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Deep learning for the dynamic prediction of multivariate longitudinal and survival data.

Publication ,  Journal Article
Lin, J; Luo, S
Published in: Stat Med
July 10, 2022

The joint model for longitudinal and survival data improves time-to-event predictions by including longitudinal outcome variables in addition to baseline covariates. However, in practice, joint models may be limited by parametric assumptions in both the longitudinal and survival submodels. In addition, computational difficulties arise when considering multiple longitudinal outcomes due to the large number of random effects to be integrated out in the full likelihood. In this article, we discuss several recent machine learning methods for incorporating multivariate longitudinal data for time-to-event prediction. The presented methods use functional data analysis or convolutional neural networks to model the longitudinal data, both of which scale well to multiple longitudinal outcomes. In addition, we propose a novel architecture based on the transformer neural network, named TransformerJM, which jointly models longitudinal and time-to-event data. The prognostic abilities of each model are assessed and compared through both simulation and real data analysis on Alzheimer's disease datasets. Specifically, the models were evaluated based on their ability to dynamically update predictions as new longitudinal data becomes available. We showed that TransformerJM improves upon the predictive performance of existing methods across different scenarios.

Duke Scholars

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

July 10, 2022

Volume

41

Issue

15

Start / End Page

2894 / 2907

Location

England

Related Subject Headings

  • Statistics & Probability
  • Prognosis
  • Neural Networks, Computer
  • Machine Learning
  • Humans
  • Deep Learning
  • Alzheimer Disease
  • 4905 Statistics
  • 4202 Epidemiology
  • 1117 Public Health and Health Services
 

Citation

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Chicago
ICMJE
MLA
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Lin, J., & Luo, S. (2022). Deep learning for the dynamic prediction of multivariate longitudinal and survival data. Stat Med, 41(15), 2894–2907. https://doi.org/10.1002/sim.9392
Lin, Jeffrey, and Sheng Luo. “Deep learning for the dynamic prediction of multivariate longitudinal and survival data.Stat Med 41, no. 15 (July 10, 2022): 2894–2907. https://doi.org/10.1002/sim.9392.
Lin, Jeffrey, and Sheng Luo. “Deep learning for the dynamic prediction of multivariate longitudinal and survival data.Stat Med, vol. 41, no. 15, July 2022, pp. 2894–907. Pubmed, doi:10.1002/sim.9392.
Journal cover image

Published In

Stat Med

DOI

EISSN

1097-0258

Publication Date

July 10, 2022

Volume

41

Issue

15

Start / End Page

2894 / 2907

Location

England

Related Subject Headings

  • Statistics & Probability
  • Prognosis
  • Neural Networks, Computer
  • Machine Learning
  • Humans
  • Deep Learning
  • Alzheimer Disease
  • 4905 Statistics
  • 4202 Epidemiology
  • 1117 Public Health and Health Services