Can artificial neural networks predict psychiatric conditions associated with cannabis use?

TY - CHAP

T1 - Can artificial neural networks predict psychiatric conditions associated with cannabis use?

AU - Stamate, Daniel

AU - Alghamdi, Wajdi

AU - Stahl, Daniel

AU - Zamyatin, Alexander

AU - Murray, Robin

AU - Di Forti, Marta

PY - 2018/5/22

Y1 - 2018/5/22

N2 - 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%.

AB - 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%.

KW - Cannabis psychosis

KW - Computational psychiatry

KW - Machine learning

KW - Monte carlo

KW - Neural networks

KW - Prediction modelling

KW - ROC optimisation

UR - http://www.scopus.com/inward/record.url?scp=85049576551&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-92007-8_27

DO - 10.1007/978-3-319-92007-8_27

M3 - Conference paper

AN - SCOPUS:85049576551

SN - 9783319920061

T3 - IFIP Advances in Information and Communication Technology

SP - 311

EP - 322

BT - Artificial Intelligence Applications and Innovations - 14th IFIP WG 12.5 International Conference, AIAI 2018, Proceedings

PB - Springer New York LLC

T2 - 14th IFIP WG 12.5 International Conference on Artificial Intelligence Applications and Innovations, AIAI 2018

Y2 - 25 May 2018 through 27 May 2018

ER -