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Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches

Stamate, Daniel; Katrinecz, Andrea; Stahl, Daniel; Verhagen, Simone J.W.; Delespaul, Philippe A.E.G.; van Os, Jim and Guloksuz, Sinan. 2019. Identifying psychosis spectrum disorder from experience sampling data using machine learning approaches. Schizophrenia Research, 209, pp. 156-163. ISSN 0920-9964 [Article]

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Abstract or Description

The ubiquity of smartphones have opened up the possibility of widespread use of the Experience Sampling Method (ESM). The method is used to collect longitudinal data of participants' daily life experiences and is ideal to capture fluctuations in emotions (momentary mental states) as an indicator for later mental ill-health. In this study, ESM data of patients with psychosis spectrum disorder and controls were used to examine daily life emotions and higher order patterns thereof. We attempted to determine whether aggregated ESM data, in which statistical measures represent the distribution and dynamics of the original data, were able to distinguish patients from controls in a predictive modelling framework. Variable importance, recursive feature elimination, and ReliefF methods were used for feature selection. Model training, tuning, and testing were performed in nested cross-validation, based on algorithms such as Random Forests, Support Vector Machines, Gaussian Processes, Logistic Regression and Neural Networks. ROC analysis was used to post-process these models. Stability of model performance was studied using Monte Carlo simulations. The results provide evidence that patterns in emotion changes can be captured by applying a combination of these techniques. Acceleration in the variables anxious and insecure was particularly successful in adding further predictive power to the models. The best results were achieved by Support Vector Machines with radial kernel (accuracy=82% and sensitivity=82%). This proof-of-concept work demonstrates that synergistic machine learning and statistical modeling may be used to harness the power of ESM data in the future.

Item Type:

Article

Identification Number (DOI):

https://doi.org/10.1016/j.schres.2019.04.028

Keywords:

machine learning, prediction modelling, mobile health, mental health, predicting risk psychosis, ROC analysis, prediction, schizophrenia, computational psychiatry

Departments, Centres and Research Units:

Computing

Dates:

DateEvent
31 April 2019Accepted
16 May 2019Published Online
July 2019Published

Item ID:

26340

Date Deposited:

17 May 2019 10:57

Last Modified:

30 Jul 2019 00:30

Peer Reviewed:

Yes, this version has been peer-reviewed.

URI:

http://research.gold.ac.uk/id/eprint/26340

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