Published on in Vol 8, No 8 (2020): August

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/19962, first published .
Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks

Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks

Journals

  1. Henson P, D’Mello R, Vaidyam A, Keshavan M, Torous J. Anomaly detection to predict relapse risk in schizophrenia. Translational Psychiatry 2021;11(1) View
  2. Asuzu K, Rosenthal M. Mobile device use among inpatients on a psychiatric unit: A preliminary study. Psychiatry Research 2021;297:113720 View
  3. Gutierrez L, Rabbani K, Ajayi O, Gebresilassie S, Rafferty J, Castro L, Banos O. Internet of Things for Mental Health: Open Issues in Data Acquisition, Self-Organization, Service Level Agreement, and Identity Management. International Journal of Environmental Research and Public Health 2021;18(3):1327 View
  4. Koyama T, Sato S, Toriumi M, Watanabe T, Nimura A, Okawa A, Sugiura Y, Fujita K. A Screening Method Using Anomaly Detection on a Smartphone for Patients With Carpal Tunnel Syndrome: Diagnostic Case-Control Study. JMIR mHealth and uHealth 2021;9(3):e26320 View
  5. Cook D, Schmitter-Edgecombe M. Fusing Ambient and Mobile Sensor Features Into a Behaviorome for Predicting Clinical Health Scores. IEEE Access 2021;9:65033 View
  6. Steingrímsson S, Odéus E, Cederlund M, Franzén S, Helgesson C, Nyström K, Sondell J, Opheim A. Weighted blanket and sleep medication use among adults with psychiatric diagnosis – a population-based register study. Nordic Journal of Psychiatry 2022;76(1):29 View
  7. Tønning M, Faurholt-Jepsen M, Frost M, Bardram J, Kessing L. Mood and Activity Measured Using Smartphones in Unipolar Depressive Disorder. Frontiers in Psychiatry 2021;12 View
  8. Frank E, Wallace M, Matthews M, Kendrick J, Leach J, Moore T, Aranovich G, Choudhury T, Shah N, Framroze Z, Posey G, Burgess S, Kupfer D. Personalized digital intervention for depression based on social rhythm principles adds significantly to outpatient treatment. Frontiers in Digital Health 2022;4 View
  9. Adler D, Wang F, Mohr D, Choudhury T, Chen C. Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies. PLOS ONE 2022;17(4):e0266516 View
  10. Zhou J, Lamichhane B, Ben-Zeev D, Campbell A, Sano A. Predicting Psychotic Relapse in Schizophrenia With Mobile Sensor Data: Routine Cluster Analysis. JMIR mHealth and uHealth 2022;10(4):e31006 View
  11. Lakhtakia T, Bondre A, Chand P, Chaturvedi N, Choudhary S, Currey D, Dutt S, Khan A, Kumar M, Gupta S, Nagendra S, Reddy P, Rozatkar A, Scheuer L, Sen Y, Shrivastava R, Singh R, Thirthalli J, Tugnawat D, Bhan A, Naslund J, Patel V, Keshavan M, Mehta U, Torous J. Smartphone digital phenotyping, surveys, and cognitive assessments for global mental health: Initial data and clinical correlations from an international first episode psychosis study. DIGITAL HEALTH 2022;8:205520762211337 View
  12. Moura I, Teles A, Viana D, Marques J, Coutinho L, Silva F. Digital Phenotyping of Mental Health using multimodal sensing of multiple situations of interest: A Systematic Literature Review. Journal of Biomedical Informatics 2023;138:104278 View
  13. Kwon S, Firth J, Joshi D, Torous J. Accessibility and availability of smartphone apps for schizophrenia. Schizophrenia 2022;8(1) View
  14. Pap I, Oniga S. A Review of Converging Technologies in eHealth Pertaining to Artificial Intelligence. International Journal of Environmental Research and Public Health 2022;19(18):11413 View
  15. Lee D, Kim C, Lee S, Son S, Cho S, Cho Y, Lim J, Park R. Psychosis Relapse Prediction Leveraging Electronic Health Records Data and Natural Language Processing Enrichment Methods. Frontiers in Psychiatry 2022;13 View
  16. Cochran J, Fang H, Le Gallo C, Peters-Strickland T, Lindenmayer J, Reuteman-Fowler J. Participant Engagement and Symptom Improvement: Aripiprazole Tablets with Sensor for the Treatment of Schizophrenia. Patient Preference and Adherence 2022;Volume 16:1805 View
  17. 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
  18. Carlier C, Niemeijer K, Mestdagh M, Bauwens M, Vanbrabant P, Geurts L, van Waterschoot T, Kuppens P. In Search of State and Trait Emotion Markers in Mobile-Sensed Language: Field Study. JMIR Mental Health 2022;9(2):e31724 View
  19. Miller M, Raugh I, Strauss G, Harvey P. Remote digital phenotyping in serious mental illness: Focus on negative symptoms, mood symptoms, and self-awareness. Biomarkers in Neuropsychiatry 2022;6:100047 View
  20. Harvey P, Depp C, Rizzo A, Strauss G, Spelber D, Carpenter L, Kalin N, Krystal J, McDonald W, Nemeroff C, Rodriguez C, Widge A, Torous J. Technology and Mental Health: State of the Art for Assessment and Treatment. American Journal of Psychiatry 2022;179(12):897 View
  21. Torous J, Bucci S, Bell I, Kessing L, Faurholt‐Jepsen M, Whelan P, Carvalho A, Keshavan M, Linardon J, Firth J. The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality. World Psychiatry 2021;20(3):318 View
