Assessing Latent Risk Based on Joint Modelling of Multiple Health Insurance Outcomes of Mixed Types
Bibliographic record
Abstract
ABSTRACT Our research is motivated by an insurance study involving 788 insurance subscribers who made claims resulting from ischemic heart disease. Four different types of health services used by these subscribers as well as the corresponding total cost were observed for two years. Health care utilizations vary a lot even for subscribers of the same personal characteristics. The research question of primary interest is how to capture patient‐specific latent risks beyond what can be explained by known personal characteristics. In this study, we characterize unobserved latent risks by random effects in our joint Tweedie mixed models for multiple health outcomes of mixed types. An optimal estimation of our model has been developed using the orthodox best linear unbiased predictors of random effects. Our approach is illustrated with the analysis of the health insurance study of 788 ischemic heart disease patients. Applying cluster analysis to the patient‐specific latent risks predicted by our model, we were able to classify patients into a high risk group of 36 patients, a medium risk group of 256 patients and a low risk group of 496 patients. The finding is of important policy relevance since the losses suffered by a few are known to be spread over many in an insurance system. Grouping of patients and prioritization of specific groups based on subject‐specific latent risks facilitates resource allocation and pricing.
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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 | 0.015 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".