EHR-ML: A data-driven framework for designing machine learning applications with electronic health records

作者：Yashpal Ramakrishnaiah, Nenad Maćešić, Geoffrey I. Webb, Anton Y. Peleg, Sonika Tyagi · 发表于：International Journal of Medical Informatics · 年份：2025 · DOI：10.1016/j.ijmedinf.2025.105816 · 被引用次数：21 · 研究领域：Machine Learning in Healthcare、Artificial Intelligence in Healthcare and Education、Electronic Health Records Systems

The healthcare landscape is experiencing a transformation with the integration of Artificial Intelligence (AI) into traditional analytic workflows. However, its integration faces challenges resulting in a crisis of generalisability. Key obstacles include; 1) Insufficient consideration of local contextual factors, such as institution-specific data formats, practices, and protocols, which can lead to variability in clinical practices across different institutions. 2) ad-hoc data preparation and design of machine learning strategies. 3) manual subjective adjustment of design parameters resulting in sub-optimal performance. 4) EHR specific challenges regarding data biases affecting the model outcomes and unique intermittent temporal nature of the data necessitating specialised handling 5) lack of cross-institutional data validations. To address these challenges, EHR-ML, provides an easy to use structured framework for designing optimum machine learning applications in a data-driven manner. The framework supports ingestion of local institutional electronic health records (EHRs) and process standardisation. The study design and parameter optimisation is done in a fully data-driven evidence-based approach. It seamlessly integrating with existing quality control tools. To handle the unique characteristics of the EHR data, it offers customisable ensemble models. It enables the acquisition of EHR data from diverse systems and harmonise them into common formats following international s...