EHR-ML: A generalisable pipeline for reproducible clinical outcomes using electronic health records

Abstract

The healthcare landscape is experiencing a transformation with the integration of Artificial Intelligence (AI) into traditional analytic workflows. However, this advancement encounters challenges due to variations in clinical practices, resulting in a crisis of generalisability. Addressing this issue, our proposed solution, EHR-ML, offers an open-source pipeline designed to empower researchers and clinicians. By leveraging institutional Electronic Health Record (EHR) data, EHR-ML facilitates predictive modelling, enabling the generation of clinical insights. EHR-ML stands out for its comprehensive analysis suite, guiding researchers through optimal study design, and its built-in flexibility allowing for construction of robust, customisable models. Notably, EHR-ML integrates a dedicated two-layered ensemble model utilising feature representation learning. Additionally, it includes a feature engineering mechanism to handle intricate temporal signals from physiological measurements. By seamlessly integrating with our quality assurance pipelines, this utility leverages its data standardization and anomaly handling capabilities. Benchmarking analyses demonstrate EHR-ML's efficacy, particularly in predicting outcomes like inpatient mortality and the Intensive Care Unit (ICU) Length of Stay (LOS). Models built with EHR-ML outperformed conventional methods, showcasing its generalisability and versatility even in challenging scenarios such as high class-imbalance. We believe EHR-ML is a critical step towards democratising predictive modelling in healthcare, enabling rapid hypothesis testing and facilitating the generation of biomedical knowledge. Widespread adoption of tools like EHR-ML will unlock the true potential of AI in healthcare, ultimately leading to improved patient care.