The project team predicted in-hospital cardiac arrests using different deep learning and machine learning models—Convolutional Neural Network (CNN), Long Short-term Memory (LSTM), Deep Neural Network (DNN), and Light Gradient Boosting Machine (LGBM). I was responsible for managing the Deep Neural Network model. I also took charge of preprocessing the data, fine-tuning the four models, and presenting the final output. We improved the predictive accuracy by 20% compared to the existing model—Deep learning-based Early Warning System (Kwon et al., 2018)—with LSTM (for 6-hour prediction) and LGBM (for 1-hour prediction).
The presentation slides are also available here.
