Selected article for: "neural network and short term memory"
Author: Jo, HyeongChan; Kim, Juhyun; Huang, Tzu-Chen; Biology, Yu-Li Ni Division of; Engineering, Biological; Caltech,; Mathematics, The Division of Physics; Astronomy,; Physics, Walter Burke Institute for Theoretical
Title: condLSTM-Q: A novel deep learning model for predicting Covid-19 mortality in fine geographical Scale
  • Cord-id: nibahhqu
  • Document date: 2020_11_23
    • ID: nibahhqu
    Snippet: Predictive models with a focus on different spatial-temporal scales benefit governments and healthcare systems to combat the COVID-19 pandemic. Here we present the conditional Long Short-Term Memory networks with Quantile output (condLSTM-Q), a well-performing model for making quantile predictions on COVID-19 death tolls at the county level with a two-week forecast window. This fine geographical scale is a rare but useful feature in publicly available predictive models, which would especially be
    Document: Predictive models with a focus on different spatial-temporal scales benefit governments and healthcare systems to combat the COVID-19 pandemic. Here we present the conditional Long Short-Term Memory networks with Quantile output (condLSTM-Q), a well-performing model for making quantile predictions on COVID-19 death tolls at the county level with a two-week forecast window. This fine geographical scale is a rare but useful feature in publicly available predictive models, which would especially benefit state-level officials to coordinate resources within the state. The quantile predictions from condLSTM-Q inform people about the distribution of the predicted death tolls, allowing better evaluation of possible trajectories of the severity. Given the scalability and generalizability of neural network models, this model could incorporate additional data sources with ease, and could be further developed to generate other useful predictions such as new cases or hospitalizations intuitively.

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