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

Digital Biomarkers and Digital Phenotyping (191) Mobile Health in Psychiatry (426) Methods and New Tools in Mental Health Research (454) Machine Learning (1465) Diagnostic Tools in Mental Health (242)

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 (58) Tweetations (6) Metrics

Journals

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  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
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  28. Price G, Heinz M, Collins A, Jacobson N. Detecting major depressive disorder presence using passively-collected wearable movement data in a nationally-representative sample. Psychiatry Research 2024;332:115693 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 a Randomized Comparison Trial. JMIR Research Protocols 2024;13:e58448 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
  41. Terhorst Y, Knauer J, Philippi P, Baumeister H. The Relation Between Passively Collected GPS Mobility Metrics and Depressive Symptoms: Systematic Review and Meta-Analysis. Journal of Medical Internet Research 2024;26:e51875 View
  42. 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
  43. Edler J, Winter M, Steinmetz H, Cohrdes C, Baumeister H, Pryss R. Predicting depressive symptoms using GPS-based regional data: A study with the CORONA HEALTH app during the COVID-19 pandemic in Germany (Preprint). Interactive Journal of Medical Research 2023 View
  44. Benito G, Goldberg X, Brachowicz N, Castaño-Vinyals G, Blay N, Espinosa A, Davidhi F, Torres D, Kogevinas M, de Cid R, Petrone P. Machine learning for anxiety and depression profiling and risk assessment in the aftermath of an emergency. Artificial Intelligence in Medicine 2024;157:102991 View
  45. Das K, Gavade P. A review on the efficacy of artificial intelligence for managing anxiety disorders. Frontiers in Artificial Intelligence 2024;7 View
  46. Terhorst Y, Messner E, Asare K, Montag C, Kannen C, Baumeister H. Which Smartphone-Based Sensing Features Matter in Depression Severity Prediction? Results from an Observation Study. (Preprint). Journal of Medical Internet Research 2023 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