ArticleApplied clinical informatics2016
Preprocessing structured clinical data for predictive modeling and decision support. A roadmap to tackle the challenges.
Article in Applied clinical informatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 12 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
12 citing papers in PubMed.
- Challenges and Recommendations for Electronic Health Records Data Extraction and Preparation for Dynamic Prediction Modeling in Hospitalized Patients: Practical Guide and Tutorial.Journal of medical Internet research · 2025Article
- Development and Validation of Echocardiography Artificial Intelligence Models: A Narrative Review.Journal of clinical medicine · 2025Review
- Five-year dementia prediction and decision support system based on real-world data.Frontiers in aging neuroscience · 2025Article
- Conceptualizing bias in EHR data: A case study in performance disparities by demographic subgroups for a pediatric obesity incidence classifier.PLOS digital health · 2024Article
- Automating Electronic Health Record Data Quality Assessment.Journal of medical systems · 2023Review
- Accuracy of identifying hospital acquired venous thromboembolism by administrative coding: implications for big data and machine learning research.Journal of clinical monitoring and computing · 2022Article
- Developing, implementing and governing artificial intelligence in medicine: a step-by-step approach to prevent an artificial intelligence winter.BMJ health & care informatics · 2022Review
- Leveraging electronic health record data to inform hospital resource management : A systematic data mining approach.Health care management science · 2021Article
- Subcategorizing EHR diagnosis codes to improve clinical application of machine learning models.International journal of medical informatics · 2021Article
- Use of machine learning to transform complex standardized nursing care plan data into meaningful research variables: a palliative care exemplar.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Leveraging the electronic health record to improve dermatologic care delivery: The importance of finding structure in data.Journal of the American Academy of Dermatology · 2020Article
- Presenting predictors and temporal trends of treatment-related outcomes in diabetic ketoacidosis.Pediatric diabetes · 2018Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEHR systems have high potential to improve healthcare delivery and management. Although structured EHR data generates information in machine-readable formats, their use for decision support still poses technical challenges for researchers due to the need to preprocess and convert data into a matrix format. During our research, we observed that clinical informatics literature does not provide guidance for researchers on how to build this matrix while avoiding potential pitfalls.
objectivesThis article aims to provide researchers a roadmap of the main technical challenges of preprocessing structured EHR data and possible strategies to overcome them.
methodsAlong standard data processing stages - extracting database entries, defining features, processing data, assessing feature values and integrating data elements, within an EDPAI framework -, we identified the main challenges faced by researchers and reflect on how to address those challenges based on lessons learned from our research experience and on best practices from related literature. We highlight the main potential sources of error, present strategies to approach those challenges and discuss implications of these strategies.
resultsFollowing the EDPAI framework, researchers face five key challenges: (1) gathering and integrating data, (2) identifying and handling different feature types, (3) combining features to handle redundancy and granularity, (4) addressing data missingness, and (5) handling multiple feature values. Strategies to address these challenges include: cross-checking identifiers for robust data retrieval and integration; applying clinical knowledge in identifying feature types, in addressing redundancy and granularity, and in accommodating multiple feature values; and investigating missing patterns adequately.
conclusionsThis article contributes to literature by providing a roadmap to inform structured EHR data preprocessing. It may advise researchers on potential pitfalls and implications of methodological decisions in handling structured data, so as to avoid biases and help realize the benefits of the secondary use of EHR data.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.