Digital Markers of Mental Health Problems: Phenotyping Across Biological, Psychological, and Environmental Dimensions
Bibliographic record
Abstract
Digital markers of mental health problems have gained attention in research as a result of the growing accessibility and data-generating capabilities of portable digital devices. Through sensors (e.g., geolocating system) and human-device interactions (e.g., keystrokes), smartphones and wearable devices can be used to generate digital indices that aim to capture a person’s mental health states and mental health determinants across biological, psychological, and environmental dimensions. Important advantages of digital (bio)markers include the potential to measure mental health on a day-to-day basis and in the person’s usual environment (rather than in the clinician’s office) and with minimal intervention required from the user. Digital markers can be combined with survey data and other variables as part of tailored predictive models with the aim of helping patients and clinicians better detect, monitor, and manage mental health conditions. In this chapter, we define digital markers in psychiatry and examine their types and applications using examples drawn from the scientific literature. We also consider some of the limitations of existing research, ethical problems, and other barriers to the implementation of digital phenotyping in clinical practice.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.021 | 0.008 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".