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Deep learning for survival outcomes.

Deep learning is a class of machine learning algorithms that are popular for building risk prediction models. When observations are censored, the outcomes are only partially observed and standard deep learning algorithms cannot be directly applied. We develop a new class of deep learning algorithms...

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Published in:Statistics in Medicine Vol. 39; no. 17; pp. 2339 - 2350
Main Authors: Steingrimsson, Jon Arni, Morrison, Samantha
Format: Journal Article
Published: Wiley-Blackwell 7/30/2020
Online Access:View this record in EBSCOhost
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      dt: 7/30/2020
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        10.1002/sim.8542
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        atl: Deep learning for survival outcomes.
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        au:
          Steingrimsson, Jon Arni
          Morrison, Samantha
        affil: Department of Biostatistics, Brown University, Providence Rhode Island, USA
      sug:
        subj:
          Algorithms
          Software
          Survival Analysis
          Probability
      ab: Deep learning is a class of machine learning algorithms that are popular for building risk prediction models. When observations are censored, the outcomes are only partially observed and standard deep learning algorithms cannot be directly applied. We develop a new class of deep learning algorithms for outcomes that are potentially censored. To account for censoring, the unobservable loss function used in the absence of censoring is replaced by a censoring unbiased transformation. The resulting class of algorithms can be used to estimate both survival probabilities and restricted mean survival. We show how the deep learning algorithms can be implemented by adapting software for uncensored data by using a form of response transformation. We provide comparisons of the proposed deep learning algorithms to existing risk prediction algorithms for predicting survival probabilities and restricted mean survival through both simulated datasets and analysis of data from breast cancer patients.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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