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

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. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement AP, NM, GW, and ST acknowledge funding support of Medical Research Future Fund (MRFF) for the SuperbugAI flagship project. YR received Monash Graduate Scholarship for his PhD. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Medical Information Mart for Intensive Care (MIMIC)-IV: https://physionet.org/content/mimiciv/2.2/ eICU Collaborative Research Database: https://eicu-crd.mit.edu/about/eicu/ I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced in the present study are available upon reasonable request to the authors