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A New Machine Learning Framework for Understanding the Link between Cannabis Use and First-Episode Psychosis

Walghamdi, Wajdi; Stamate, Daniel; Stahl, Daniel; Murray, Robin and Di Forti, Marta. 2018. A New Machine Learning Framework for Understanding the Link between Cannabis Use and First-Episode Psychosis. In: , ed. Volume 248: Health Informatics Meets eHealth. 248 IOS Press, pp. 9-16. ISBN 978-1-61499-857-0 [Book Section]

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Abstract or Description

Lately, several studies started to investigate the existence of links between cannabis use and psychotic disorders. This work proposes a refined Machine
Learning framework for understanding the links between cannabis use and 1st episode psychosis. The novel framework concerns extracting predictive patterns
from clinical data using optimised and post-processed models based on Gaussian Processes, Support Vector Machines, and Neural Networks algorithms. The cannabis use attributes’ predictive power is investigated, and we demonstrate statistically and with ROC analysis that their presence in the dataset enhances the
prediction performance of the models with respect to models built on data without these specific attributes.

Item Type:

Book Section

Identification Number (DOI):

https://doi.org/10.3233/978-1-61499-858-7-9

Keywords:

eHealth, Machine Learning, First-Episode Psychosis, Gaussian Processes, Support Vector Machine, Neural Networks

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
1 March 2018Accepted
8 May 2018Published

Item ID:

24129

Date Deposited:

17 Sep 2018 13:19

Last Modified:

17 Sep 2018 13:19

URI:

http://research.gold.ac.uk/id/eprint/24129

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