Identifying and mitigating biases in EHR laboratory tests
Open accessAbstract
Electronic health record (EHR) data show promise for deriving new ways of modeling human disease states. Although EHR researchers often use numerical values of laboratory tests as features in disease models, a great deal of information is contained in the context within which a laboratory test is taken. For example, the same numerical value of a creatinine test has different interpretation for a chronic kidney disease patient and a patient with acute kidney injury. We study whether EHR research studies are subject to biased results and interpretations if laboratory measurements taken in different contexts are not explicitly separated. We show that the context of a laboratory test measurement can often be captured by the way the test is measured through time. We perform three tasks to study the properties of these temporal measurement patterns. In the first task, we confirm that laboratory test measurement patterns provide additional information to the stand-alone numerical value. The second task identifies three measurement pattern motifs across a set of 70 laboratory tests performed for over 14,000 patients. Of these, one motif exhibits properties that can lead to biased research results. In the third task, we demonstrate the potential for biased results on a specific example. We conduct an association study of lipase test values to acute pancreatitis. We observe a diluted signal when using only a lipase value threshold, whereas the full association is recovered when properly accounting for lipase measurements in different contexts (leveraging the lipase measurement patterns to separate the contexts). Aggregating EHR data without separating distinct laboratory test measurement patterns can intermix patients with different diseases, leading to the confounding of signals in large-scale EHR analyses. This paper presents a methodology for leveraging measurement frequency to identify and reduce laboratory test biases.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
CONCERN EWS's methodological choices, including robust modeling of temporal data patterns and use of nursing assessment and observational data, allowed it to have accurate and robust predictive power and sufficient lead time to alter a patient's clinical trajectory.
“Several methodological choices allowed CONCERN EWS to have accurate and robust predictive power and sufficient lead time to alter a patient’s clinical trajectory as we observed in this trial, including: (1) robust modeling of temporal data patterns40–42 (for example, time of day, day of week and patient hospital day) for health care processes (for example, nurses’ surveillance and medication”
From Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial (2025) · cites this paper as [44]
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredCONCERN EWS overcomes the limitations of physiological measurements by leveraging nursing surveillance patterns and an existing expert knowledge base.
“By leveraging nursing surveillance patterns rather than physiological measures10,44, CONCERN EWS overcomes the limitations of physiological measurements8 and leverages an existing expert knowledge base: nurses’ autonomous decisions to take greater action and document more observations or notes than mandated by policy in direct response to concerning changes in a patient’s clinical state10,13.”
From Real-time surveillance system for patient deterioration: a pragmatic cluster-randomized controlled trial (2025) · cites this paper as [44]
The Introduction says laboratory-test values alone cannot distinguish some disease states, and the Results (section 3.1) find additional information in test-ordering patterns. This supports a limitation of laboratory values, but the citation is attached to the broader phrase “limitations of physiological measurements”; the paper does not establish that broader claim. The sentence’s statements about CONCERN EWS and nursing surveillance are not attributed to this source. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Grundy, S. M., Bilheimer, D. W., Chait, A., Clark, L. T., Denke, M. A., Havel, R. J., Hazzard, W. R., Hulley, S. B., Hunninghake, D. B., Kreisberg, R. A., et al. (1993) · JAMA · doi:10.1001/jama.269.23.3015 · Cited by 27,944 · see citing works
Missing data: our view of the state of the art
Schafer, J. L., & Graham, J. W. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.2.147 · Cited by 11,638 · see citing works
RUBIN, D. B. (1976) · Biometrika · doi:10.1093/biomet/63.3.581 · Cited by 9,925 · see citing works
Practice guidelines in acute pancreatitis
Banks, P. A., Freeman, M. L., & the Practice Parameters Committee of the American College of Gastroenterology (2006) · The American Journal of Gastroenterology · doi:10.1111/j.1572-0241.2006.00856.x · Cited by 2,050 · see citing works
Clinical diagnosis and management by laboratory methods
