A Multi-factor Model for the Assessment of Depression Associated with Obstructive Sleep Apnea: A Fuzzy Logic Approach
Bibliographic record
Abstract
Many patients with obstructive sleep apnea (OSA) also exhibit depressive symptoms such as fatigue, anhedonia, weight changes, and depressed or sad mood. Some of these patients are misdiagnosed with clinical depression and treated with antidepressants, which may actually impede OSA treatment. Thus, the assessment of depression is of crucial importance in sleep clinics, and is often used both before and after treatment. As there are no objective ways to measure depression, the most common form of assessment is using subjective, usually self-reporting, questionnaires. These questionnaires were created for assessing and diagnosing clinical depression and not for multiple assessments of depressive symptoms as a secondary medical condition. They are also subject to reporting inaccuracies. In this paper, we introduce STEM-D, a fuzzy logic model for assessing depression in OSA patients that incorporates the multifactorial nature of depression. We studied nine existing questionnaires and created four categories of questions. We modeled the categories using fuzzy variables, with the output variable being the severity of a patient's depression. STEM-D will be used multiple times throughout treatment to monitor a patient's change in depressive symptoms as a result of OSA treatment. This model will be applied in a clinical setting as part of a larger project, CPAP-T*MONITOR.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".