Opportunities and challenges for biomarker discovery using electronic health record data
Open accessAbstract
Electronic health records (EHRs) have become increasingly relied upon as a source for biomedical research. One important research application of EHRs is the identification of biomarkers associated with specific patient states, especially within complex conditions. However, using EHRs for biomarker identification can be challenging because the EHR was not designed with research as the primary focus. Despite this challenge, the EHR offers huge potential for biomarker discovery research to transform our understanding of disease etiology and treatment and generate biological insights informing precision medicine initiatives. This review paper provides an in-depth analysis of how EHR data is currently used for phenotyping and identifying molecular biomarkers, current challenges and limitations, and strategies we can take to mitigate challenges going forward.
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LLMs play a key role in synthesizing and analyzing large volumes of longitudinal data.
“LLMs, which are a subset of generative AI, play a key role in this process by synthesizing and analyzing large volumes of longitudinal data.”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [102]
LLMs can identify hidden relationships between diabetes and cardiovascular markers by processing both structured and unstructured data, including clinical records, lab results, and physician notes.
“LLMs can identify hidden relationships between diabetes and cardiovascular markers by processing both structured and unstructured data, including clinical records, lab results, and physician notes (102).”
From Generative artificial intelligence in predictive analysis of diabetes and its complications: a narrative review (2025) · cites this paper as [102]
Cites
ChatGPT: five priorities for research
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Imputation of missing values for electronic health record laboratory data
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Osonuga, A., Olawade, D. B., Gore, M., Rotifa, O. B., Egbon, E., Chinwah, V., & Boussios, S. (2025) · Annals of Translational Medicine · doi:10.21037/atm-25-62 · Cited by 4 · see citing works
- Authors
- Singhal, P., Tan, A. L. M., Drivas, T. G., Johnson, K. B., Ritchie, M. D., & Beaulieu-Jones, B. K.
- Affiliations
- University of Pennsylvania (US)
- Harvard University (US)
- Corresponding author
- Ritchie, M.D.
- Venue
- Trends in Molecular Medicine
- Publisher
- Elsevier BV
- Published
- 2023-09
- Volume
- 29
- Issue
- 9
- Pages
- 765-776
- ISSN
- 1471-4914 (ISSN-L)
- PMC
- PMC10530198
- OpenAlex
- W4384700407
- Cited by
- 21
- References
- 3
- Citations by year
- 2026: 8 · 2025: 5 · 2024: 5 · 2023: 3
- FWCI
- 0.44
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- www.elsevier.com/open-access/userlicense/1.0/ (am, 343 d)
- doi.org/10.15223/policy-017 (stm-asf)
- doi.org/10.15223/policy-037 (stm-asf)
- doi.org/10.15223/policy-012 (stm-asf)
- doi.org/10.15223/policy-029 (stm-asf)
- doi.org/10.15223/policy-004 (stm-asf)
- Locations
- Trends in Molecular Medicine (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health (K08DK127247)
- National Institute of Neurological Disorders and Stroke (R00NS114850)
- Burroughs Wellcome Fund
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Physical Sciences › Environmental Science › Health, Toxicology and Mutagenesis › Health, Environment, Cognitive Aging
- Keywords
- electronic health records, biomarker discovery, phenotyping, molecular biomarkers, precision medicine, etiology, clinical research data
- MeSH terms
- Humans, Biomarkers, Biomedical Research, Precision Medicine, Electronic Health Records
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