaches is presented and a data driven analysis using machine learning is applied on a dataset of adult autism cases consisting of 192 cases. Our results show initial promise, achieving total positive rate (i.e., correctly classified instances to all instances ratio) up to 88.5%, but also point to limitations of currently available data, opening up avenues for further research. The main direction of this research is the development of a novel autism screening tool for adults (ASTA) also introduced in this work and preliminary results indicate the ASTA is suitable for use as a screening tool for adult populations in clinical settings.
| Original language | English |
|---|---|
| Pages (from-to) | 224-243 |
| Number of pages | 20 |
| Journal | Digital |
| Volume | 2 |
| Issue number | 2 |
| Early online date | 11 May 2022 |
| DOIs | |
| Publication status | Published - 1 Jun 2022 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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