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.
Original language | English (US) |
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Title of host publication | Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 407-408 |
Number of pages | 2 |
ISBN (Electronic) | 9781538653777 |
DOIs | |
State | Published - Jul 24 2018 |
Event | 6th IEEE International Conference on Healthcare Informatics, ICHI 2018 - New York, United States Duration: Jun 4 2018 → Jun 7 2018 |
Other
Other | 6th IEEE International Conference on Healthcare Informatics, ICHI 2018 |
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Country | United States |
City | New York |
Period | 6/4/18 → 6/7/18 |
Keywords
- Censoring
- Machine learning
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Health Informatics