Development of LSTM Based Models for Air Pollutant-Related Public Health Effects
Bibliographic record
Abstract
Air pollutants are considered to pose significant risk for public health and are often taken as one of the major concerns in related environmental epidemiology studies.Various statistical methods have been developed to assess the impact of short-term air pollutants exposure on human health, with Generalized Additive Models (GAMs) being the most widely-used models for their health risk response interpretability.However, challenges still exist for GAMs when dealing with multiple air pollutants as well as assessing health outcomes from accumulated exposure impacts with distributed lags.Considering the advancement of neural networks in recent years, this paper proposes a long short-term memory (LSTM) architecture-based model for air pollutant-related public health effect assessment.Datasets from the National Morbidity, Mortality and Air Pollution Study (NMMAPS) program are first prepared, and then an LSTM based health effect model with weighted evaluation of impacts from exposure to air pollutants with distributed lags is presented.Test results show that the proposed model has great potential in assessing the influence of air pollutants on public health effects, taking advantage of accumulative lagging impacts of multiple air pollutants exposure.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".