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Plos One
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April 27, 2022
Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies
Daniel A Adler, Fei Wang, David C Mohr, et al.
JMIR Formative Research
|
August 10, 2023
Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study
Jodie Nghiem, Daniel A Adler, Deborah Estrin, et al.
JMIR Formative Research
|
March 21, 2022
Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study
Lisa Militello, Michael Sobolev, Fabian Okeke, et al.
Bjpsych Open
|
March 3, 2022
A call for open data to develop mental health digital biomarkers
Daniel A Adler, Fei Wang, David C Mohr, et al.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
|
April 21, 2022
Identifying Mobile Sensing Indicators of Stress-Resilience
Daniel A Adler, Vincent W-S Tseng, Gengmo Qi, et al.
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI Conference
|
July 3, 2025
Designing Technologies for Value-based Mental Healthcare: Centering Clinicians' Perspectives on Outcomes Data Specification, Collection, and Use
Daniel A Adler, Yuewen Yang, Thalia Viranda, et al.
Npj Mental Health Research
|
April 22, 2024
Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data
Daniel A Adler, Caitlin A Stamatis, Jonah Meyerhoff, et al.
Research Square
|
May 15, 2024
Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data
Daniel A Adler, Caitlin A Stamatis, Jonah Meyerhoff, et al.
Proceedings of the ACM on Human-Computer Interaction
|
January 30, 2023
Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians
Daniel A Adler, Emily Tseng, Khatiya C Moon, et al.
JMIR Mhealth and Uhealth
|
September 1, 2020
Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks
Daniel A Adler, Dror Ben-Zeev, Vincent W-S Tseng, et al.
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Search research articles
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Showing results (1-10 of 12) with videos related to
Sort By:
Page
of 2
Plos One
|
April 27, 2022
Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies
Daniel A Adler, Fei Wang, David C Mohr, et al.
JMIR Formative Research
|
August 10, 2023
Understanding Mental Health Clinicians' Perceptions and Concerns Regarding Using Passive Patient-Generated Health Data for Clinical Decision-Making: Qualitative Semistructured Interview Study
Jodie Nghiem, Daniel A Adler, Deborah Estrin, et al.
JMIR Formative Research
|
March 21, 2022
Digital Prompts to Increase Engagement With the Headspace App and for Stress Regulation Among Parents: Feasibility Study
Lisa Militello, Michael Sobolev, Fabian Okeke, et al.
Bjpsych Open
|
March 3, 2022
A call for open data to develop mental health digital biomarkers
Daniel A Adler, Fei Wang, David C Mohr, et al.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies
|
April 21, 2022
Identifying Mobile Sensing Indicators of Stress-Resilience
Daniel A Adler, Vincent W-S Tseng, Gengmo Qi, et al.
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. CHI Conference
|
July 3, 2025
Designing Technologies for Value-based Mental Healthcare: Centering Clinicians' Perspectives on Outcomes Data Specification, Collection, and Use
Daniel A Adler, Yuewen Yang, Thalia Viranda, et al.
Npj Mental Health Research
|
April 22, 2024
Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data
Daniel A Adler, Caitlin A Stamatis, Jonah Meyerhoff, et al.
Research Square
|
May 15, 2024
Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data
Daniel A Adler, Caitlin A Stamatis, Jonah Meyerhoff, et al.
Proceedings of the ACM on Human-Computer Interaction
|
January 30, 2023
Burnout and the Quantified Workplace: Tensions around Personal Sensing Interventions for Stress in Resident Physicians
Daniel A Adler, Emily Tseng, Khatiya C Moon, et al.
JMIR Mhealth and Uhealth
|
September 1, 2020
Predicting Early Warning Signs of Psychotic Relapse From Passive Sensing Data: An Approach Using Encoder-Decoder Neural Networks
Daniel A Adler, Dror Ben-Zeev, Vincent W-S Tseng, et al.
Page
of 2