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
.
Journals
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Kulkarni P, Kirkham R, McNaney R. Opportunities for Smartphone Sensing in E-Health Research: A Narrative Review. Sensors 2022;22(10):3893 View
- 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
- 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
- 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
- Fukazawa Y. Estimating Mental Health Using Human-generated Big Data and Machine Learning. The Brain & Neural Networks 2022;29(2):78 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Terhorst Y, Sander L, Ebert D, Baumeister H. Optimizing the predictive power of depression screenings using machine learning. DIGITAL HEALTH 2023;9 View
- 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
- 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
- Das A, Dhillon P. Application of machine learning in measurement of ageing and geriatric diseases: a systematic review. BMC Geriatrics 2023;23(1) View
- 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
- 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
- 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
- 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
- Ramalho D, Constantino P, Silva H, Constante M, Sanches J. An Augmented Teleconsultation Platform for Depressive Disorders. IEEE Access 2022;10:130563 View
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Terhorst Y, Knauer J, Philippi P, Baumeister H. The Relation between passively collected GPS mobility metrics and depressive symptoms: A systematic review and meta-analysis. (Preprint). Journal of Medical Internet Research 2023 View
- 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
- 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
Books/Policy Documents
- Terhorst Y, Knauer J, Baumeister H. Digital Phenotyping and Mobile Sensing. View
- Opoku Asare K, Visuri A, Vega J, Ferreira D. Wireless Mobile Communication and Healthcare. View
- Ahmed M, Hasan T, Rahman M, Ahmed N. Pervasive Computing Technologies for Healthcare. View
- Tahsin M, Jasim S, Naheen I. Inventive Communication and Computational Technologies. View
- Roy D, Roy A, Roy U. Computational Intelligence in Healthcare Informatics. View
- Ghate R, Walambe R, Kalnad N, Kotecha K. Artificial Intelligence: Theory and Applications. View
- Vidya S, Raju G, Vinayaka Murthy M. Proceedings of 4th International Conference on Recent Trends in Machine Learning, IoT, Smart Cities and Applications. View
- Garatva P, Baumeister H. Psychologische Begutachtung. View