Henry, J. B. (1979) · Medical Entomology and Zoology · Cited by 1,699 · see citing works
Pattern-Mixture Models for Multivariate Incomplete Data
Little, R. J. A. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10594302 · Cited by 1,026 · see citing works
Next-generation phenotyping of electronic health records
Hripcsak, G., & Albers, D. J. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001145 · Cited by 795 · see citing works
Self-citationAccuracy of ICD-9-CM Codes for Identifying Cardiovascular and Stroke Risk Factors
Birman-Deych, E., Waterman, A. D., Yan, Y., Nilasena, D. S., Radford, M. J., & Gage, B. F. (2005) · Medical Care · doi:10.1097/01.mlr.0000160417.39497.a9 · Cited by 723 · see citing works
the eMERGE Team, McCarty, C. A., Chisholm, R. L., Chute, C. G., Kullo, I. J., Jarvik, G. P., Larson, E. B., Li, R., Masys, D. R., Ritchie, M. D., et al. (2011) · BMC Medical Genomics · doi:10.1186/1755-8794-4-13 · Cited by 692 · see citing works
Hersh, W. R., Weiner, M. G., Embi, P. J., Logan, J. R., Payne, P. R. O., Bernstam, E. V., Lehmann, H. P., Hripcsak, G., Hartzog, T. H., Cimino, J. J., et al. (2013) · Medical Care · doi:10.1097/mlr.0b013e31829b1dbd · Cited by 567 · see citing works
Henry's Clinical Diagnosis and Management by Laboratory Methods
McPherson, E. B. R. A., & Pincus, M. R. (2006) · Medical Entomology and Zoology · Cited by 476 · see citing works
Henry’s Clinical Diagnosis and Management by Laboratory Methods
Chou, D. (2007) · JAMA · doi:10.1001/jama.297.16.1829 · Cited by 406 · see citing works
Lasko, T. A., Denny, J. C., & Levy, M. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066341 · Cited by 313 · see citing works
Prokosch, H. (2009) · doi:10.3414/me9132 · Cited by 262 · see citing works
A Novel Framework for Imputation of Missing Values in Databases
Farhangfar, A., Kurgan, L., & Pedrycz, W. (2007) · IEEE Transactions on Systems Man and Cybernetics - Part A Systems and Humans · doi:10.1109/tsmca.2007.902631 · Cited by 248 · see citing works
Missing data in medical databases: Impute, delete or classify?
Cismondi, F., Fialho, A. S., Vieira, S. M., Reti, S. R., Sousa, J. M. C., & Finkelstein, S. N. (2013) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2013.01.003 · Cited by 200 · see citing works
Exploiting missing clinical data in Bayesian network modeling for predicting medical problems
Lin, J.-H., & Haug, P. J. (2007) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2007.06.001 · Cited by 138 · see citing works
Population-based study of repeat laboratory testing.
van Walraven, C., & Raymond, M. (2003) · Clinical Chemistry · doi:10.1373/clinchem.2003.021220 · Cited by 137 · see citing works
Cohen, R., Elhadad, M., & Elhadad, N. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-10 · Cited by 121 · see citing works
Self-citationChen, Y., Carroll, R. J., Hinz, E. M., Shah, A., Eyler, A. E., Denny, J. C., & Xu, H. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001945 · Cited by 111 · see citing works
Exploiting time in electronic health record correlations
Hripcsak, G., Albers, D., & Perotte, A. (2011) · J. Am. Medical Informatics Assoc. · doi:10.1136/amiajnl-2011-000463 · Cited by 75 · see citing works
Self-citationAutomated knowledge acquisition from clinical narrative reports.
Wang, X., Chused, A. E., Elhadad, N., Friedman, C., & Markatou, M. (2008) · PubMed Central · Cited by 68 · see citing works
Self-citationFarzandipour, M., Sheikhtaheri, A., & Sadoughi, F. (2009) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2009.07.002 · Cited by 63 · see citing works
Computational Approaches to Phenotyping: High-Throughput Phenomics
Lussier, Y. A., & Liu, Y. (2007) · Proceedings of the American Thoracic Society · doi:10.1513/pats.200607-142jg · Cited by 62 · see citing works
Albers, D. J., & Hripcsak, G. (2012) · Chaos: An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/1.3675621 · Cited by 53 · see citing works
Self-citationLyon, A. W., Higgins, T., Wesenberg, J. C., Tran, D. V., & Cembrowski, G. S. (2009) · Journal of Diabetes Science and Technology · doi:10.1177/193229680900300302 · Cited by 44 · see citing works
Albers, D. J., & Hripcsak, G. M. (2012) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2012.03.003 · Cited by 40 · see citing works
Self-citationAlbers, D. J., & Hripcsak, G. (2010) · Physics Letters A · doi:10.1016/j.physleta.2009.12.067 · Cited by 36 · see citing works
Self-citationWei, W., Tao, C., Jiang, G., & Chute, C. G. (2010) · PubMed Central · Cited by 33 · see citing works
Weber, G. M., & Kohane, I. S. (2013) · PLoS ONE · doi:10.1371/journal.pone.0064933 · Cited by 30 · see citing works
Pincus, M. R., & McPherson, R. A. (2011) · Cited by 26 · see citing works
Albers, D. J., Hripcsak, G. M., & Schmidt, J. M. (2012) · PLoS ONE · doi:10.1371/journal.pone.0048058 · Cited by 25 · see citing works
Self-citationPhenome based analysis as a means for discovering context dependent clinical reference ranges.
