TDSTF: Transformer-based Diffusion probabilistic model for Sparse Time series Forecasting

Background and objective: In the intensive care unit (ICU), vital sign monitoring is critical, and an accurate predictive system is required. This study will create a novel model to forecast Heart Rate (HR), Systolic Blood Pressure (SBP), and Diastolic Blood Pressure (DBP) in ICU. These vital signs are crucial for prompt interventions for patients. We extracted $24,886$ ICU stays from the MIMIC-III database, which contains data from over $46$ thousand patients, to train and test the model. Methods: The model proposed in this study, areansformerin intensive careabilistic Model for Sparse Time Series Forecasting (TDSTF), uses a deep learning technique called the Transformer. The TDSTF model showed state-of-the-art performance in predicting vital signs in the ICU, outperforming other models' ability to predict distributions of vital signs and being more computationally efficient. The code is available at https://github.com/PingChang818/TDSTF. Results: The results of the study showed that TDSTF achieved a Normalized Average Continuous Ranked Probability Score (NACRPS) of $0.4438$ and a Mean Squared Error (MSE) of $0.4168$, an improvement of $18.9\%$ and $34.3\%$ over the best baseline model, respectively. Conclusion: In conclusion, TDSTF is an effective and efficient solution for forecasting vital signs in the ICU, and it shows a significant improvement compared to other models in the field.