%0 Journal Article %1 journals/quanbio/JoKHN22 %A Jo, HyeongChan %A Kim, Juhyun %A Huang, Tzu-Chen %A Ni, Yu-Li %D 2022 %J Quant. Biol. %K dblp %N 2 %P 125 %T condLSTM-Q: A novel deep learning model for predicting COVID-19 mortality in fine geographical scale. %U http://dblp.uni-trier.de/db/journals/quanbio/quanbio10.html#JoKHN22 %V 10

@article{journals/quanbio/JoKHN22, added-at = {2022-09-09T00:00:00.000+0200}, author = {Jo, HyeongChan and Kim, Juhyun and Huang, Tzu-Chen and Ni, Yu-Li}, biburl = {https://www.bibsonomy.org/bibtex/2d827cacc8cc1fa16f0c29ff1fbfc0c16/dblp}, ee = {https://doi.org/10.15302/j-qb-021-0276}, interhash = {07ceb8a462783716c4d71616db0383ef}, intrahash = {d827cacc8cc1fa16f0c29ff1fbfc0c16}, journal = {Quant. Biol.}, keywords = {dblp}, number = 2, pages = 125, timestamp = {2024-04-08T15:29:54.000+0200}, title = {condLSTM-Q: A novel deep learning model for predicting COVID-19 mortality in fine geographical scale.}, url = {http://dblp.uni-trier.de/db/journals/quanbio/quanbio10.html#JoKHN22}, volume = 10, year = 2022 }