用深度学习捕捉长期暴露对生存率的影响，兼顾精度与可解释性。
Exploring Cumulative Effects in Survival Data Using Deep Learning Networks
- 基于深度网络建模时间依赖暴露的动态风险关系
- 发现环境暴露与生存结果存在多年滞后关联，行为变化预示订阅流失
- 适合研究长期健康影响的流行病学与医疗数据科学家
在流行病学研究中，建模随时间变化的暴露因素对生存结果的累积效应面临复杂的时间动态挑战。传统基于样条的统计方法虽有效，但需反复转换数据以调整样条参数，且生存分析计算依赖全量数据，难以处理大规模数据集。现有基于神经网络的生存分析方法侧重准确性，却常忽略累积暴露模式的可解释性。为此，我们提出CENNSurv，一种新型深度学习方法，可从时间依赖数据中捕捉动态风险关系。在两个真实世界数据集上的评估显示，CENNSurv揭示了慢性环境暴露与关键生存结局之间长达数年的滞后关联，以及订阅终止前的关键短期行为转变。这表明CENNSurv在建模复杂时间模式方面具备更高可扩展性。该方法为研究累积效应的研究者提供了兼具实用性与可解释性的工具。
原文摘要 · Abstract (English)
In epidemiological research, modeling the cumulative effects of time-dependent exposures on survival outcomes presents a challenge due to their intricate temporal dynamics. Conventional spline-based statistical methods, though effective, require repeated data transformation for each spline parameter tuning, with survival analysis computations relying on the entire dataset, posing difficulties for large datasets. Meanwhile, existing neural network-based survival analysis methods focus on accuracy but often overlook the interpretability of cumulative exposure patterns. To bridge this gap, we introduce CENNSurv, a novel deep learning approach that captures dynamic risk relationships from time-dependent data. Evaluated on two diverse real-world datasets, CENNSurv revealed a multi-year lagged association between chronic environmental exposure and a critical survival outcome, as well as a critical short-term behavioral shift prior to subscription lapse. This demonstrates CENNSurv's ability to model complex temporal patterns with improved scalability. CENNSurv provides researchers studying cumulative effects a practical tool with interpretable insights.
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