RHealth: A R Toolkit for Deep Learning in Healthcare

Abstract

Machine learning for electronic health records (EHR) is advancing rapidly and already underpins risk stratification, readmission and mortality prediction, and decision support, yet reliable translation still stalls on fragmented data pipelines, inconsistent medical-code handling, and hard-to-reproduce eval-uation-barriers that especially hinder R-centric clinical teams. Despite impressive methodological gains in temporal modeling, attention mechanisms, and strong classical baselines, most turnkey toolchains live in Python; as a result, many healthcare researchers and clinical data scientists working in R lack a single, integrated path from raw multi-table EHR to calibrated, auditable models. We address this gap with RHealth, an open-source, R-native toolkit that plays the role of an end-to-end conductor: from data harmonization and medical-code normal-ization to task specification, model training, and standardized reporting. Concretely, RHealth provides adapters for widely used public datasets (e.g., MIMIC-III/IV, eICU), utilities to traverse and map ICD-9/10 and CCS codes, task templates for common outcomes (mortality, 30-day readmission, length of stay), and a modeling stack that-at this development stage-supports standard recurrent baselines (e.g., RNN) and offers an extensible interface for user-defined architectures under active development, all evaluated with reproducible splits, AUROC/AUPRC, and cali-bration diagnostics. By packaging the full pipeline-from data to evaluation granularity-into modular, composable components, RHealth lowers the entry barrier for R users, reduces “glue code,” and promotes transparent, people-centric experimentation that can also serve as a trustworthy upstream substrate for LLM-enabled applications. To our knowledge, it is among the first comprehensive, integrated deep-learning toolkits for EHR in the R ecosystem. The code and documentation will be released after the double-blind review process.

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