Scholarlink

<p>Recent advances in deep learning have expanded the potential for predictive modeling in survival analysis, particularly in high-dimensional datasets with time-varying covariates. This paper applies deep learning approaches, DeepSurv, DeepHit, and Dynamic DeepHit, to model HIV incidence (time-to-event outcome) using high-dimensional longitudinal data, incorporating time-varying cytokine profiles alongside baseline covariates. We employ the time-dependent concordance index (C-index) and Brier scores to assess the models’ predictive accuracy. We also address missing data using missForest, evaluating model performance on imputed and complete-case datasets. Different strategies for integrating cytokine profiles were explored: DeepSurv and DeepHit utilized derived variables, mean, and difference between the first and last measurements, while Dynamic DeepHit preserved the original time-varying nature of the cytokine data. Our findings demonstrate that retaining the dynamic nature of cytokine covariates, rather than relying on derived summary measures, underscores the robustness and suitability of Dynamic DeepHit as a clinical prediction model, particularly in scenarios where key variables evolve over time.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Deep learning models for the analysis of high-dimensional survival data with time-varying covariates while handling missing data

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月24日

查看全文
  • Sarah Ogutu,
  • Mohanad Mohammed,
  • Henry Mwambi

摘要

Recent advances in deep learning have expanded the potential for predictive modeling in survival analysis, particularly in high-dimensional datasets with time-varying covariates. This paper applies deep learning approaches, DeepSurv, DeepHit, and Dynamic DeepHit, to model HIV incidence (time-to-event outcome) using high-dimensional longitudinal data, incorporating time-varying cytokine profiles alongside baseline covariates. We employ the time-dependent concordance index (C-index) and Brier scores to assess the models’ predictive accuracy. We also address missing data using missForest, evaluating model performance on imputed and complete-case datasets. Different strategies for integrating cytokine profiles were explored: DeepSurv and DeepHit utilized derived variables, mean, and difference between the first and last measurements, while Dynamic DeepHit preserved the original time-varying nature of the cytokine data. Our findings demonstrate that retaining the dynamic nature of cytokine covariates, rather than relying on derived summary measures, underscores the robustness and suitability of Dynamic DeepHit as a clinical prediction model, particularly in scenarios where key variables evolve over time.