Predicting Substance Use Disorder in ADHD Patients using Long-Short Term Memory Model

Sajjad Fouladvand; Emily R. Hankosky; Darren W. Henderson; Heather Bush; Jin Chen; Linda P. Dwoskin; Patricia R. Freeman; Kathleen Kantak; Jeffery Talbert; Shiqiang Tao; Guo Qiang Zhang

doi:10.1109/ICHI-W.2018.00014

Predicting Substance Use Disorder in ADHD Patients using Long-Short Term Memory Model

Sajjad Fouladvand, Emily R. Hankosky, Darren W. Henderson, Heather Bush, Jin Chen, Linda P. Dwoskin, Patricia R. Freeman, Kathleen Kantak, Jeffery Talbert, Shiqiang Tao, Guo Qiang Zhang

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

About 20% of individuals with attention deficit hyperactivity disorder (ADHD) are first diagnosed during adolescence. While preclinical experiments suggest that adolescent-onset exposure to ADHD medication is an important factor in the development of substance use disorder (SUD) phenotypes in adulthood, the long-Term impact of ADHD medication initiated during adolescence has been largely unexplored in humans. Our analysis of 11,624 adolescent ADHD patients in the Truven database indicates that temporal medication features are the important factors on the health consequences related to SUD and ADHD medication initiation during adolescence.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
Pages	49-50
Number of pages	2
ISBN (Electronic)	9781538667774
DOIs	https://doi.org/10.1109/ICHI-W.2018.00014
State	Published - Jul 16 2018
Event	6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 - New York, United States Duration: Jun 4 2018 → Jun 7 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018

Conference

Conference	6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018
Country/Territory	United States
City	New York
Period	6/4/18 → 6/7/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

Attention Deficit-Hyperactivity Disorder
Big Data
Long-Short Term Memory Model
Substance Use Disorder

ASJC Scopus subject areas

Information Systems and Management
Health Informatics

Access to Document

10.1109/ICHI-W.2018.00014

Cite this

Fouladvand, S., Hankosky, E. R., Henderson, D. W., Bush, H., Chen, J., Dwoskin, L. P., Freeman, P. R., Kantak, K., Talbert, J., Tao, S., & Zhang, G. Q. (2018). Predicting Substance Use Disorder in ADHD Patients using Long-Short Term Memory Model. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018 (pp. 49-50). [8411680] (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018). https://doi.org/10.1109/ICHI-W.2018.00014

Fouladvand, Sajjad ; Hankosky, Emily R. ; Henderson, Darren W. et al. / Predicting Substance Use Disorder in ADHD Patients using Long-Short Term Memory Model. Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. 2018. pp. 49-50 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018).

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abstract = "About 20% of individuals with attention deficit hyperactivity disorder (ADHD) are first diagnosed during adolescence. While preclinical experiments suggest that adolescent-onset exposure to ADHD medication is an important factor in the development of substance use disorder (SUD) phenotypes in adulthood, the long-Term impact of ADHD medication initiated during adolescence has been largely unexplored in humans. Our analysis of 11,624 adolescent ADHD patients in the Truven database indicates that temporal medication features are the important factors on the health consequences related to SUD and ADHD medication initiation during adolescence.",

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author = "Sajjad Fouladvand and Hankosky, {Emily R.} and Henderson, {Darren W.} and Heather Bush and Jin Chen and Dwoskin, {Linda P.} and Freeman, {Patricia R.} and Kathleen Kantak and Jeffery Talbert and Shiqiang Tao and Zhang, {Guo Qiang}",

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doi = "10.1109/ICHI-W.2018.00014",

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Fouladvand, S, Hankosky, ER, Henderson, DW, Bush, H , Chen, J , Dwoskin, LP , Freeman, PR, Kantak, K, Talbert, J, Tao, S & Zhang, GQ 2018, Predicting Substance Use Disorder in ADHD Patients using Long-Short Term Memory Model. in Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018., 8411680, Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018, pp. 49-50, 6th IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018, New York, United States, 6/4/18. https://doi.org/10.1109/ICHI-W.2018.00014

Predicting Substance Use Disorder in ADHD Patients using Long-Short Term Memory Model. / Fouladvand, Sajjad; Hankosky, Emily R.; Henderson, Darren W. et al.

Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. 2018. p. 49-50 8411680 (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Dwoskin, Linda P.

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Fouladvand S, Hankosky ER, Henderson DW, Bush H , Chen J , Dwoskin LP et al. Predicting Substance Use Disorder in ADHD Patients using Long-Short Term Memory Model. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018. 2018. p. 49-50. 8411680. (Proceedings - 2018 IEEE International Conference on Healthcare Informatics Workshops, ICHI-W 2018). doi: 10.1109/ICHI-W.2018.00014

Predicting Substance Use Disorder in ADHD Patients using Long-Short Term Memory Model

Abstract

Publication series

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Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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