TY - JOUR
T1 - Recurrent Neural Networks in Mobile Sampling and Intervention
AU - Koppe, Georgia
AU - Guloksuz, Sinan
AU - Reininghaus, Ulrich
AU - Durstewitz, Daniel
N1 - Funding Information:
S.G. was supported by the European Community’s Seventh Framework Program (grant agreement no. HEALTH-F2-2009–241909) (project EU-GEI). U.R. was supported by a Heisenberg professorship from the German Research Foundation (grant no. 389624707). D.D. was supported by grants from the German Federal Ministry of Education and Research within the e:Med program (01ZX1311A [SP7] and 01ZX1314G [SP10]) and the German Science Foundation (Du 354/8-2).
Publisher Copyright:
© The Author(s) 2018. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved.
PY - 2019/3
Y1 - 2019/3
N2 - The rapid rise and now widespread distribution of handheld and wearable devices, such as smartphones, fitness trackers, or smartwatches, has opened a new universe of possibilities for monitoring emotion and cognition in everyday-life context, and for applying experience- and context-specific interventions in psychosis. These devices are equipped with multiple sensors, recording channels, and app-based opportunities for assessment using experience sampling methodology (ESM), which enables to collect vast amounts of temporally highly resolved and ecologically valid personal data from various domains in daily life. In psychosis, this allows to elucidate intermediate and clinical phenotypes, psychological processes and mechanisms, and their interplay with socioenvironmental factors, as well as to evaluate the effects of treatments for psychosis on important clinical and social outcomes. Although these data offer immense opportunities, they also pose tremendous challenges for data analysis. These challenges include the sheer amount of time series data generated and the many different data modalities and their specific properties and sampling rates. After a brief review of studies and approaches to ESM and ecological momentary interventions in psychosis, we will discuss recurrent neural networks (RNNs) as a powerful statistical machine learning approach for time series analysis and prediction in this context. RNNs can be trained on multiple data modalities simultaneously to learn a dynamical model that could be used to forecast individual trajectories and schedule online feedback and intervention accordingly. Future research using this approach is likely going to offer new avenues to further our understanding and treatments of psychosis.
AB - The rapid rise and now widespread distribution of handheld and wearable devices, such as smartphones, fitness trackers, or smartwatches, has opened a new universe of possibilities for monitoring emotion and cognition in everyday-life context, and for applying experience- and context-specific interventions in psychosis. These devices are equipped with multiple sensors, recording channels, and app-based opportunities for assessment using experience sampling methodology (ESM), which enables to collect vast amounts of temporally highly resolved and ecologically valid personal data from various domains in daily life. In psychosis, this allows to elucidate intermediate and clinical phenotypes, psychological processes and mechanisms, and their interplay with socioenvironmental factors, as well as to evaluate the effects of treatments for psychosis on important clinical and social outcomes. Although these data offer immense opportunities, they also pose tremendous challenges for data analysis. These challenges include the sheer amount of time series data generated and the many different data modalities and their specific properties and sampling rates. After a brief review of studies and approaches to ESM and ecological momentary interventions in psychosis, we will discuss recurrent neural networks (RNNs) as a powerful statistical machine learning approach for time series analysis and prediction in this context. RNNs can be trained on multiple data modalities simultaneously to learn a dynamical model that could be used to forecast individual trajectories and schedule online feedback and intervention accordingly. Future research using this approach is likely going to offer new avenues to further our understanding and treatments of psychosis.
KW - mobile health (mHealth)
KW - deep neural networks
KW - machine learning
KW - ecological momentary assessment
KW - ecological momentary intervention
KW - digital phenotyping and schizophrenia
KW - ECOLOGICAL MOMENTARY INTERVENTIONS
KW - DYNAMICAL-SYSTEMS
KW - REACTIVITY
KW - STRESS
U2 - 10.1093/schbul/sby171
DO - 10.1093/schbul/sby171
M3 - Article
C2 - 30496527
SN - 0586-7614
VL - 45
SP - 272
EP - 276
JO - Schizophrenia Bulletin
JF - Schizophrenia Bulletin
IS - 2
ER -