Big data approaches to develop a comprehensive and accurate tool aimed at improving autism spectrum disorder diagnosis and subtype stratification. Issue 4 (23rd June 2020)

• Record Type: Journal Article
• Authors: Chen, Tao; Froehlich, Tanya; Li, Tingyu; Lu, Long
• Is Part Of: Library hi tech. Volume 38:Issue 4(2020)
• Page Start: 819
• Page End: 833
• Publication Date: 2020-06-23
• Subjects: Autism spectrum disorder -- Data integration -- Classification -- Clustering -- Stratification -- Subtype; Library science -- Technological innovations -- Periodicals; Libraries -- Automation -- Periodicals; Information science -- Periodicals; 025.00285
• DOI: 10.1108/LHT-08-2019-0175
• Languages: English
• ISSNs: 0737-8831

Abstract

Abstract : Purpose: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder that is difficult to diagnose accurately due to its heterogeneous clinical manifestations. Comprehensive models combining different big data approaches (e.g. neuroimaging, genetics, eye tracking, etc.) may offer the opportunity to characterize ASD from multiple distinct perspectives. This paper aims to provide an overview of a novel diagnostic approach for ASD classification and stratification based on these big data approaches. Design/methodology/approach: Multiple types of data were collected and recorded for three consecutive years, including clinical assessment, neuroimaging, gene mutation and expression and response signal data. The authors propose to establish a classification model for predicting ASD clinical diagnostic status by integrating the various data types. Furthermore, the authors suggest a data-driven approach to stratify ASD into subtypes based on genetic and genomic data. Findings: By utilizing complementary information from different types of ASD patient data, the proposed integration model has the potential to achieve better prediction performance than models focusing on only one data type. The use of unsupervised clustering for the gene-based data-driven stratification will enable identification of more homogeneous subtypes. The authors anticipate that such stratification will facilitate a more consistent and personalized ASD diagnostic tool. Originality/value: This study aims to utilize a more comprehensive investigation of ASD-related data types than prior investigations, including proposing longitudinal data collection and a storage scheme covering diverse populations. Furthermore, this study offers two novel diagnostic models that focus on case-control status prediction and ASD subtype stratification, which have been under-explored in the prior literature.