Inferring Symptom State of Generalized Anxiety Disorder: A Bayesian Network Approach
Bibliographic record
Abstract
Instead of viewing psychiatric disorders as latent causes that lead to observable symptoms, a network view of psychiatric disorders argues that each disorder can be regarded as a complex network of interacting symptoms. Such a network view of psychiatric disorders enables the analysis of the inter-dependencies between individual symptoms. Here, I modeled a set of binary symptoms in Generalized Anxiety Disorder (GAD) as a Bayesian network and performed Belief Propagation on this symptom network to infer the potential states of unobserved symptom variables. In the learned symptom network, the interactions between GAD symptoms were directly supported by empirical investigation of the co-occurrences or causal relations between them. The symptom network enabled one to infer the state of unobserved symptom variables given partial observation. Furthermore, predicting symptom states on the Bayesian network out-performed state-of-the-art machine learning methods that did not explicitly model the interdependencies between symptom variables. Together, this study proposes a novel and reliable approach for measuring the risk of certain GAD symptoms for a patient by inferring the likelihood of developing the symptoms of interest on the Bayesian symptom network. The learned symptom network also predicts novel interdependencies between symptoms that can be verified in future empirical research. The Bayesian network model of GAD provides a potential mechanistic account underlying the co-occurrence of symptoms in GAD.
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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 | 0.019 |
| 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".