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Goldsmiths - University of London

Can Artificial Neural Networks Predict Psychiatric Conditions Associated with Cannabis Use?

Stamate, Daniel; Alghamdi, Wajdi; Stahl, Daniel; Zamyatin, Alexander; Murray, Robin and di Forti, Marta. 2018. Can Artificial Neural Networks Predict Psychiatric Conditions Associated with Cannabis Use? In: , ed. Artificial Intelligence Applications and Innovations. 519 Springer, pp. 311-322. ISBN 978-3-319-92006-1 [Book Section]

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Abstract or Description

This data-driven computational psychiatry research proposes a novel machine learning approach to developing predictive models for the onset of first-episode psychosis, based on artificial neural networks. The performance capabilities of the predictive models are enhanced and evaluated by a methodology consisting of novel model optimisation and testing, which integrates a phase of model tuning, a phase of model post-processing with ROC optimisation based on maximum accuracy, Youden and top-left methods, and a model evaluation with the k-fold cross-testing methodology. We further extended our framework by investigating the cannabis use attributes’ predictive power, and demonstrating statistically that their presence in the dataset enhances the prediction performance of the neural network models. Finally, the model stability is explored via simulations with 1000 repetitions of the model building and evaluation experiments. The results show that our best Neural Network model’s average accuracy of predicting first-episode psychosis, which is evaluated with Monte Carlo, is above 80%.

Item Type:

Book Section

Identification Number (DOI):

https://doi.org/10.1007/978-3-319-92007-8_27

Keywords:

Machine Learning, Neural Networks, Prediction modelling, ROC optimisation, Monte Carlo, Computational Psychiatry, Cannabis, Psychosis

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
8 March 2018Accepted
22 May 2018Published

Item ID:

24128

Date Deposited:

14 Sep 2018 15:08

Last Modified:

14 Sep 2018 15:08

URI:

http://research.gold.ac.uk/id/eprint/24128

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