Yilin Ning, Siqi Li, Marcus Eng Hock Ong, Feng Xie, Bibhas Chakraborty, Daniel Shu Wei Ting, Nan Liu 0003. A Novel Interpretable Machine Learning System to Generate Clinical Risk Scores: An Application for Predicting Early Mortality or Unplanned Readmission in A Retrospective Cohort Study. In AMIA 2022, American Medical Informatics Association Annual Symposium, Washington, DC, USA, November 5-9, 2022. AMIA, 2022. [doi]
@inproceedings{NingLOXCT022,
title = {A Novel Interpretable Machine Learning System to Generate Clinical Risk Scores: An Application for Predicting Early Mortality or Unplanned Readmission in A Retrospective Cohort Study},
author = {Yilin Ning and Siqi Li and Marcus Eng Hock Ong and Feng Xie and Bibhas Chakraborty and Daniel Shu Wei Ting and Nan Liu 0003},
year = {2022},
url = {https://knowledge.amia.org/76677-amia-1.4637602/f007-1.4641746/f007-1.4641747/365-1.4641922/356-1.4641919},
researchr = {https://researchr.org/publication/NingLOXCT022},
cites = {0},
citedby = {0},
booktitle = {AMIA 2022, American Medical Informatics Association Annual Symposium, Washington, DC, USA, November 5-9, 2022},
publisher = {AMIA},
}