Warner, J. L., & Alterovitz, G. (2012) · PubMed Central · Cited by 22 · see citing works
Sagreiya, H., & Altman, R. B. (2010) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2010.03.014 · Cited by 22 · see citing works
Abdala, O. T., & Saeed, M. (2005) · Computers in cardiology · doi:10.1109/cic.2004.1443033 · Cited by 16 · see citing works
Predicting the risk and trajectory of intensive care patients using survival models
Hug, C. W. (2006) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 12 · see citing works
Chen, D. P., Dudley, J. T., & Butte, A. J. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-s9-s4 · Cited by 8 · see citing works
Quality assurance study of cardiac isoenzyme utilization in a large teaching hospital.
Saxena, S., Anderson, D. W., Kaufman, R. L., Hannah, J. A., & Wong, E. T. (1993) · PubMed · Cited by 6 · see citing works
Flagged first, then by citations
1 of the 39 references couldn’t be matched to a known paper.
Research works printed but not linked: 2
- [21]Albers D Hripcsak G Estimation of time-delayed mutual information and bias for irregularly and sparsely sampled time-series arXiv 2011 PMC3332129 22536009
- [28]Cismondi F Fialho AS Vieira SM Reti SR Sousa JMC Finkelstein SN Artificial Intelligence in Medicine Artificial intelligence in medicine 2013 58 1 63 72 23428358
Cited by
Sorted by citations
Opportunities and obstacles for deep learning in biology and medicine
Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310 · see citing works
Sellgren, C. M., Gracias, J., Watmuff, B., Biag, J. D., Thanos, J. M., Whittredge, P. B., Fu, T., Worringer, K. A., Brown, H. E., Wang, J. P., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-018-0334-7 · Cited by 784 · see citing works
Agniel, D., Kohane, I. S., & Weber, G. M. (2018) · BMJ · doi:10.1136/bmj.k1479 · Cited by 391 · see citing works
From promise to practice: towards the realisation of AI-informed mental health care
Koutsouleris, N., Hauser, T. U., Skvortsova, V., & De Choudhury, M. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00153-4 · Cited by 251 · see citing works
Automated methods for the summarization of electronic health records
Pivovarov, R., & Elhadad, N. (2015) · J. Am. Medical Informatics Assoc. · doi:10.1093/jamia/ocv032 · Cited by 238 · see citing works
Self-citationPredicting Missing Values in Medical Data Via XGBoost Regression
Zhang, X., Yan, C., Gao, C., Malin, B., & Chen, Y. (2020) · Journal of Healthcare Informatics Research · doi:10.1007/s41666-020-00077-1 · Cited by 185 · see citing works
Risk Prediction on Electronic Health Records with Prior Medical Knowledge
Ma, F., Gao, J., Suo, Q., You, Q., Zhou, J. L., & Zhang, A. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220020 · Cited by 137 · see citing works
Dennis, J. K., Sealock, J., Straub, P., Lee, Y. A., Hucks, D., Actkins, K. V., Faucon, A. B., Feng, Y. A., Ge, T., Goleva, S. B., et al. (2021) · Genome Medicine · doi:10.1186/s13073-020-00820-8 · Cited by 129 · see citing works
Using Clinical Notes and Natural Language Processing for Automated HIV Risk Assessment
Feller, D. J., Zucker, J., Yin, M. T., Gordon, P., & Elhadad, N. (2018) · JAIDS Journal of Acquired Immune Deficiency Syndromes · doi:10.1097/qai.0000000000001580 · Cited by 130 · see citing works
Self-citationOutcomes of respiratory viral-bacterial co-infection in adult hospitalized patients
