A new machine learning framework for understanding the link etween cannabis use and first-episode psychosis

Wajdi Alghamdi*, Daniel Stamate, Daniel Stahl, Alexander Zamyatin, Robin Murray, Marta Di Forti

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

4 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationHealth Informatics Meets eHealth
Subtitle of host publicationBiomedical Meets eHealth - From Sensors to Decisions - Proceedings of the 12th eHealth Conference
PublisherIOS Press
Pages9-16
Number of pages8
ISBN (Electronic)9781614998570
DOIs
Publication statusPublished - 1 Jan 2018
Event12th Annual Conference on Health Informatics Meets eHealth, eHealth 2018 - Vienna, Austria
Duration: 8 May 20189 May 2018

Publication series

NameStudies in Health Technology and Informatics
Volume248
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference12th Annual Conference on Health Informatics Meets eHealth, eHealth 2018
Country/TerritoryAustria
CityVienna
Period8/05/20189/05/2018

Keywords

  • eHealth
  • First-episode psychosis
  • Gaussian processes
  • Machine learning
  • Neural networks
  • Support vector machine

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