Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions
Open accessAbstract
The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper’s overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.
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Patients may discontinue chronic treatment due to lack of rapid and consistent treatment benefits, treatment-related adverse events, and expectations of treatment that are misaligned with the treatment goals of HCPs.
“Patients may discontinue chronic treatment due to lack of rapid and consistent treatment benefits, 37 treatmentrelated adverse events (AEs), [37][38][39][40] and expectations of treatment that are misaligned with the treatment goals of HCPs.41,42”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [42]
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McIntyre, R. S., Greenleaf, W., Bulaj, G., Taylor, S. T., Mitsi, G., Saliu, D., Czysz, A., Silvesti, G., Garcia, M., & Jain, R. (2023) · CNS Spectrums · doi:10.1017/s1092852923002225 · Cited by 38 · see citing works
- Authors
- Richter, T., Fishbain, B., Richter-Levin, G., & Okon-Singer, H.
- Affiliations
- University of Haifa (IL)
- Technion – Israel Institute of Technology (IL)
- Department of Psychology, School of Psychological Sciences, University of Haifa, Haifa 3498838, Israel
- Corresponding author
- Richter, Thalia
- Venue
- Journal of Personalized Medicine
- Publisher
- MDPI AG
- Published
- 2021-09-26
- Volume
- 11
- Issue
- 10
- Pages
- 957
- ISSN
- 2075-4426 (ISSN-L)
- PMC
- PMC8537335
- OpenAlex
- W3203334966
- Cited by
- 52
- References
- 91
- Citations by year
- 2026: 8 · 2025: 14 · 2024: 15 · 2023: 6 · 2022: 9
- FWCI
- 1.33
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Personalized Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Social Sciences › Psychology › Social Psychology › Mental Health via Writing
- Keywords
- depression diagnosis, behavioral data, machine learning, psychiatric diagnosis, social media data, medical data mining, objective assessment, personalized treatment, self-report bias
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