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

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

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