Digital Phenotyping-based Depression Detection in the Presence of Comorbidity: An Uncertainty Reasoning Approach -- the latest research paper of the project "Research on Active Intelligent Screening, Assessment, Intervention Methods, and Privacy Protection Mechanisms for Depression in the Mobile Internet Environment" led by Professor Yan Zhijun from the School of Management, Beijing Institute of Technology (BIT) -- has recently been published in the top international journal in information systems, the Journal of Management Information Systems.

The paper has introduced an uncertainty reasoning approach and explored a digital phenotyping-based depression detection method in the presence of comorbidity.

This research achievement was made through the collaboration of Professor Yan Zhijun's team and Professor Dongsong Zhang from the University of North Carolina at Charlotte.

Depression is an increasingly serious health and social issue. Its detection and diagnosis have always been very challenging, especially for patients with comorbid conditions, the conditions that exist simultaneously but are independent of each other. 

Digital phenotyping, a technology that uses sensors to collect behavioral data for detecting mental illnesses, has emerged as a highly promising tool in the field of automated depression detection. However, existing digital phenotyping-based depression detection methods have not taken into account the diagnostic uncertainty caused by shared similar symptoms between depression and other comorbidities, which could potentially have a negative effect on detection accuracy.

This paper proposes a new deep learning model that outperforms existing models. It introduces a novel AI-based approach to handling uncertainty problems, improving the accuracy of depression detection in comorbid conditions and making significant contributions to the design of scientific and mental health research.

Journal of Management Information Systems is one of the Financial Times 50 Journals, which is widely used to assess the research standards of business schools and enjoys high international recognition. Research papers published in the journal carry considerable influence and win academic recognition.

Paper information:

Fei Peng, Dongsong Zhang, Zhijun Yan (2024) Digital Phenotyping-based Depression Detection in the Presence of Comorbidity: An Uncertainty Reasoning Approach, Journal of Management Information Systems, 41:4, 931-957, DOI: 10.1080/07421222.2024.2415770