Alghamdi, W;
Stamate, D;
Vang, K;
Stahl, D;
Colizzi, M;
Tripoli, G;
Quattrone, D;
... Di Forti, M; + view all
(2017)
A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use.
In:
Proceedings of the 15th IEEE International Conference on Machine Learning and Applications (ICMLA) 2016.
(pp. pp. 825-830).
IEEE: Danvers (MA), USA.
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Abstract
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.
| Type: | Proceedings paper |
|---|---|
| Title: | A Prediction Modelling and Pattern Detection Approach for the First-Episode Psychosis Associated to Cannabis Use |
| Event: | ICMLA 2016: 15th IEEE International Conference on Machine Learning and Applications (ICMLA) |
| Location: | Anaheim (CA), USA |
| Dates: | 18th-20th December 2016 |
| ISBN-13: | 978-1-5090-6167-9 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/ICMLA.2016.0148 |
| Publisher version: | https://doi.org/10.1109/ICMLA.2016.0148 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Predictive models, Computational modeling, Data models, Psychiatry, Monte Carlo methods, Bayes methods, Machine learning algorithms |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Behavioural Science and Health |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10049730 |
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