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 proceedingConference 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 languageEnglish (US)
Title of host publicationProceedings - 2018 IEEE International Conference on Healthcare Informatics, ICHI 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages407-408
Number of pages2
ISBN (Electronic)9781538653777
DOIs
StatePublished - Jul 24 2018
Event6th IEEE International Conference on Healthcare Informatics, ICHI 2018 - New York, United States
Duration: Jun 4 2018Jun 7 2018

Other

Other6th IEEE International Conference on Healthcare Informatics, ICHI 2018
CountryUnited States
CityNew York
Period6/4/186/7/18

Keywords

  • Censoring
  • Machine learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Health Informatics

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  • 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