Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Nursing Interventions I: Taxonomy of Nursing Interventions01:03

Nursing Interventions I: Taxonomy of Nursing Interventions

3.9K
Nursing interventions are chosen as part of the planning process to achieve patient outcomes. Once nursing diagnoses are determined, the goals and outcomes are specified, then the nursing interventions are selected and individualized according to the patient's situation.
A nursing intervention is a treatment or action based on scientific concepts and knowledge from the nursing, behavioral, and physical sciences. Identifying and prioritizing nursing interventions based on the desired outcome...
3.9K
Protein Networks02:26

Protein Networks

4.5K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.5K
Protein Networks02:26

Protein Networks

2.9K
2.9K
Nursing Interventions II: Selecting and Classifying the Nursing Interventions01:29

Nursing Interventions II: Selecting and Classifying the Nursing Interventions

3.3K
Creating and executing a nursing diagnosis helps nurses plan care and guide patient, family, and community interventions. They are developed based on a patient's physical evaluation and support measuring the outcomes. It is not recommended to select random interventions throughout the planning process. Instead, consider the following six essential factors when choosing interventions:
3.3K
Network Covalent Solids02:18

Network Covalent Solids

16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
Community Based Intervention01:30

Community Based Intervention

482
Community-based interventions in mental health represent a paradigm shift from institution-centered care to treatments embedded within the fabric of local communities. By prioritizing inclusion and leveraging existing societal structures, this approach fosters a supportive environment conducive to addressing mental health challenges while promoting individual dignity and agency.
Foundations of Community Mental Health Programs
Central to the success of community-based interventions is the...
482

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A Data-Driven Closed-Loop Control Approach to Drive Neural State Transitions for Mechanistic Insight.

Human brain mapping·2026
Same author

Interpersonal stress, epigenetic indices of inflammation, and depressive symptoms: Longitudinal associations from adolescence to young adulthood.

Neurobiology of stress·2026
Same author

Developmental Trauma as a Prognostic Factor for Later Psychotic Disorder in an Adolescent Clinical Cohort: A 20-Year Follow Up Study.

Acta psychiatrica Scandinavica·2026
Same author

Co-expression-based models improve eQTL predictions for transcriptome-wide association studies and highlight new schizophrenia-associated genes.

Nature genetics·2026
Same author

Promoting Psychological Resilience and Well-Being in Youth With a Smartphone-Based Ecological Momentary mHealth Intervention: Secondary Analysis of a Microrandomized Trial.

Journal of medical Internet research·2026
Same author

Shared genetic architecture between Alzheimer's disease and brain morphology.

Alzheimer's research & therapy·2026

Related Experiment Video

Updated: Feb 1, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K

Recurrent Neural Networks in Mobile Sampling and Intervention.

Georgia Koppe1,2, Sinan Guloksuz3,4, Ulrich Reininghaus3,5,6

  • 1Department of Theoretical Neuroscience, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.

Schizophrenia Bulletin
|November 30, 2018
PubMed
Summary

Wearable devices and experience sampling methodology (ESM) offer new ways to monitor psychosis. Recurrent neural networks (RNNs) can analyze this complex data for better understanding and treatment.

Keywords:
deep neural networksdigital phenotyping and schizophreniaecological momentary assessmentecological momentary interventionmachine learningmobile health (mHealth)

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments
08:40

Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments

Published on: January 20, 2022

4.9K

Related Experiment Videos

Last Updated: Feb 1, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

1.1K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

10.0K
Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments
08:40

Using a Cyclic Ion Mobility Spectrometer for Tandem Ion Mobility Experiments

Published on: January 20, 2022

4.9K

Area of Science:

  • Digital Phenotyping
  • Psychosis Research
  • Machine Learning in Healthcare

Background:

  • Widespread adoption of smartphones and wearables enables real-time data collection in daily life.
  • Experience Sampling Methodology (ESM) provides ecologically valid, high-resolution personal data.
  • This data can elucidate psychosis phenotypes, psychological mechanisms, and socioenvironmental interactions.

Purpose of the Study:

  • To explore the potential of wearable devices and ESM for monitoring psychosis.
  • To discuss the challenges posed by large, multi-modal time-series data in psychosis research.
  • To introduce Recurrent Neural Networks (RNNs) as a solution for analyzing such data.

Main Methods:

  • Review of existing studies on ESM and ecological momentary interventions in psychosis.
  • Discussion of Recurrent Neural Networks (RNNs) for time-series analysis and prediction.
  • Highlighting RNNs' capability to integrate multiple data modalities for dynamical modeling.

Main Results:

  • Wearable devices and ESM generate vast amounts of complex, multi-modal time-series data.
  • RNNs offer a powerful approach to analyze this data, overcoming traditional limitations.
  • RNNs can learn dynamical models for forecasting individual trajectories in psychosis.

Conclusions:

  • Wearable technology and ESM present significant opportunities for psychosis research and intervention.
  • RNNs are a promising machine learning tool for analyzing complex digital phenotyping data.
  • Future research with RNNs could enhance understanding and treatment of psychosis.