Liu, Y., Ling, L., Wong, S. H., Wang, M. H., Fitzgerald, J. R., Zou, X., Fang, S., Liu, X., Wang, X., Hu, W., et al. (2021) · EClinicalMedicine · doi:10.1016/j.eclinm.2021.100955 · Cited by 131 · see citing works
Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
Shashikumar, S. P., Wardi, G., Malhotra, A., & Nemati, S. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00504-6 · Cited by 130 · see citing works
Learning Probabilistic Phenotypes from Heterogeneous EHR Data
Pivovarov, R., Perotte, A. J., Grave, E., Angiolillo, J., Wiggins, C., & Elhadad, N. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.10.001 · Cited by 140 · see citing works
Self-citationSylman, J. L., Mitrugno, A., Atallah, M. B., Tormoen, G. W., Shatzel, J. J., Yunga, S. T., Wagner, T. H., Leppert, J. T., Mallick, P., & McCarty, O. J. T. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00078 · Cited by 121 · see citing works
Readmission prediction via deep contextual embedding of clinical concepts
Xiao, C., Ma, T., Dieng, A. B., Blei, D. M., & Wang, F. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195024 · Cited by 118 · see citing works
Development and validation of early warning score system: A systematic literature review
Fu, L.-H., Schwartz, J., Moy, A., Knaplund, C., Kang, M.-J., Schnock, K. O., Garcia, J. P., Jia, H., Dykes, P. C., Cato, K., et al. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103410 · Cited by 102 · see citing works
Self-citationPhelan, M., Bhavsar, N. A., & Goldstein, B. A. (2017) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.5334/egems.243 · Cited by 95 · see citing works
Big data are coming to psychiatry: a general introduction
Monteith, S., Glenn, T., Geddes, J. R., & Bauer, M. (2015) · International Journal of Bipolar Disorders · doi:10.1186/s40345-015-0038-9 · Cited by 93 · see citing works
Big Healthcare Data Analytics: Challenges and Applications
Lee, C., Luo, Z., Ngiam, K. Y., Zhang, M., Zheng, K., Chen, G., Ooi, B. C., & Yip, W. L. J. (2017) · Scalable computing and communications · doi:10.1007/978-3-319-58280-1_2 · Cited by 83 · see citing works
Parameterizing time in electronic health record studies
Hripcsak, G. M., Albers, D. J., & Perotte, A. (2015) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocu051 · Cited by 75 · see citing works
Self-citationHigh-fidelity phenotyping: richness and freedom from bias
Hripcsak, G. M., & Albers, D. J. (2017) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx110 · Cited by 74 · see citing works
Self-citationSurvival analysis with electronic health record data: Experiments with chronic kidney disease
Hagar, Y., Albers, D. J., Perotte, R., Chase, H. S., Dukić, V., & Elhadad, N. (2014) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11236 · Cited by 58 · see citing works
Self-citationRossetti, S. C., Dykes, P. C., Knaplund, C., Cho, S., Withall, J., Lowenthal, G., Albers, D., Lee, R. Y., Jia, H., Bakken, S., et al. (2025) · Nature Medicine · doi:10.1038/s41591-025-03609-7 · Cited by 54 · see citing works
“…level of concern about a patient’s deterioration risk, and (2) the use of nursing assessment and observational data that may reflect patient condition changes earlier than other physiological values 10,43,44 and that are information-rich 13,21 but often not well understood outside of nursing.”
