WB-ML-2021

Authors: Konrad Komisarczyk, Kacper Grzymkowski, Jakub Fołtyn

• Our Book Chapter
• Group WB Repo
• Book Repo
• Main WB Repo

Folder structure

• proj1/
  • Analysis of (Yan et al. 2020) article and responses to the article, with a focus on EDA, XGBoost modeling and comparison to other datasets.
• proj2/
  • Things used when creating our WB-Book chapter.
  • Based on (Li et al. 2020) article, reproducing the architecture, proposing modifications and comparing it to XGBoost models.

References

Yan, L., Zhang, H.-T., Goncalves, J., Xiao, Y., Wang, M., Guo, Y., et al. (2020d). An interpretable mortality prediction model for COVID-19 patients. Nature Machine Intelligence, 2(5), 283--288. https://www.nature.com/articles/s42256-020-0180-7

Li, X., Ge, P., Zhu, J., Li, H., Graham, J., Singer, A., et al. (2020). Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables. PeerJ, 8. https://peerj.com/articles/10337/