Outcome-Oriented Deep Temporal Phenotyping of Disease Progression

Changhee Lee*, Jem Rashbass, Mihaela Van Der Schaar

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    1 Citation (Scopus)

    Abstract

    Chronic diseases evolve slowly throughout a patient's lifetime creating heterogeneous progression patterns that make clinical outcomes remarkably varied across individual patients. A tool capable of identifying temporal phenotypes based on the patients different progression patterns and clinical outcomes would allow clinicians to better forecast disease progression by recognizing a group of similar past patients, and to better design treatment guidelines that are tailored to specific phenotypes. To build such a tool, we propose a deep learning approach, which we refer to as outcome-oriented deep temporal phenotyping (ODTP), to identify temporal phenotypes of disease progression considering what type of clinical outcomes will occur and when based on the longitudinal observations. More specifically, we model clinical outcomes throughout a patient's longitudinal observations via time-to-event (TTE) processes whose conditional intensity functions are estimated as non-linear functions using a recurrent neural network. Temporal phenotyping of disease progression is carried out by our novel loss function that is specifically designed to learn discrete latent representations that best characterize the underlying TTE processes. The key insight here is that learning such discrete representations groups progression patterns considering the similarity in expected clinical outcomes, and thus naturally provides outcome-oriented temporal phenotypes. We demonstrate the power of ODTP by applying it to a real-world heterogeneous cohort of 11 779 stage III breast cancer patients from the U.K. National Cancer Registration and Analysis Service. The experiments show that ODTP identifies temporal phenotypes that are strongly associated with the future clinical outcomes and achieves significant gain on the homogeneity and heterogeneity measures over existing methods. Furthermore, we are able to identify the key driving factors that lead to transitions between phenotypes which can be translated into actionable information to support better clinical decision-making.

    Original languageEnglish
    Article number9275296
    Pages (from-to)2423-2434
    Number of pages12
    JournalIEEE Transactions on Biomedical Engineering
    Volume68
    Issue number8
    DOIs
    Publication statusPublished - Aug 2021

    Bibliographical note

    Funding Information:
    Manuscript received July 31, 2020; revised November 1, 2020; accepted November 27, 2020. Date of publication December 1, 2020; date of current version July 19, 2021. This work was supported by the National Science Foundation (NSF) under Grant 1722516 and the Office of Naval Q2 Research (ONR). (Corresponding author: Changhee Lee.) Changhee Lee is with the Electrical and Computer Engineering, University of California, Los Angeles, CA 90095 USA (e-mail: chl8856@gmail.com). Jem Rashbass is with the Public Health England, London.

    Publisher Copyright:
    © 1964-2012 IEEE.

    Keywords

    • Deep learning
    • dynamic survival analysis
    • outcome-oriented phenotyping
    • temporal clustering
    • temporal phenotyping
    • time-to-event data

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