@inproceedings{e4ec5246e0ab4e49b6ed6eeef62def3e,
title = "Predicting time to first treatment in chronic lymphocytic leukemia using machine learning survival and classification methods",
abstract = "Dealing with censored data is an important consideration for disease prognosis modeling. This is particularly true when diseases have highly heterogeneous presentations and prognosis. Algorithms used to develop prognostic models must be robust to censored data. We explore methods to deal with censoring in a highly heterogeneous disease - chronic lymphocytic leukemia. Although survival analysis is the standard method for estimating survival times, binary classifiers can potentially yield better predictive accuracy, depending on the outcome specified.",
keywords = "Censoring, Machine learning",
author = "David Chen and Gaurav Goyal and Ronald Go and Sameer Parikh and Che Ngufor",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 6th IEEE International Conference on Healthcare Informatics, ICHI 2018 ; Conference date: 04-06-2018 Through 07-06-2018",
year = "2018",
month = jul,
day = "24",
doi = "10.1109/ICHI.2018.00076",
language = "English (US)",
series = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "407--408",
booktitle = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018",
}