A predictive model for paediatric autism screening

Abstract

Autism spectrum disorder is an umbrella term for a group of neurodevelopmental disorders that is associated with impairments to social interaction, communication, and behaviour. Typically, autism spectrum disorder is first detected with a screening tool (e.g. modified checklist for autism in toddlers). However, the interpretation of autism spectrum disorder behavioural symptoms varies across cultures: the sensitivity of modified checklist for autism in toddlers is as low as 25 per cent in Sri Lanka. A culturally sensitive screening tool called pictorial autism assessment schedule has overcome this problem. Low-and middle-income countries have a shortage of mental health specialists, which is a key barrier for obtaining an early autism spectrum disorder diagnosis. Early identification of autism spectrum disorder enables intervention before atypical patterns of behaviour and brain function become established. This article proposes a culturally sensitive autism spectrum disorder screening mobile application. The proposed application embeds an intelligent machine learning model and uses a clinically validated symptom checklist to monitor and detect autism spectrum disorder in low-and middle-income countries for the first time. Machine learning models were trained on clinical pictorial autism assessment schedule data and their predictive performance was evaluated, which demonstrated that the random forest was the optimal classifier (area under the receiver operating characteristic (0.98)) for embedding into the mobile screening tool. In addition, feature selection demonstrated that many pictorial autism assessment schedule questions are redundant and can be removed to optimise the screening process.

Information and Communication Technologies to Support Early Screening of Autism Spectrum Disorder: A Systematic Review

“…Two studies involved children from 6 to 18 months [ 42 , 43 ]. Six studies involved children within the 10 to 48 months range [ 36 , 40 , 41 , 44 , 45 , 46 ], seven studies involved participants aged between 16 and 30 months [ 22 , 25 , 31 , 32 , 34 , 35 , 47 ], and four studies involved children within the 18- and 72-months range [ 37 , 39 , 48 , 49 ]. Three studies involved a sample of children aged between 48 and 72 months [ 33 , 50 , 51 ].…”

“…The majority of the studies reported in the papers identified was conducted in the USA (n = 19). Seven studies were conducted in as many countries, including China [ 51 ], Peru [ 49 ], UK [ 48 ], Italy [ 52 ], France [ 54 ], Colombia [ 56 ], and Sri-Lanka [ 46 ]. Two papers either did not provide information [ 55 ] or provided unclear information as to the country of origin of the participants recruited [ 45 ].…”

“…In a similar vein, the studies reported by Egger et al [ 25 ] and Carpenter et al [ 36 ] were included both in the NUI and PC/Mobile category. Accordingly, 11 papers reported on the use of computerized solutions (PC or mobile platforms) to administer parent-reported questionnaires [ 22 , 31 , 32 , 33 , 34 , 35 , 37 , 39 , 46 , 47 , 55 ], and seven papers employed screening tools in which videos were collected from [ 37 , 39 , 43 , 50 ] or showed via [ 25 , 36 , 42 ] parents’ mobile/PC devices.…”

Effects of Intra-Subject Variation in Gait Analysis on ASD Classification Performance in Machine Learning Models

“…Autism Spectrum Disorder (ASD) is a permanent neurological condition that is prevalent in the entire world population [1]. Despite the field focusing on the behavioural aspect of ASD [2], [3], more recent research has been conducted indicating that ASD also influences the motor control for many people with the condition. This can manifest itself through various, measurable features relevant to a person's gait such as the kinematics of their joints [4] and ground reaction forces during perambulation [5].…”

Prediction of Cocaine Inpatient Treatment Success Using Machine Learning on High-Dimensional Heterogeneous Data

“…Machine learning (ML) methods are useful to learn the relationships that exist amongst data points, and this can be applied to clinical datasets for the purpose of developing robust risk models [21]. The development of decision support systems based on predictive machine learning models has become a subject of enormous scientific interest, and this can be observed in various fields of medicine, finding systems that allow the identification of prescriptions with high risk of medication error [22], the prediction of metastasis in gastric cancer [23], the detection of pediatric autism [24], the prediction of coronary artery disease [25], the treatment of large kidney stones [26] or the prediction of heart failure [27] among others.…”