ArticleBMC infectious diseases2024
Evaluating the effectiveness of self-attention mechanism in tuberculosis time series forecasting.
Article in BMC infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Transmission dynamics and control of tuberculosis in high-altitude regions: a modelling study in Xizang, China.BMJ open · 2026Article
- Global to local burdens, inequalities, and achievable frontiers of child and adolescent malignant neoplasm of bone and articular cartilage across 953 countries and sublocations, 1980-2040, with deep learning-based forecasts.Journal of orthopaedic translation · 2026Article
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8 authors.
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Abstract
backgroundWith the increasing impact of tuberculosis on public health, accurately predicting future tuberculosis cases is crucial for optimizing of health resources and medical service allocation. This study applies a self-attention mechanism to predict the number of tuberculosis cases, aiming to evaluate its effectiveness in forecasting.
methodsMonthly tuberculosis case data from Changde City between 2010 and 2021 were used to construct a self-attention model, a long short-term memory (LSTM) model, and an autoregressive integrated moving average (ARIMA) model. The performance of these models was evaluated using three metrics: root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE).
resultsThe self-attention model outperformed the other models in terms of prediction accuracy. On the test set, the RMSE of the self-attention model was approximately 7.41% lower than that of the LSTM model, MAE was reduced by about 10.99%, and MAPE was reduced by approximately 9.87%. Compared to the ARIMA model, RMSE was reduced by about 28.86%, MAE by about 32.22%, and MAPE by approximately 29.89%.
conclusionThe self-attention model can effectively improve the prediction accuracy of tuberculosis cases, providing guidance for health departments optimizing of health resources and medical service allocation.
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