A New Machine Learning Framework for Understanding the Link Between Cannabis Use and First-Episode Psychosis.

Journal: Studies in health technology and informatics
Published Date: Jan 1, 2018

Abstract

Lately, several studies started to investigate the existence of links between cannabis use and psychotic disorders. This work proposes a refined Machine Learning framework for understanding the links between cannabis use and 1st episode psychosis. The novel framework concerns extracting predictive patterns from clinical data using optimised and post-processed models based on Gaussian Processes, Support Vector Machines, and Neural Networks algorithms. The cannabis use attributes' predictive power is investigated, and we demonstrate statistically and with ROC analysis that their presence in the dataset enhances the prediction performance of the models with respect to models built on data without these specific attributes.

Authors

  • Wajdi Alghamdi
    Data Science & Soft Computing Lab, and Department of Computing, Goldsmiths, University of London, UK.
  • Daniel Stamate
    Data Science & Soft Computing Lab, and Department of Computing, Goldsmiths, University of London, UK.
  • Daniel Stahl
    King's College London, Institute of Psychiatry, Department of Biostatistics, London, UK.
  • Alexander Zamyatin
    Faculty of Informatics, Department of Applied Informatics, National Research Tomsk State University, UK.
  • Robin Murray
    Department of Psychosis Studies, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.
  • Marta Di Forti
    MRC Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King's College London, UK.

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