Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide
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arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916654289518592 |
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| author | Albu, Elena Gao, Shan Stijnen, Pieter Rademakers, Frank E. van Bussel, Bas C T Collyer, Taya Hernandez-Boussard, Tina Wynants, Laure Van Calster, Ben |
| author_facet | Albu, Elena Gao, Shan Stijnen, Pieter Rademakers, Frank E. van Bussel, Bas C T Collyer, Taya Hernandez-Boussard, Tina Wynants, Laure Van Calster, Ben |
| contents | Dynamic predictive modelling 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, in part, determined by the stages preceding the model development: data extraction from EHR systems and data preparation. In this article, we identified 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 comprehensive list serves as a practical guide for data extraction engineers and researchers, promoting best practices and improving the quality and real-world applicability of dynamic prediction models in clinical settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_10240 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide Albu, Elena Gao, Shan Stijnen, Pieter Rademakers, Frank E. van Bussel, Bas C T Collyer, Taya Hernandez-Boussard, Tina Wynants, Laure Van Calster, Ben Machine Learning Artificial Intelligence Dynamic predictive modelling 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, in part, determined by the stages preceding the model development: data extraction from EHR systems and data preparation. In this article, we identified 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 comprehensive list serves as a practical guide for data extraction engineers and researchers, promoting best practices and improving the quality and real-world applicability of dynamic prediction models in clinical settings. |
| title | Challenges and recommendations for Electronic Health Records data extraction and preparation for dynamic prediction modelling in hospitalized patients -- a practical guide |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2501.10240 |