Summary of Analyzing the Variations in Emergency Department Boarding and Testing the Transferability Of Forecasting Models Across Covid-19 Pandemic Waves in Hong Kong: Hybrid Cnn-lstm Approach to Quantifying Building-level Socioecological Risk, by Eman Leung (1) et al.
Analyzing the Variations in Emergency Department Boarding and Testing the Transferability of Forecasting Models across COVID-19 Pandemic Waves in Hong Kong: Hybrid CNN-LSTM approach to quantifying building-level socioecological risk
by Eman Leung, Jingjing Guan, Kin On Kwok, CT Hung, CC. Ching, CK. Chung, Hector Tsang, EK Yeoh, Albert Lee
First submitted to arxiv on: 17 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Physics and Society (physics.soc-ph)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a hybrid CNN-LSTM model for forecasting emergency department boarding times in Hong Kong. The model leverages public-domain data from the Hospital Authority, Department of Health, and Housing Authority to predict ED waiting times. The researchers also investigate how the COVID-19 pandemic affected the healthcare system’s complexity, revealing a stable pattern of interconnectedness among its components using deep transfer learning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps improve patient outcomes and health system performance by developing an effective forecasting model for emergency department boarding times during the COVID-19 pandemic. The hybrid CNN-LSTM model uses public-domain data to predict ED waiting times, making it a valuable tool for healthcare administrators. |
Keywords
» Artificial intelligence » Cnn » Lstm » Transfer learning
