Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide

• URL: http://arxiv.org/abs/2501.10240v1
• Date: Fri, 17 Jan 2025 15:09:57 GMT
• Title: Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide
• Authors: Elena Albu, Shan Gao, Pieter Stijnen, Frank E. Rademakers, Bas C T van Bussel, Taya Collyer, Tina Hernandez-Boussard, Laure Wynants, Ben Van Calster,
• Abstract summary: We list over forty challenges encountered during the stages preceding the model development. These challenges are organized into four categories: cohort definition, outcome definition, feature engineering, and data cleaning. This list is designed to serve as a practical guide for data extraction engineers and researchers.
• Score: 7.861792747606358
• License:
• Abstract: Dynamic predictive modeling using electronic health record (EHR) data has gained significant attention in recent years. The reliability and trustworthiness of such models depend heavily on the quality of the underlying data, which is largely determined by the stages preceding the model development: data extraction from EHR systems and data preparation. We list over forty challenges encountered during these stages and provide actionable recommendations for addressing them. These challenges are organized into four categories: cohort definition, outcome definition, feature engineering, and data cleaning. This list is designed to serve as a practical guide for data extraction engineers and researchers, supporting better practices and improving the quality and real-world applicability of dynamic prediction models in clinical settings.

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