Modeling Chronic Obstructive Pulmonary Disease Progression Using Continuous-Time Hidden Markov Models
Bibliographic record
Abstract
Understanding the progression of chronic diseases, such as chronic obstructive pulmonary disease (COPD), is important to inform early diagnosis, personalized care, and health system management. Data from clinical and administrative systems have the potential to advance this understanding, but traditional methods for modelling disease progression are not well-suited to analyzing data collected at irregular intervals, such as when a patient interacts with a healthcare system. We applied a continuous-time hidden Markov model to irregularly-spaced healthcare utilization events and patient-level characteristics in order to analyze the progression through discrete states of 76,888 patients with COPD. A 4-state model allowed classification of patients into interpretable states of disease progression and generated insights about the role of comorbidities, such as cardiovascular diseases, in accelerating severe trajectories. These results can improve the understanding of the evolution of COPD and point to new hypotheses about chronic disease management and comorbidity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".