This paper is in the following e-collection/theme issue:

mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1732) mHealth for Data Collection and Research (1196) Wearable Devices and Sensors (1133) Psychotic Disorders (194) Diagnostic Tools in Mental Health (326) Machine Learning (2076)

Published on in Vol 10, No 4 (2022): April

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/31006, first published .
Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis

Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis

Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis

Authors of this article:

Joanne Zhou1 Author Orcid Image ;   Bishal Lamichhane2 Author Orcid Image ;   Dror Ben-Zeev3 Author Orcid Image ;   Andrew Campbell4 Author Orcid Image ;   Akane Sano2 Author Orcid Image
Article Authors Cited by (23) Tweetations (10) Metrics

Journals

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  4. Zlatintsi A, Filntisis P, Garoufis C, Efthymiou N, Maragos P, Menychtas A, Maglogiannis I, Tsanakas P, Sounapoglou T, Kalisperakis E, Karantinos T, Lazaridi M, Garyfalli V, Mantas A, Mantonakis L, Smyrnis N. E-Prevention: Advanced Support System for Monitoring and Relapse Prevention in Patients with Psychotic Disorders Analyzing Long-Term Multimodal Data from Wearables and Video Captures. Sensors 2022;22(19):7544 View
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  6. Sharma G, Joshi A, Yadav D, Mohanty S. A Smart Healthcare Framework for Accurate Detection of Schizophrenia Using Multichannel EEG. IEEE Transactions on Instrumentation and Measurement 2023;72:1 View
  7. Lamichhane B, Zhou J, Sano A. Psychotic Relapse Prediction in Schizophrenia Patients Using A Personalized Mobile Sensing-Based Supervised Deep Learning Model. IEEE Journal of Biomedical and Health Informatics 2023;27(7):3246 View
  8. Gleeson J, McGuckian T, Fernandez D, Fraser M, Pepe A, Taskis R, Alvarez-Jimenez M, Farhall J, Gumley A. Systematic review of early warning signs of relapse and behavioural antecedents of symptom worsening in people living with schizophrenia spectrum disorders. Clinical Psychology Review 2024;107:102357 View
  9. Khoo L, Lim M, Chong C, McNaney R. Machine Learning for Multimodal Mental Health Detection: A Systematic Review of Passive Sensing Approaches. Sensors 2024;24(2):348 View
  10. Zaher F, Diallo M, Achim A, Joober R, Roy M, Demers M, Subramanian P, Lavigne K, Lepage M, Gonzalez D, Zeljkovic I, Davis K, Mackinley M, Sabesan P, Lal S, Voppel A, Palaniyappan L. Speech markers to predict and prevent recurrent episodes of psychosis: A narrative overview and emerging opportunities. Schizophrenia Research 2024;266:205 View
  11. Guinart D, Fagiolini A, Fusar-Poli P, Giordano G, Leucht S, Moreno C, Correll C. On the Road to Individualizing Pharmacotherapy for Adolescents and Adults with Schizophrenia – Results from an Expert Consensus Following the Delphi Method. Neuropsychiatric Disease and Treatment 2024;Volume 20:1139 View
  12. Zlatintsi A, Filntisis P, Efthymiou N, Garoufis C, Retsinas G, Sounapoglou T, Maglogiannis I, Tsanakas P, Smyrnis N, Maragos P. Person Identification and Relapse Detection From Continuous Recordings of Biosignals Challenge: Overview and Results. IEEE Open Journal of Signal Processing 2024;5:641 View
  13. dos Santos M, Heckler W, Bavaresco R, Barbosa J. Machine learning applied to digital phenotyping: A systematic literature review and taxonomy. Computers in Human Behavior 2024;161:108422 View
  14. Paraschiv E, Băjenaru L, Petrache C, Bica O, Nicolau D. AI-Driven Neuro-Monitoring: Advancing Schizophrenia Detection and Management Through Deep Learning and EEG Analysis. Future Internet 2024;16(11):424 View
  15. Smyrnis A, Theleritis C, Ferentinos P, Smyrnis N. Psychotic relapse prediction via biomarker monitoring: a systematic review. Frontiers in Psychiatry 2024;15 View
  16. Jean T, Guay Hottin R, Orban P, Al-Jumeily OBE D. Forecasting mental states in schizophrenia using digital phenotyping data. PLOS Digital Health 2025;4(2):e0000734 View
  17. Byun A, Lane E, Langholm C, Flathers M, Hall M, Torous J. Towards clinical subtypes in schizophrenia: integrating cognitive, functional, and digital phenotyping assessments. Molecular Psychiatry 2025;30(10):4641 View
  18. Yan A, Speed T, Taylor C. Relapse prediction using wearable data through convolutional autoencoders and clustering for patients with psychotic disorders. Scientific Reports 2025;15(1) View
  19. Berkemeier L, Kamphuis W, Brouwer A, de Vries H, Schadd M, van Baardewijk J, Oldenhuis H, Verdaasdonk R, van Gemert-Pijnen L. Measuring Affective State: Subject-Dependent and -Independent Prediction Based on Longitudinal Multimodal Sensing. IEEE Transactions on Affective Computing 2025;16(2):827 View
  20. Eisner E, Ball H, Ainsworth J, Cella M, Chalmers N, Clifford S, Drake R, Elton D, Faulkner S, Greenwood K, Gumley A, Haddock G, Kendall K, Kenny A, Krogsæter T, Lees J, Lewis S, Maclean L, O’Hare K, Phiri A, Richardson C, Schwannauer M, Turner R, Walsh A, Walters J, Wykes T, Zahid U, Bucci S. Using Passive Sensing to Predict Psychosis Relapse: An In-Depth Qualitative Study Exploring Perspectives of People With Psychosis. Schizophrenia Bulletin 2025 View

Conference Proceedings

  1. Efthymiou N, Retsinas G, Filntisis P, Maragos P. ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Augmenting Transformer Autoencoders with Phenotype Classification for Robust Detection of Psychotic Relapses View
  2. Tsakmaki P, Tasoulis S, Georgakopoulos S, Plagianakos V. 2024 IEEE Congress on Evolutionary Computation (CEC). Harnessing LSTMs for Enhanced Prediction of Psychotic Episodes in Schizophrenia Spectrum View
  3. Tobar B, Pabon S, Paredes C. 2025 IEEE VIII Congreso Internacional en Inteligencia Ambiental, Ingenieria de Software y Salud Electronica y Movil (AmITIC). MindMate: An Application for Symptom Monitoring and Clinical Support in Schizophrenia View