A study of Cardiac reference values and environmental factors using big data and deep learning methods
Bibliographic record
Abstract
Abstract AimsThe purpose of this study was to simulate the Left Ventricular Ejection Fraction (LVEF) reference values with heart rate and environmental data using LSTM deep learning methods and to derive the spatial distribution of LVEF reference values in China.MethodsHeart rate and environmental factors were used as independent variables, and LVEF indicator values were used as the dependent variable. After the sample data were randomly sampled, the block acquisition data were converted into sample sequence data. Once the sequence data were processed, the sample data were input into the long- and short-term memory networks for deep learning parameter training. After repeated weighting parameter adjustment training and optimization, the optimal prediction model for adult LVEF reference values were derived.ResultsThe LVEF reference values of normal adults showed a downward trend from moving from the south to north in China. LVEF is negatively correlated with heart rate and annual air temperature range; while positively correlated with annual mean air temperature, annual mean relative humidity, annual precipitation.ConclusionsThe LVEF reference values are related to heart rate and environmental factors. According to the predicted LSTM model, if the environmental factors of a region is known, combined with the heart rate data from big data, the model can be used to obtain a more accurate LVEF reference value prediction model. A predicted LSTM model is more capable of taking geographical and individual differences into account.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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".