Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission
Not on the map. Why?
Abstract (arXiv)
Survival analysis is essential for studying time-to-event outcomes and providing a dynamic understanding of the probability of an event occurring over time. Various survival analysis techniques, from traditional statistical models to state-of-the-art machine learning algorithms, support healthcare intervention and policy decisions. However, there remains ongoing discussion about their comparative performance. We conducted a comparative study of several survival analysis methods, including Cox proportional hazards (CoxPH), stepwise CoxPH, elastic net penalized Cox model, Random Survival Forests (RSF), Gradient Boosting machine (GBM) learning, AutoScore-Survival, DeepSurv, time-dependent Cox model based on neural network (CoxTime), and DeepHit survival neural network. We applied the concordance index (C-index) for model goodness-of-fit, and integral Brier scores (IBS) for calibration, and considered the model interpretability. As a case study, we performed a retrospective analysis of patients admitted through the emergency department of a tertiary hospital from 2017 to 2019, predicting 90-day all-cause mortality based on patient demographics, clinicopathological features, and historical data. The results of the C-index indicate that deep learning achieved comparable performance, with DeepSurv producing the best discrimination (DeepSurv: 0.893; CoxTime: 0.892; DeepHit: 0.891). The calibration of DeepSurv (IBS: 0.041) performed the best, followed by RSF (IBS: 0.042) and GBM (IBS: 0.0421), all using the full variables. Moreover, AutoScore-Survival, using a minimal variable subset, is easy to interpret, and can achieve good discrimination and calibration (C-index: 0.867; IBS: 0.044). While all models were satisfactory, DeepSurv exhibited the best discrimination and calibration. In addition, AutoScore-Survival offers a more parsimonious model and excellent interpretability.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 9 of its 51 references that are in the map. First posted on arXiv 4 Mar 2024.
Builds on
9 of 51 references.
References · 9
- 2016DeepSurv: Personalized Treatment Recommender System Using A Cox Proportional Hazards Deep Neural Networkcited by 358
- 2019Time-to-Event Prediction with Neural Networks and Cox Regressioncited by 175
- 2017Machine Learning for Survival Analysis: A Surveycited by 112
- 2018Effective Ways to Build and Evaluate Individual Survival Distributionscited by 64
- 2021Benchmarking emergency department triage prediction models with machine learning and large public electronic health recordscited by 31
- 2022High-Dimensional Survival Analysis: Methods and Applicationscited by 16
- 2021AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival datacited by 7
- 2022Predicting Survival Outcomes in the Presence of Unlabeled Datacited by 3
- 2016A Multi-Modal Graph-Based Semi-Supervised Pipeline for Predicting Cancer Survivalcited by 2
Built on by
Citing papers per year: 2026: 1.
1 paper.