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

Digital Biomarkers and Digital Phenotyping (178) Mobile Health in Psychiatry (403) Methods and New Tools in Mental Health Research (428) Machine Learning (1401) Diagnostic Tools in Mental Health (224)

Published on in Vol 9, No 7 (2021): July

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/26540, first published .
Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study

Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study

Predicting Depression From Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study

Authors of this article:

Kennedy Opoku Asare1 Author Orcid Image ;   Yannik Terhorst2 Author Orcid Image ;   Julio Vega3 Author Orcid Image ;   Ella Peltonen1 Author Orcid Image ;   Eemil Lagerspetz4 Author Orcid Image ;   Denzil Ferreira1 Author Orcid Image
Article Authors Cited by (51) Tweetations (6) Metrics

Journals

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  2. Taywade A, Ramasamy S. (Retracted) Internet of things assisted improved web service to optimize power-sharing for a gadget application. Journal of Electronic Imaging 2022;32(05) View
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  8. Gupta S, Goel L, Singh A, Prasad A, Ullah M, Hošovský A. Psychological Analysis for Depression Detection from Social Networking Sites. Computational Intelligence and Neuroscience 2022;2022:1 View
  9. Philippi P, Baumeister H, Apolinário-Hagen J, Ebert D, Hennemann S, Kott L, Lin J, Messner E, Terhorst Y. Acceptance towards digital health interventions – Model validation and further development of the Unified Theory of Acceptance and Use of Technology. Internet Interventions 2021;26:100459 View
  10. Hossain E, Alazeb A, Almudawi N, Almakdi S, Alshehri M, Gazi Golam Faruque M, Rahman W. Forecasting Mental Stress Using Machine Learning Algorithms. Computers, Materials & Continua 2022;72(3):4945 View
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  18. Yang X, Knights J, Bangieva V, Kambhampati V. Association Between the Severity of Depressive Symptoms and Human-Smartphone Interactions: Longitudinal Study. JMIR Formative Research 2023;7:e42935 View
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  20. Ahmed M, Ahmed N. A Fast and Minimal System to Identify Depression Using Smartphones: Explainable Machine Learning–Based Approach. JMIR Formative Research 2023;7:e28848 View
  21. Seiferth C, Vogel L, Aas B, Brandhorst I, Carlbring P, Conzelmann A, Esfandiari N, Finkbeiner M, Hollmann K, Lautenbacher H, Meinzinger E, Newbold A, Opitz A, Renner T, Sander L, Santangelo P, Schoedel R, Schuller B, Stachl C, Terhorst Y, Torous J, Wac K, Werner-Seidler A, Wolf S, Löchner J. How to e-mental health: a guideline for researchers and practitioners using digital technology in the context of mental health. Nature Mental Health 2023;1(8):542 View
  22. McIntyre R, Greenleaf W, Bulaj G, Taylor S, Mitsi G, Saliu D, Czysz A, Silvesti G, Garcia M, Jain R. Digital health technologies and major depressive disorder. CNS Spectrums 2023;28(6):662 View
  23. Terhorst Y, Sander L, Ebert D, Baumeister H. Optimizing the predictive power of depression screenings using machine learning. DIGITAL HEALTH 2023;9 View
  24. Terhorst Y, Weilbacher N, Suda C, Simon L, Messner E, Sander L, Baumeister H. Acceptance of smart sensing: a barrier to implementation—results from a randomized controlled trial. Frontiers in Digital Health 2023;5 View
  25. Akbarova S, Im M, Kim S, Toshnazarov K, Chung K, Chun J, Noh Y, Kim Y. Improving Depression Severity Prediction from Passive Sensing: Symptom-Profiling Approach. Sensors 2023;23(21):8866 View
  26. Das A, Dhillon P. Application of machine learning in measurement of ageing and geriatric diseases: a systematic review. BMC Geriatrics 2023;23(1) View
  27. Wang Y, Shi M, Zhang J. Value of Chuanjin Qinggan decoction in improving the depressive state of patients with herpes zoster combined with depression. World Journal of Psychiatry 2023;13(12):1037 View
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  32. Lee S, Kim J. Testing the bipolar assumption of Singer-Loomis Type Deployment Inventory for Korean adults using classification and multidimensional scaling. Frontiers in Psychology 2024;14 View
  33. Khattak A, Zhang J, Chan P, Chen F, Almujibah H. Estimating Wind Shear Magnitude Near Runways at Hong Kong International Airport Using an Interpretable Local Cascade Ensemble Strategy. Asia-Pacific Journal of Atmospheric Sciences 2024;60(3):271 View
  34. Zafar F, Fakhare Alam L, Vivas R, Wang J, Whei S, Mehmood S, Sadeghzadegan A, Lakkimsetti M, Nazir Z. The Role of Artificial Intelligence in Identifying Depression and Anxiety: A Comprehensive Literature Review. Cureus 2024 View
  35. Rottstädt F, Becker E, Wilz G, Croy I, Baumeister H, Terhorst Y. Enhancing the acceptance of smart sensing in psychotherapy patients: findings from a randomized controlled trial. Frontiers in Digital Health 2024;6 View
  36. Adler D, Stamatis C, Meyerhoff J, Mohr D, Wang F, Aranovich G, Sen S, Choudhury T. Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data. npj Mental Health Research 2024;3(1) View
  37. Knauer J, Baumeister H, Schmitt A, Terhorst Y. Acceptance of smart sensing, its determinants, and the efficacy of an acceptance-facilitating intervention in people with diabetes: results from a randomized controlled trial. Frontiers in Digital Health 2024;6 View
  38. Ruan Z, Yang P, Huang J, Yang K, Lv Y, Zhang Z. Automatic Depression Detection Among Higher Education Students Based on DeepFM. IEEE Transactions on Instrumentation and Measurement 2024;73:1 View
  39. Holloway I, Wu E, Boka C, Young N, Hong C, Fuentes K, Kärkkäinen K, Beikzadeh M, Avendaño A, Jauregui J, Zhang A, Sevillano L, Fyfe C, Brisbin C, Beltran R, Cordero L, Parsons J, Sarrafzadeh M. Novel Machine-Learning HIV Intervention for Sexual and Gender Minority Young People Who Have Sex with Men (uTECH): Protocol for Randomized Comparison Trial (Preprint). JMIR Research Protocols 2024 View
  40. Janssen Daalen J, van den Bergh R, Prins E, Moghadam M, van den Heuvel R, Veen J, Mathur S, Meijerink H, Mirelman A, Darweesh S, Evers L, Bloem B. Digital biomarkers for non-motor symptoms in Parkinson’s disease: the state of the art. npj Digital Medicine 2024;7(1) View

Books/Policy Documents

  1. Terhorst Y, Knauer J, Baumeister H. Digital Phenotyping and Mobile Sensing. View
  2. Opoku Asare K, Visuri A, Vega J, Ferreira D. Wireless Mobile Communication and Healthcare. View
  3. Ahmed M, Hasan T, Rahman M, Ahmed N. Pervasive Computing Technologies for Healthcare. View
  4. Tahsin M, Jasim S, Naheen I. Inventive Communication and Computational Technologies. View
  5. Roy D, Roy A, Roy U. Computational Intelligence in Healthcare Informatics. View
  6. Ghate R, Walambe R, Kalnad N, Kotecha K. Artificial Intelligence: Theory and Applications. View
  7. Vidya S, Raju G, Vinayaka Murthy M. Proceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. View
  8. Garatva P, Baumeister H. Psychologische Begutachtung. View