LSTM-Based Coherent Mortality Forecasting for Developing Countries
Why this work is in the frame
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Bibliographic record
Abstract
This paper studies a long short-term memory (LSTM)-based coherent mortality forecasting method for developing countries or regions. Many of such developing countries have experienced a rapid mortality decline over the past few decades. However, their recent mortality development trend is not necessarily driven by the same factors as their long-term behavior. Hence, we propose a time-varying mortality forecasting model based on the life expectancy and lifespan disparity gap between these developing countries and a selected benchmark group. Here, the mortality improvement trend for developing countries is expected to converge gradually to that of the benchmark group during the projection phase. More specifically, we use a unified deep neural network model with LSTM architecture to project the life expectancy and lifespan disparity difference, which further controls the rotation of the time-varying weight parameters in the model. This approach is applied to three developing countries and three developing regions. The empirical results show that this LSTM-based coherent forecasting method outperforms classical methods, especially for the long-term projections of mortality rates in developing countries.
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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 | 0.002 | 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 | 0.001 | 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) | 0.000 | 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 it