A Hybrid Model for Bipolar Disorder Classification from Visual Information
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Bipolar Disorder (BD) is one of the most prevalent mental illnesses in the world. It has a negative impact on people's social and personal functions. The principal indicator of BD is the extreme swing in the mood ranging from manic to depressive states. This paper addresses the challenge of detecting the BD states by monitoring affective information extracted from video recordings of structured interviews. Our goal is to classify the condition of patients suffering from BD into the clinically significant states of remission, hypo-mania, and mania. To this end, we apply a Convolutional Neural Network (CNN) model to extract facial features from video signals. We supply the features' sequence to a Long-Short-Term Memory (LSTM) model to resolve the BD state. Our framework achieved promising results on the development set of the Turkish Audio-Visual Bipolar Disorder Corpus with the Unweighted Average Recall (UAR) of 60.67%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 it