  22. Gumley A, Bradstreet S, Ainsworth J, Allan S, Alvarez-Jimenez M, Aucott L, Birchwood M, Briggs A, Bucci S, Cotton S, Engel L, French P, Lederman R, Lewis S, Machin M, MacLennan G, McLeod H, McMeekin N, Mihalopoulos C, Morton E, Norrie J, Schwannauer M, Singh S, Sundram S, Thompson A, Williams C, Yung A, Farhall J, Gleeson J. The EMPOWER blended digital intervention for relapse prevention in schizophrenia: a feasibility cluster randomised controlled trial in Scotland and Australia. The Lancet Psychiatry 2022;9(6):477 View
  23. Coutts F, Koutsouleris N, McGuire P. Psychotic disorders as a framework for precision psychiatry. Nature Reviews Neurology 2023 View
  24. Yang Y, Xu F, Chen J, Tao C, Li Y, Chen Q, Tang S, Lee H, Shen W. Artificial intelligence-assisted smartphone-based sensing for bioanalytical applications: A review. Biosensors and Bioelectronics 2023;229:115233 View
  25. Nghiem J, Adler D, Estrin D, Livesey C, Choudhury T. Understanding Mental Health Clinicians’ Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study. JMIR Formative Research 2023;7:e47380 View
  26. Frank A, Li R, Peterson B, Narayanan S. Wearable and Mobile Technologies for the Evaluation and Treatment of Obsessive-Compulsive Disorder: Scoping Review. JMIR Mental Health 2023;10:e45572 View
  27. Caballero N, Machiraju S, Diomino A, Kennedy L, Kadivar A, Cadenhead K. Recent Updates on Predicting Conversion in Youth at Clinical High Risk for Psychosis. Current Psychiatry Reports 2023;25(11):683 View
  28. Roumeliotis A, Kaselimi M, Papafragkakis A, Panagopoulos A, Doulamis N. In-Excess Attenuation Detection Using Satellite Link Channel Measurements at Ka- and Q-Bands With Deep-Learning Architectures. IEEE Transactions on Antennas and Propagation 2023;71(8):6839 View
  29. Fried E, Proppert R, Rieble C. Building an early warning system for depression: Rationale, objectives, and methods of the WARN-D study. Clinical Psychology in Europe 2023;5(3) View
  30. Bufano P, Laurino M, Said S, Tognetti A, Menicucci D. Digital Phenotyping for Monitoring Mental Disorders: Systematic Review. Journal of Medical Internet Research 2023;25:e46778 View
  31. 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
  32. Vairavan S, Rashidisabet H, Li Q, Ness S, Morrison R, Soares C, Uher R, Frey B, Lam R, Kennedy S, Trivedi M, Drevets W, Narayan V. Personalized relapse prediction in patients with major depressive disorder using digital biomarkers. Scientific Reports 2023;13(1) View
  33. 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
  34. Wu T, Sherman G, Giorgi S, Thanneeru P, Ungar L, Kamath P, Simonetto D, Curtis B, Shah V. Smartphone sensor data estimate alcohol craving in a cohort of patients with alcohol-associated liver disease and alcohol use disorder. Hepatology Communications 2023;7(12) View
  35. Wang Y, Zuo J, Duan C, Peng H, Huang J, Zhao L, Zhang L, Dong Z. Large language models assisted multi-effect variants mining on cerebral cavernous malformation familial whole genome sequencing. Computational and Structural Biotechnology Journal 2024;23:843 View
  36. Hill J, Revankar G, Singh V, Kerber-Folstrom M, Fortuna K. Predicting Onset of Visual Hallucinations Using Pareidolias: A Qualitative Exploration of the Ethics of a Digital App to Detect a Possible Biomarker. Journal of Technology in Behavioral Science 2024 View
  37. Bryan A, Heinz M, Salzhauer A, Price G, Tlachac M, Jacobson N. Behind the Screen: A Narrative Review on the Translational Capacity of Passive Sensing for Mental Health Assessment. Biomedical Materials & Devices 2024;2(2):778 View
  38. 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
  39. Formica M, Fuller-Tyszkiewicz M, Reininghaus U, Kempton M, Delespaul P, de Haan L, Nelson B, Mikocka-Walus A, Olive L, Ruhrmann S, Rutten B, Riecher-Rössler A, Sachs G, Valmaggia L, van der Gaag M, McGuire P, van Os J, Hartmann J. Associations between disturbed sleep and attenuated psychotic experiences in people at clinical high risk for psychosis. Psychological Medicine 2024:1 View
  40. Choi A, Ooi A, Lottridge D. Digital Phenotyping for Stress, Anxiety, and Mild Depression: Systematic Literature Review. JMIR mHealth and uHealth 2024;12:e40689 View
  41. Cohen A, Naslund J, Lane E, Bhan A, Rozatkar A, Mehta U, Vaidyam A, Byun A, Barnett I, Torous J. Digital phenotyping data and anomaly detection methods to assess changes in mood and anxiety symptoms across a transdiagnostic clinical sample. Acta Psychiatrica Scandinavica 2024 View
  42. 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

Books/Policy Documents

  1. Schneider H. Artificial Intelligence in Medicine. View
  2. Schneider H. Artificial Intelligence in Medicine. View
  3. Opoku Asare K, Visuri A, Vega J, Ferreira D. Wireless Mobile Communication and Healthcare. View
  4. Volpe U, Elkholy H, Gargot T, Pinto da Costa M, Orsolini L. Tasman’s Psychiatry. View