Self-citationAn unsupervised learning method to identify reference intervals from a clinical database
Poole, S. F., Schroeder, L. F., & Shah, N. H. (2015) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2015.12.010 · Cited by 52 · see citing works
Albers, D. J., Elhadad, N., Tabak, E. G., Perotte, A., & Hripcsak, G. M. (2014) · PLoS ONE · doi:10.1371/journal.pone.0096443 · Cited by 44 · see citing works
Self-citationRossetti, S. C., Knaplund, C., Albers, D., Dykes, P. C., Kang, M. J., Korach, T. Z., Zhou, L., Schnock, K., Garcia, J., Schwartz, J., et al. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab006 · Cited by 42 · see citing works
Self-citationTemporal trends of hemoglobin A1c testing
Pivovarov, R., Albers, D. J., Hripcsak, G., Sepulveda, J. L., & Elhadad, N. (2014) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-002592 · Cited by 41 · see citing works
Self-citationXu, D., Hu, P. J., Huang, T., Fang, X., & Hsu, C. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103576 · Cited by 42 · see citing works
Resolving the Bias in Electronic Medical Records
Zheng, K., Gao, J., Ngiam, K. Y., Ooi, B. C., & Yip, W. L. J. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098149 · Cited by 37 · see citing works
Healthcare Analysis in Smart Big Data Analytics: Reviews, Challenges and Recommendations
Ebada, A. I., Shehab, A., & Elhenawy, I. (2018) · Lecture notes in intelligent transportation and infrastructure · doi:10.1007/978-3-030-01560-2_2 · Cited by 38 · see citing works
Yang, H. S., Rhoads, D. D., Sepulveda, J. L., Zang, C., Chadburn, A., & Wang, F. (2022) · Archives of Pathology & Laboratory Medicine · doi:10.5858/arpa.2021-0635-ra · Cited by 35 · see citing works
Self-citationZhou, Y., Shi, J., Stein, R., Liu, X., Baldassano, R. N., Forrest, C. B., Chen, Y., & Huang, J. (2023) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad066 · Cited by 33 · see citing works
Variational Bayes survival analysis for unemployment modelling
Boškoski, P., Perne, M., Rameša, M., & Boshkoska, B. M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107335 · Cited by 33 · see citing works
Bettencourt‐Silva, J. H., Clark, J., Cooper, C. S., Mills, R., Rayward-Smith, V. J., & de la Iglesia, B. (2015) · JMIR Medical Informatics · doi:10.2196/medinform.4221 · Cited by 33 · see citing works
Ethical layering in AI-driven polygenic risk scores—New complexities, new challenges
Fritzsche, M.-C., Akyüz, K., Abadía, M. C., McLennan, S., Marttinen, P., Mayrhofer, M. T., & Buyx, A. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1098439 · Cited by 35 · see citing works
Evaluating Large Language Models in extracting cognitive exam dates and scores
Zhang, H., Jethani, N., Jones, S., Genes, N. G., Major, V. J., Jaffe, I. S., Cardillo, A. B., Heilenbach, N., Ali, N. F., Bonanni, L. J., et al. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000685 · Cited by 32 · see citing works
Kaur, N., Bhattacharya, S., & Butte, A. J. (2021) · Nature Reviews Nephrology · doi:10.1038/s41581-021-00439-x · Cited by 33 · see citing works
Optimizing Autoencoders for Learning Deep Representations from Health Data
Zhou, C., Jia, Y., & Motani, M. (2019) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2018.2856820 · Cited by 29 · see citing works
Hama, T., Alsaleh, M. M., Allery, F., Choi, J. W., Tomlinson, C., Wu, H., Lai, A. G., Pontikos, N., & Thygesen, J. H. (2025) · Journal of Medical Internet Research · doi:10.2196/57358 · Cited by 32 · see citing works
Dauvin, A., Donado, C., Bächtiger, P., Huang, K.-C., Sauer, C. M., Ramazzotti, D., Bonvini, M., Celi, L. A., & Douglas, M. J. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0192-z · Cited by 29 · see citing works
Phenomics and Robust Multiomics Data for Cardiovascular Disease Subtyping
Maiorino, E., & Loscalzo, J. (2023) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.122.318892 · Cited by 31 · see citing works
Kohn, J. N., Kościółek, T., Marotz, C. A., Aleti, G., Guay-Ross, R. N., Hong, S.-H., Hansen, S., Swafford, A. D.-E., Knight, R. D., & Hong, S. (2020) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2020.02.004 · Cited by 23 · see citing works
Spatio-temporal autoencoder for feature learning in patient data with missing observations
Jia, Y., Zhou, C., & Motani, M. (2017) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2017.8217773 · Cited by 25 · see citing works
Albers, D. J., Elhadad, N., Claassen, J., Perotte, R., Goldstein, A., & Hripcsak, G. M. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.01.004 · Cited by 25 · see citing works
