AutismOnt: An Ontology-Driven Decision Support For Autism Diagnosis and Treatment Mariam M. Hassan ⇑ , Hoda M.O. Mokhtar Faculty of Computers and Information, Cairo University, Egypt article info Article history: Received 13 November 2020 Revised 19 June 2021 Accepted 5 July 2021 Available online xxxx Keywords: Autism spectrum disorder Decision tree Medical ontology Decision support abstract Autism Spectrum Disorder (ASD) is a deleterious neurodevelopmental disorder affecting 1 in 54 children. The complex interdisciplinary nature of ASD research, however, introduces challenges to the spread and accessibility of new discoveries among researchers in different disciplines; furthermore, this highly com- plex research environment makes it even harder for practicing physicians and primary care providers to keep up with recent advances, which would profoundly impact the extent toward which recent research impacts standard medical and caretake practices. In order to contribute toward bridging the gaps between researchers in different fields, practicing physicians, and primary caretakers, we have created the most expansive autism ontology up to date (AutimsOnt) through utilizing the Protégé ontological framework. With 676 classes and more than 124 properties, AutismOnt can serve as the foundation to support a wide range of applications ranging from decision support systems for practicing physicians to text annotation processes that would allow for the creation of an interdisciplinary research platform where investigators can easily share and retrieve scientific findings. The Ontology is available in the NCBO BioPortal. Ó 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Computers and Artificial Intel- ligence, Cairo University. This is an open access article under the CC BY-NC-ND license (http://creative- commons.org/licenses/by-nc-nd/4.0/). 1. Introduction Autism Spectrum Disorders (ASD) is an umbrella term that cov- ers a multitude of early-onset neurodevelopmental conditions characterized by a wide array of cognitive, behavioral, and social impairments [1]. The symptoms exhibited by ASD patients are diverse, as ASD is very heterogeneous in its manifestation, with a 4:1 male to female gender ratio and a wide range of symptom severity ranging from highly functioning individuals who can lead a relatively independent life as adults to severely mentally impaired individuals who would require constant caretaking and supervision throughout their lives [2,3]. Despite the diversity of symptoms, however, most ASD individuals suffer from a reduced quality of life, as even high functioning individuals still face social challenges, which can be a source of distress; furthermore, ASD also incurs a tremendous emotional and financial toll on the family of ASD individuals as well as society as a whole [4]. Research on ASD has progressed rapidly over the past two dec- ades [5]. ASD research, however, is extremely multi-faceted, rang- ing from basic scientists seeking to understand the molecular and cellular underpinnings of the disorder through utilizing animal models and molecular neurobiology approaches to behavioral sci- entists working with patients in order to develop new approaches to help the parents and educators better take care of individuals afflicted with this disorder. This diversity in research makes it par- ticularly challenging to bridge the gap between such disparate dis- ciplines; furthermore, as the research environment grows increasingly complex, the development of techniques ensuring the accessibility of this research output to practicing physicians and primary caretakers becomes of paramount importance [6–8]. Biomedical ontologies have recently witnessed a remarkable increase in popularity as the tool of choice for bridging interdisci- plinary gaps and ensuring the widespread accessibility and exchange of information between researchers and professionals with diverse backgrounds and a broad range of specializations [6–14]. The striking success of biomedical ontologies stems from https://doi.org/10.1016/j.eij.2021.07.002 1110-8665/Ó 2021 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Computers and Artificial Intelligence, Cairo University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). ⇑ Corresponding author. E-mail addresses: m.mostafa@pg.cu.edu.eg (M.M. Hassan), h.mokhtar@fci-cu. edu.eg (H.M.O. Mokhtar). Peer review under responsibility of Faculty of Computers and Information, Cairo University. Production and hosting by Elsevier Egyptian Informatics Journal xxx (xxxx) xxx Contents lists available at ScienceDirect Egyptian Informatics Journal journal homepage: www.sciencedirect.com Please cite this article as: M.M. Hassan and Hoda M.O. Mokhtar, AutismOnt: An Ontology-Driven Decision Support For Autism Diagnosis and Treatment, Egyptian Informatics Journal, https://doi.org/10.1016/j.eij.2021.07.002