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

Digital Biomarkers and Digital Phenotyping (198) Mobile Health in Psychiatry (438) Methods and New Tools in Mental Health Research (463) Machine Learning (1501) Diagnostic Tools in Mental Health (248)

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 (61) Tweetations (6) Metrics

Journals

  1. Vega J, Bell B, Taylor C, Xie J, Ng H, Honary M, McNaney R. Detecting Mental Health Behaviors Using Mobile Interactions: Exploratory Study Focusing on Binge Eating. JMIR Mental Health 2022;9(4):e32146 View
  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
  3. Richter T, Fishbain B, Richter-Levin G, Okon-Singer H. Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions. Journal of Personalized Medicine 2021;11(10):957 View
  4. Hong J, Kim J, Kim S, Oh J, Lee D, Lee S, Uh J, Yoon J, Choi Y. Depressive Symptoms Feature-Based Machine Learning Approach to Predicting Depression Using Smartphone. Healthcare 2022;10(7):1189 View
  5. Choudhary S, Thomas N, Ellenberger J, Srinivasan G, Cohen R. A Machine Learning Approach for Detecting Digital Behavioral Patterns of Depression Using Nonintrusive Smartphone Data (Complementary Path to Patient Health Questionnaire-9 Assessment): Prospective Observational Study. JMIR Formative Research 2022;6(5):e37736 View
  6. Choudhary S, Srinivasan G. The Importance of Using Binary Classification Models in Predicting Depression from a Machine Learning Perspective. Digital Medicine and Healthcare Technology 2022;2022:1 View
  7. Vega J, Li M, Aguillera K, Goel N, Joshi E, Khandekar K, Durica K, Kunta A, Low C. Reproducible Analysis Pipeline for Data Streams: Open-Source Software to Process Data Collected With Mobile Devices. Frontiers in Digital Health 2021;3 View
  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
  11. Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. Sensors 2022;22(10):3893 View
  12. Maatoug R, Oudin A, Adrien V, Saudreau B, Bonnot O, Millet B, Ferreri F, Mouchabac S, Bourla A. Digital phenotype of mood disorders: A conceptual and critical review. Frontiers in Psychiatry 2022;13 View
  13. Girousse E, Vuillerme N. The Use of Passive Smartphone Data to Monitor Anxiety and Depression Among College Students in Real-World Settings: Protocol for a Systematic Review. JMIR Research Protocols 2022;11(12):e38785 View
  14. 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
  15. Fukazawa Y. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78 View
  16. Medvedev I, Ustyuzhanin V, Zinkina J, Korotayev A. Machine Learning for Ranking Factors of Global and Regional Protest Destabilization with a Special Focus on Afrasian Instability Macrozone. Comparative Sociology 2022;21(5):604 View
  17. Attai K, Amannejad Y, Vahdat Pour M, Obot O, Uzoka F. A Systematic Review of Applications of Machine Learning and Other Soft Computing Techniques for the Diagnosis of Tropical Diseases. Tropical Medicine and Infectious Disease 2022;7(12):398 View
  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
  19. Fernandes G, Choi A, Schauer J, Pfammatter A, Spring B, Darwiche A, Alshurafa N. An Explainable Artificial Intelligence Software Tool for Weight Management Experts (PRIMO): Mixed Methods Study. Journal of Medical Internet Research 2023;25:e42047 View
  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
  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
  29. Stamatis C, Meyerhoff J, Meng Y, Lin Z, Cho Y, Liu T, Karr C, Liu T, Curtis B, Ungar L, Mohr D. Differential temporal utility of passively sensed smartphone features for depression and anxiety symptom prediction: a longitudinal cohort study. npj Mental Health Research 2024;3(1) View
  30. Ye G, Chen T, Hung Nguyen Q, Yin H. Heterogeneous decentralised machine unlearning with seed model distillation. CAAI Transactions on Intelligence Technology 2024;9(3):608 View
  31. Ramalho D, Constantino P, Silva H, Constante M, Sanches J. An Augmented Teleconsultation Platform for Depressive Disorders. IEEE Access 2022;10:130563 View
  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 in Germany With the CORONA HEALTH App During the COVID-19 Pandemic: Cross-Sectional Study. Interactive Journal of Medical Research 2024;13:e53248 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
  47. Juergensen V, Peter L, Steyrl D, Lor C, Bui A, McLaren T, Muehlan H, Tomczyk S, Schmidt S, Schomerus G. The help-seeking process and predictors of mental health care use among individuals with depressive symptoms: a machine learning approach. Frontiers in Public Health 2024;12 View
  48. Patel J, Hung C, Katapally T. Evaluating predictive artificial intelligence approaches used in mobile health platforms to forecast mental health symptoms among youth: a systematic review. Psychiatry Research 2025;343:116277 View
  49. Haley F, Andrews J, Moghaddam N. Advancements and Limitations: A Systematic Review of Remote-Based Deep Learning Predictive Algorithms for Depression. Journal of Technology in Behavioral Science 2024 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