Using healthcare analytics to determine an effective diagnostic model for ADHD in students

Diane Mitchnick, Vive Kumar, Kinshuk, Shawn Fraser

Research output: Chapter in Book/Report/Conference proceedingPublished Conference contributionpeer-review

1 Citation (Scopus)

Abstract

Attention Deficit Hyperactivity Disorder (ADHD) is a mental health disorder. People diagnosed with ADHD are often inattentive (have difficulty focusing on a task for a considerable period of time), overly impulsive (make rash decisions), and are hyperactive (moving excessively, often at inappropriate times). ADHD is often diagnosed through psychiatric assessments with additional input from physical/neurological evaluations. Current tools designed for ADHD screening collect data manually and do not interoperate with each other. This paper will first review the effectiveness of common screening tools in relation to the Diagnostic and Statistical Manual of Mental Disorders (DSM) for ADHD classifier. This paper will also introduce the concept of using written performance data as a method of screening, since previous research has linked written language disorder (WLD) to ADHD as well. The current phase of this research proposes that an integrated computational model that combines outcomes from these screening tools will have a more effective diagnosis of ADHD in adult students than from the diagnosis of any individual screening tool. The integrated computational model, based on neural networks, will be built and tested in a future phase with each of the datasets (physical, behavior and learning performance) being collected from students.

Original languageEnglish
Title of host publication3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
ISBN (Electronic)9781509024551
DOIs
Publication statusPublished - 18 Apr. 2016
Event3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016 - Las Vegas, United States
Duration: 24 Feb. 201627 Feb. 2016

Publication series

Name3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Volume2016-January

Conference

Conference3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Country/TerritoryUnited States
CityLas Vegas
Period24/02/1627/02/16

Keywords

  • Data mining
  • Healthcare data analysis
  • Learning analytics
  • Machine learning
  • Neural networks

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