SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis
This addresses the 'preprocessing gap' for researchers in healthcare AI, facilitating fair model comparisons, though it is incremental as it standardizes existing practices rather than introducing new analytical methods.
The authors tackled the problem of inconsistent preprocessing methodologies in survival analysis using electronic health record data by introducing SurvBench, a standardised open-source pipeline that transforms raw datasets into model-ready tensors, enabling reproducible comparisons and supporting multiple critical care databases and modalities.
Electronic health record (EHR) data present tremendous opportunities for advancing survival analysis through deep learning, yet reproducibility remains severely constrained by inconsistent preprocessing methodologies. We present SurvBench, a comprehensive, open-source preprocessing pipeline that transforms raw PhysioNet datasets into standardised, model-ready tensors for multi-modal survival analysis. SurvBench provides data loaders for three major critical care databases, MIMIC-IV, eICU, and MC-MED, supporting diverse modalities including time-series vitals, static demographics, ICD diagnosis codes, and radiology reports. The pipeline implements rigorous data quality controls, patient-level splitting to prevent data leakage, explicit missingness tracking, and standardised temporal aggregation. SurvBench handles both single-risk (e.g., in-hospital mortality) and competing-risks scenarios (e.g., multiple discharge outcomes). The outputs are compatible with pycox library packages and implementations of standard statistical and deep learning models. By providing reproducible, configuration-driven preprocessing with comprehensive documentation, SurvBench addresses the "preprocessing gap" that has hindered fair comparison of deep learning survival models, enabling researchers to focus on methodological innovation rather than data engineering.