Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies
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BACKGROUND: The increasing availability of Electronic Health Record (EHR) data and specifically free-text patient notes presents opportunities for phenotype extraction. Text-mining methods in particular can help disease modeling by mapping named-entities mentions to terminologies and clustering semantically related terms. EHR corpora, however, exhibit specific statistical and linguistic characteristics when compared with corpora in the biomedical literature domain. We focus on copy-and-paste redundancy: clinicians typically copy and paste information from previous notes when documenting a current patient encounter. Thus, within a longitudinal patient record, one expects to observe heavy redundancy. In this paper, we ask three research questions: (i) How can redundancy be quantified in large-scale text corpora? (ii) Conventional wisdom is that larger corpora yield better results in text mining. But how does the observed EHR redundancy affect text mining? Does such redundancy introduce a bias that distorts learned models? Or does the redundancy introduce benefits by highlighting stable and important subsets of the corpus? (iii) How can one mitigate the impact of redundancy on text mining? RESULTS: We analyze a large-scale EHR corpus and quantify redundancy both in terms of word and semantic concept repetition. We observe redundancy levels of about 30% and non-standard distribution of both words and concepts. We measure the impact of redundancy on two standard text-mining applications: collocation identification and topic modeling. We compare the results of these methods on synthetic data with controlled levels of redundancy and observe significant performance variation. Finally, we compare two mitigation strategies to avoid redundancy-induced bias: (i) a baseline strategy, keeping only the last note for each patient in the corpus; (ii) removing redundant notes with an efficient fingerprinting-based algorithm. (a)For text mining, preprocessing the EHR corpus with fingerprinting yields significantly better results. CONCLUSIONS: Before applying text-mining techniques, one must pay careful attention to the structure of the analyzed corpora. While the importance of data cleaning has been known for low-level text characteristics (e.g., encoding and spelling), high-level and difficult-to-quantify corpus characteristics, such as naturally occurring redundancy, can also hurt text mining. Fingerprinting enables text-mining techniques to leverage available data in the EHR corpus, while avoiding the bias introduced by redundancy.
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The over representation of certain SDoH factors because of duplication impairs the training and evaluation of machine learning and deep learning models.
“The over representation of certain SDoH factors because of the duplication impairs the training and evaluation of machine learning and deep learning models81,82.”
From Social determinants of health extraction from clinical notes across institutions using large language models (2025) · cites this paper as [82]
The Background says redundancy skews statistical methods and hampers machine learning by making it difficult to create non-overlapping training and test sets. The Results and discussion also report poorer topic-modeling performance on redundant corpora. The paper does not specifically examine SDoH factors or deep learning models, so the claim extends beyond its findings. (LLM, conf=0.88)
Experimental — accuracy not yet measured
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- Authors
- Cohen, R., Elhadad, M., & Elhadad, N.
- Affiliations
- Ben-Gurion University of the Negev (IL)
- Columbia University (US)
- Corresponding author
- Cohen, Raphael
- Venue
- BMC Bioinformatics
- Published
- 2013-01-16
- Volume
- 14
- Issue
- 1
- Pages
- 10-10
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC3599108
- OpenAlex
- W2146408445
- Cited by
- 121
- References
- 61
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 10 · 2023: 6 · 2022: 13 · 2021: 7 · 2020: 10 · 2019: 10 · 2018: 9 · 2017: 8 · 2016: 10 · 2015: 18 · 2014: 8 · 2013: 2 · 2012: 1
- FWCI
- 5.68
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- U.S. National Library of Medicine
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Social Sciences › General Social Sciences › Computational and Text Analysis Methods
- Keywords
- electronic health records, clinical text, collocation identification, topic modeling, fingerprinting, data cleaning, named entity recognition, phenotype extraction, longitudinal patient records
- MeSH terms
- Algorithms, Humans, Semantics, Health Records, Personal, Data Mining, Electronic Health Records
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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