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) |
|---|---|
| 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 |
|---|---|
| Country | United States |
| City | New York |
| Period | 6/4/18 → 6/7/18 |
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Keywords
- Censoring
- Machine learning
ASJC Scopus subject areas
- Artificial Intelligence
- Computer Networks and Communications
- Health Informatics
Cite this
Predicting time to first treatment in chronic lymphocytic leukemia using machine learning survival and classification methods. / Chen, David; Goyal, Gaurav; Go, Ronald; Parikh, Sameer A; Ngufor, Che.
Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018. Institute of Electrical and Electronics Engineers Inc., 2018. p. 407-408 8419409.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Predicting time to first treatment in chronic lymphocytic leukemia using machine learning survival and classification methods
AU - Chen, David
AU - Goyal, Gaurav
AU - Go, Ronald
AU - Parikh, Sameer A
AU - Ngufor, Che
PY - 2018/7/24
Y1 - 2018/7/24
N2 - 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.
AB - 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.
KW - Censoring
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85051146610&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85051146610&partnerID=8YFLogxK
U2 - 10.1109/ICHI.2018.00076
DO - 10.1109/ICHI.2018.00076
M3 - Conference contribution
AN - SCOPUS:85051146610
SP - 407
EP - 408
BT - Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
PB - Institute of Electrical and Electronics Engineers Inc.
ER -