Multivariate Beta Dirichlet Process-based Hidden Markov Models Applied to Medical Applications
Bibliographic record
Abstract
Hidden Markov Models (HMMs) are known as one of the most capable statistical tools applied to various applications. Determining the number of hidden states has been an attractive topic in research. In this work, we assume a state space with a nonparametric structure. Moreover, we suppose emission probabilities follow multivariate Beta distribution (MB) which is a flexible and powerful distribution. We present multivariate Beta Dirichlet process mixture as a nonparametric extension of finite mixture model. This elegant structure provides more capability to model in fitting data. Such modifications will empower the classical framework of HMMs. We name our novel clustering method multivariate Beta Dirichlet process-based HMM. To learn our proposed model, we apply variational inference as a compelling approach. We evaluate our model performance by testing it on two medical applications including dementia detection and analyzing colonoscopy images. The results indicate that our proposed model has good potential and could be applied as a promising technique.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".