TY - GEN
T1 - Using healthcare analytics to determine an effective diagnostic model for ADHD in students
AU - Mitchnick, Diane
AU - Kumar, Vive
AU - Kinshuk,
AU - Fraser, Shawn
N1 - Publisher Copyright:
© 2021 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.
PY - 2016/4/18
Y1 - 2016/4/18
N2 - 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.
AB - 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.
KW - Data mining
KW - Healthcare data analysis
KW - Learning analytics
KW - Machine learning
KW - Neural networks
UR - http://www.scopus.com/inward/record.url?scp=85114855676&partnerID=8YFLogxK
U2 - 10.1109/BHI.2016.7467133
DO - 10.1109/BHI.2016.7467133
M3 - Published Conference contribution
AN - SCOPUS:85114855676
T3 - 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
BT - 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
T2 - 3rd IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2016
Y2 - 24 February 2016 through 27 February 2016
ER -