Predicting time to first treatment in chronic lymphocytic leukemia using machine learning survival and classification methods

David Chen; Gaurav Goyal; Ronald Go; Sameer A Parikh; Che Ngufor

doi:10.1109/ICHI.2018.00076

Predicting time to first treatment in chronic lymphocytic leukemia using machine learning survival and classification methods

David Chen, Gaurav Goyal, Ronald Go, Sameer A Parikh, Che Ngufor

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

3 Scopus citations

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	https://doi.org/10.1109/ICHI.2018.00076
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

Keywords

Censoring
Machine learning

ASJC Scopus subject areas

Artificial Intelligence
Computer Networks and Communications
Health Informatics

Access to Document

10.1109/ICHI.2018.00076

Fingerprint Dive into the research topics of 'Predicting time to first treatment in chronic lymphocytic leukemia using machine learning survival and classification methods'. Together they form a unique fingerprint.

Cite this

Chen, D., Goyal, G., Go, R., Parikh, S. A., & Ngufor, C. (2018). Predicting time to first treatment in chronic lymphocytic leukemia using machine learning survival and classification methods. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018 (pp. 407-408). [8419409] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICHI.2018.00076

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

Chen, D, Goyal, G, Go, R, Parikh, SA & Ngufor, C 2018, Predicting time to first treatment in chronic lymphocytic leukemia using machine learning survival and classification methods. in Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018., 8419409, Institute of Electrical and Electronics Engineers Inc., pp. 407-408, 6th IEEE International Conference on Healthcare Informatics, ICHI 2018, New York, United States, 6/4/18. https://doi.org/10.1109/ICHI.2018.00076

Chen D, Goyal G, Go R, Parikh SA , Ngufor C. Predicting time to first treatment in chronic lymphocytic leukemia using machine learning survival and classification methods. In Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018. Institute of Electrical and Electronics Engineers Inc. 2018. p. 407-408. 8419409 https://doi.org/10.1109/ICHI.2018.00076

@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 Parikh, {Sameer A} and Che Ngufor",

year = "2018",

month = jul,

day = "24",

doi = "10.1109/ICHI.2018.00076",

language = "English (US)",

pages = "407--408",

booktitle = "Proceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

note = "6th IEEE International Conference on Healthcare Informatics, ICHI 2018 ; Conference date: 04-06-2018 Through 07-06-2018",

}

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.

T2 - 6th IEEE International Conference on Healthcare Informatics, ICHI 2018

Y2 - 4 June 2018 through 7 June 2018

ER -