Analysing the History of Autism Spectrum Disorder Using Topic Models

Abstract

Abstract-We describe a novel framework for the discovery of underlying topics of a longitudinal collection of scholarly data, and the tracking of their lifetime and popularity over time. Unlike the social media or news data, as the topic nuances in science result in new scientific directions to emerge, a new approach to model the longitudinal literature data is using topics which remain identifiable over the course of time. Current studies either disregard the time dimension or treat it as an exchangeable covariate when they fix the topics over time or do not share the topics over epochs when they model the time naturally. We address these issues by adopting a non-parametric Bayesian approach. We assume the data is partially exchangeable and divided it into consecutive epochs. Then, by fixing the topics in a recurrent Chinese restaurant franchise, we impose a static topical structure on the corpus such that the they are shared across epochs and the documents within epochs. We demonstrate the effectiveness of the proposed framework on a collection of medical literature related to autism spectrum disorder. We collect a large corpus of publications and carefully examining two important research issues of the domain as case studies. Moreover, we make the results of our experiment and the source code of the model, freely available to aid other researchers by analysing the results or applying the model to their data collections.

Diagnosis Prediction from Electronic Health Records Using the Binary Diagnosis History Vector Representation

“…Yet, the available resources are inherently limited. To ensure their best usage it is crucial both to develop an understanding of the related epidemiology, as well as to be able to communicate this knowledge effectively to those who can benefit from it: governments (Berwick and Hackbarth, 2012), the medical research community (Beykikhoshk et al, 2015a(Beykikhoshk et al, , 2016Andrei and Arandjelović, 2016), health care practitioners (Arandjelović, 2015a;Osuala and Arandjelović, 2017), and patients (Beykikhoshk et al, 2014;Barracliffe et al, 2017).…”

Discovering topic structures of a temporally evolving document corpus

“…This is unsurprising considering the close relatedness of the two measures, as highlighted previously in Sect. 4.2 and as expressed by the expression in (16). However, at first sight the results obtained using the quasi-Jaccard index appear rather different.…”

“…For this reason in recent years the problem of temporal topic modelling has been attracting an increasing amount of research attention [2,4,12,13,16,25]. Indeed the focus of the present paper is on temporally changing corpora.…”

A Micro Perspective of Research Dynamics Through “Citations of Citations” Topic Analysis

“…To perceive or even predict temporal changes to the research landscape, researchers from various domains leverage topic models to revise the research dynamics of their domains. Beykikhoshk et al (2016) use topic model to analyze the history of the autism spectrum disorder. Doyle and Elkan (2019) use Dirichlet compound multinomial (DCM) distributions to model the phenomenon of "burstiness.".…”