A Markovian Aging Process Forecasting Model: Predicting U.S. Mortality
Bibliographic record
Abstract
In this study, we provide a novel finite-state Markov model for predicting death rates. The Markovian physiological age, which forms the basis of this model, is represented by the states in the underlying continuous-time Markov chain. This model forecasts mortality rates for the Markovian physiological age, for which mortality rates for calendar ages can be easily computed. A set of data from the U.S. population is used to calibrate the model. The data collection includes individuals aged 30 to 108 and covers the years 1970 to 2019. We train the model using data from 1970 to 2014 and then test it using data from 2015 to 2019. Based on metrics utilized for training and test datasets, the suggested model outperforms the models of both Lee and Carter (Citation1992) and Renshaw and Haberman (Citation2006). It is noteworthy that this method uses fewer model parameters than the comparable models. Forecasts of mortality rates and life expectancy are made using the findings. According to the findings, a 30-year-old’s life expectancy in 2040, 2060, and 2080 will be 55, 59, and 63 years, respectively, which is longer than the basic Lee-Carter model predicted. The model in this work, unlike the Lee Carter model, is identifiable.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".