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A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use

Alghamdi, Wajdi; Stamate, Daniel; Vang, Katherine; Stahl, Daniel; Colizzi, Marco; Tripoli, Giada; Quattrone, Diego; Ajnakina, Olesya; Murray, Robin M. and Forti, Marta Di. 2016. A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use. In: , ed. 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). Anaheim, CA, USA: IEEE, pp. 825-830. ISBN 978-1-5090-6167-9 [Book Section]

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Abstract or Description

Over the last two decades, a significant body of
research has established a link between cannabis use and
psychotic outcomes. In this study, we aim to propose a novel symbiotic machine learning and statistical approach to pattern detection and to developing predictive models for the onset of first-episode psychosis. The data used has been gathered from real cases in cooperation with a medical research institution, and comprises a wide set of variables including demographic, drug-related, as well as several variables specifically related to the cannabis use. Our approach is built upon several machine learning techniques whose predictive models have been optimised in a computationally intensive framework. The ability of these models to predict first-episode psychosis has been extensively tested through large scale Monte Carlo
simulations. Our results show that Boosted Classification Trees outperform other models in this context, and have significant predictive ability despite a large number of missing values in the data. Furthermore, we extended our approach by further investigating how different patterns of cannabis use relate to new cases of psychosis, via association analysis and Bayesian techniques.

Item Type:

Book Section

Identification Number (DOI):

https://doi.org/10.1109/ICMLA.2016.0148

Keywords:

Bayesian inference, Predicting first-episode psychosis, Cannabis use, Precision medicine, Prediction modelling, Classification, Monte Carlo simulation, Association analysis

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
UNSPECIFIEDAccepted
18 December 2016Published

Item ID:

21110

Date Deposited:

22 Sep 2017 11:04

Last Modified:

10 Jul 2018 09:56

URI:

http://research.gold.ac.uk/id/eprint/21110

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