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Goldsmiths - University of London

Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning

Stamate, Daniel; Alghamdi, Wajdi; Stahl, Daniel; Pu, Ida; Murtagh, Fionn; Belgrave, Danielle; Murray, Robin and di Forti, Marta. 2018. Predicting First-Episode Psychosis Associated with Cannabis Use with Artificial Neural Networks and Deep Learning. In: , ed. Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. 855 Springer, pp. 691-702. ISBN 978-3-319-91479-4 [Book Section]

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Abstract or Description

In recent years, a number of researches started to investigate the existence of links between cannabis use and psychotic disorder. More recently, artificial neural networks and in particular deep learning have set a revolutionary wave in pattern recognition and machine learning. This study proposes a novel machine learning approach based on neural network and deep learning algorithms, to developing highly accurate predictive models for the onset of first-episode psychosis. Our approach is based also on a novel methodology of optimising and post-processing the predictive models in a computationally intensive framework. A study of the trade-off between the volume of the data and the extent of uncertainty due to missing values, both of which influencing the predictive performance, enhanced this approach. Furthermore, we extended our approach by proposing and encapsulating a novel post-processing k-fold cross-testing method in order to further optimise, and test these models. The results show that the average accuracy in predicting first-episode psychosis achieved by our models in intensive Monte Carlo simulation, is about 89%.

Item Type: Book Section

Identification Number (DOI):

https://doi.org/10.1007/978-3-319-91479-4_57

Keywords:

First-episode psychosis, Precision medicine, Cannabis use, Prediction modelling, Classification, Neural network, Deep learning, Post-processing, Monte Carlo simulation, Missing data based uncertainty

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
18 May 2018Published Online

Item ID:

23586

Date Deposited:

27 Jun 2018 14:18

Last Modified:

09 Jul 2018 15:36

URI: http://research.gold.ac.uk/id/eprint/23586

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