# AutoScore-Survival: Developing interpretable machine learning-based time-to-event scores with right-censored survival data

arXiv: https://arxiv.org/abs/2106.06957 · HTML: https://mlsanity.com/paper/2106.06957/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 57 references that are in the map.
- First posted on arXiv 13 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges](https://mlsanity.com/paper/2103.11251/) (2021)
- [Supersparse Linear Integer Models for Optimized Medical Scoring Systems](https://mlsanity.com/paper/1502.04269/) (2015)
- [Learning Optimized Risk Scores](https://mlsanity.com/paper/1610.00168/) (2016)
- [To tune or not to tune the number of trees in random forest?](https://mlsanity.com/paper/1705.05654/) (2017)

## Built on by (most cited first, 7 of 7)

- [Deep learning for temporal data representation in electronic health records: A systematic review of challenges and methodologies](https://mlsanity.com/paper/2107.09951/) (2021)
- [Benchmarking emergency department triage prediction models with machine learning and large public electronic health records](https://mlsanity.com/paper/2111.11017/) (2021)
- [AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data](https://mlsanity.com/paper/2107.06039/) (2021)
- [A novel interpretable machine learning system to generate clinical risk scores: An application for predicting early mortality or unplanned readmission in a retrospective cohort study](https://mlsanity.com/paper/2201.03291/) (2022)
- [Developing Federated Time-to-Event Scores Using Heterogeneous Real-World Survival Data](https://mlsanity.com/paper/2403.05229/) (2024)
- [Survival modeling using deep learning, machine learning and statistical methods: A comparative analysis for predicting mortality after hospital admission](https://mlsanity.com/paper/2403.06999/) (2024)
- [SHAPoint: Task-Agnostic, Efficient, and Interpretable Point-Based Risk Scoring via Shapley Values](https://mlsanity.com/paper/2509.23756/) (2025)

## Abstract (arXiv)

Scoring systems are highly interpretable and widely used to evaluate time-to-event outcomes in healthcare research. However, existing time-to-event scores are predominantly created ad-hoc using a few manually selected variables based on clinician's knowledge, suggesting an unmet need for a robust and efficient generic score-generating method. AutoScore was previously developed as an interpretable machine learning score generator, integrated both machine learning and point-based scores in the strong discriminability and accessibility. We have further extended it to time-to-event data and developed AutoScore-Survival, for automatically generating time-to-event scores with right-censored survival data. Random survival forest provides an efficient solution for selecting variables, and Cox regression was used for score weighting. We illustrated our method in a real-life study of 90-day mortality of patients in intensive care units and compared its performance with survival models (i.e., Cox) and the random survival forest. The AutoScore-Survival-derived scoring model was more parsimonious than survival models built using traditional variable selection methods (e.g., penalized likelihood approach and stepwise variable selection), and its performance was comparable to survival models using the same set of variables. Although AutoScore-Survival achieved a comparable integrated area under the curve of 0.782 (95% CI: 0.767-0.794), the integer-valued time-to-event scores generated are favorable in clinical applications because they are easier to compute and interpret. Our proposed AutoScore-Survival provides an automated, robust and easy-to-use machine learning-based clinical score generator to studies of time-to-event outcomes. It provides a systematic guideline to facilitate the future development of time-to-event scores for clinical applications.