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

Digital Biomarkers and Digital Phenotyping (198) mHealth for Wellness, Behavior Change and Prevention (2923) Machine Learning (1505) mHealth for Data Collection and Research (982) Mobile Health in Psychiatry (438) Methods and New Tools in Mental Health Research (464) Diagnostic Tools in Mental Health (249) Depression and Mood Disorders; Suicide Prevention (1393)

Published on in Vol 9, No 3 (2021): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/24465, first published .
Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach

Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach

Predicting Emotional States Using Behavioral Markers Derived From Passively Sensed Data: Data-Driven Machine Learning Approach

Authors of this article:

Emese Sükei1 Author Orcid Image ;   Agnes Norbury2 Author Orcid Image ;   M Mercedes Perez-Rodriguez2 Author Orcid Image ;   Pablo M Olmos1, 3 Author Orcid Image ;   Antonio Artés1, 3 Author Orcid Image
Article Authors Cited by (21) Tweetations (5) Metrics

Journals

  1. Schueller S, Neary M, Lai J, Epstein D. Understanding People’s Use of and Perspectives on Mood-Tracking Apps: Interview Study. JMIR Mental Health 2021;8(8):e29368 View
  2. Sharma S, Sharma S, Singh R, Gehlot A, Priyadarshi N, Twala B. Deep Recurrent Neural Network Assisted Stress Detection System for Working Professionals. Applied Sciences 2022;12(17):8678 View
  3. 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
  4. 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
  5. 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
  6. Hirten R, Suprun M, Danieletto M, Zweig M, Golden E, Pyzik R, Kaur S, Helmus D, Biello A, Landell K, Rodrigues J, Bottinger E, Keefer L, Charney D, Nadkarni G, Suarez-Farinas M, Fayad Z. A machine learning approach to determine resilience utilizing wearable device data: analysis of an observational cohort. JAMIA Open 2023;6(2) View
  7. Boolani A, Gruber A, Torad A, Stamatis A. Identifying Current Feelings of Mild and Moderate to High Depression in Young, Healthy Individuals Using Gait and Balance: An Exploratory Study. Sensors 2023;23(14):6624 View
  8. Saylam B, İncel Ö. Quantifying Digital Biomarkers for Well-Being: Stress, Anxiety, Positive and Negative Affect via Wearable Devices and Their Time-Based Predictions. Sensors 2023;23(21):8987 View
  9. Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu A, Al Muhanna D, Al-Muhanna F. A Review of the Role of Artificial Intelligence in Healthcare. Journal of Personalized Medicine 2023;13(6):951 View
  10. Lee T, Lee H, Lee J, Kim J. Ensemble Approach to Combining Episode Prediction Models Using Sequential Circadian Rhythm Sensor Data from Mental Health Patients. Sensors 2023;23(20):8544 View
  11. Sedlakova J, Daniore P, Horn Wintsch A, Wolf M, Stanikic M, Haag C, Sieber C, Schneider G, Staub K, Alois Ettlin D, Grübner O, Rinaldi F, von Wyl V, Sarmiento R. Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review. PLOS Digital Health 2023;2(10):e0000347 View
  12. Hurley M, Sonig A, Herrington J, Storch E, Lázaro-Muñoz G, Blumenthal-Barby J, Kostick-Quenet K. Ethical considerations for integrating multimodal computer perception and neurotechnology. Frontiers in Human Neuroscience 2024;18 View
  13. Chow P, Cohn W, Finan P, Eton D, Anderson R. Investigating psychological mechanisms linking pain severity to depression symptoms in women cancer survivors at a cancer center with a rural catchment area. Supportive Care in Cancer 2024;32(3) View
  14. Zhang Y, Li D, Li X, Zhou X, Newman G. The integration of geographic methods and ecological momentary assessment in public health research: A systematic review of methods and applications. Social Science & Medicine 2024;354:117075 View
  15. 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
  16. 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
  17. Lim D, Jeong J, Song Y, Cho C, Yeom J, Lee T, Lee J, Lee H, Kim J. Accurately predicting mood episodes in mood disorder patients using wearable sleep and circadian rhythm features. npj Digital Medicine 2024;7(1) View

Books/Policy Documents

  1. Sharma R, Gulati A, Chopra K. Artificial Intelligence and Machine Learning in Healthcare. View
  2. Narayanro P, Srilakshmi R, Deepika M, Lalitha Surya Kumari P. Optimized Predictive Models in Healthcare Using Machine Learning. View
  3. Dhulkhed V, Kumbhar S. Internet of Medicine for Smart Healthcare. View