Self-citationPredictability Bounds of Electronic Health Records
Dahlem, D., Maniloff, D., & Ratti, C. (2015) · Scientific Reports · doi:10.1038/srep11865 · Cited by 26 · see citing works
Learning Optimal Individualized Treatment Rules from Electronic Health Record Data
Wang, Y., Wu, P., Liu, Y., Weng, C., & Zeng, D. (2016) · IEEE International Conference on Healthcare Informatics (ICHI) · doi:10.1109/ichi.2016.13 · Cited by 23 · see citing works
Clinical data quality: a data life cycle perspective
Weng, C. (2019) · Biostatistics & Epidemiology · doi:10.1080/24709360.2019.1572344 · Cited by 21 · see citing works
Yield and bias in defining a cohort study baseline from electronic health record data
Vassy, J. L., Ho, Y.-L. A., Honerlaw, J. P., Cho, K., Gaziano, J. M., Wilson, P. W. F., & GAGNON, D. R. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.12.017 · Cited by 22 · see citing works
Methodological variations in lagged regression for detecting physiologic drug effects in EHR data
Levine, M. E., Albers, D. J., & Hripcsak, G. M. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.08.014 · Cited by 19 · see citing works
Self-citationA simple modeling framework for prediction in the human glucose–insulin system
Sirlanci, M., Levine, M. E., Wang, C. C. L., Albers, D. J., & Stuart, A. M. (2023) · Chaos An Interdisciplinary Journal of Nonlinear Science · doi:10.1063/5.0146808 · Cited by 19 · see citing works
Self-citation(2019) · PubMed Central · Cited by 19 · see citing works
Self-citationOn Missingness Features in Machine Learning Models for Critical Care: Observational Study
Singh, J., Sato, M., & Ohkuma, T. (2021) · JMIR Medical Informatics · doi:10.2196/25022 · Cited by 17 · see citing works
Separating disease and health for indirect reference intervals
Sikaris, K. A. (2021) · Journal of Laboratory Medicine · doi:10.1515/labmed-2020-0157 · Cited by 17 · see citing works
Sha, Y., Venugopalan, J., & Wang, M. D. (2016) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2975167.2975202 · Cited by 16 · see citing works
Luo, G. (2019) · Global Transitions · doi:10.1016/j.glt.2018.11.001 · Cited by 14 · see citing works
Fu, L.-H., Knaplund, C., Cato, K., Perotte, A., Kang, M.-J., Dykes, P. C., Albers, D., & Collins Rossetti, S. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab111 · Cited by 14 · see citing works
Self-citationAlbers, D. J., Levine, M. E., Mamykina, L., & Hripcsak, G. M. (2019) · Mathematical Biosciences · doi:10.1016/j.mbs.2019.108242 · Cited by 13 · see citing works
Self-citationLearning Deep Representations from Heterogeneous Patient Data for Predictive Diagnosis
Zhou, C., Jia, Y., Motani, M., & Chew, J. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3107433 · Cited by 12 · see citing works
Peer, K., Adams, W. G., Legler, A., Sandel, M. T., Levy, J. I., Boynton‐Jarrett, R., Kim, C., Leibler, J. H., & Fabian, M. P. (2020) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.11.045 · Cited by 12 · see citing works
Progress in Biomedical Knowledge Discovery: A 25-year Retrospective
Sacchi, L., & Holmes, J. H. (2016) · Yearbook of Medical Informatics · doi:10.15265/iys-2016-s033 · Cited by 11 · see citing works
Lamer, A., Fruchart, M., Paris, N., Popoff, B., Payen, A., Balcaen, T., Gacquer, W., Bouzillé, G., Marc Cuggia, A., Doutreligne, M., et al. (2022) · JMIR Medical Informatics · doi:10.2196/38936 · Cited by 10 · see citing works
Shi, J., Hubbard, A., Fong, N., & Pirracchio, R. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-03058-9 · Cited by 10 · see citing works
A Blockchain-Assisted Electronic Medical Records by Using Proxy Reencryption and Multisignature
Liu, X., Yan, J. H., Shan, S.-Q., & Wu, R. (2022) · Security and Communication Networks · doi:10.1155/2022/6737942 · Cited by 10 · see citing works
Hyohdoh, Y., Hatakeyama, Y., & Okuhara, Y. (2021) · Informatics in Medicine Unlocked · doi:10.1016/j.imu.2021.100512 · Cited by 9 · see citing works
Wang, Y., Stroh, J. N., Hripcsak, G. M., Wang, C. C. L., Bennett, T. D., Wrobel, J., Der-Nigoghossian, C. A., Mueller, S. W., Claassen, J., & Albers, D. J. (2023) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2023.104547 · Cited by 8 · see citing works
Self-citationThaweethai, T., Arterburn, D. E., Coleman, K. J., & Haneuse, S. (2021) · The Annals of Applied Statistics · doi:10.1214/20-aoas1386 · Cited by 8 · see citing works
How Deep is Your Guess? A Fresh Perspective on Deep Learning for Medical Time-Series Imputation
Qian, L., Ellis, H. L., Wang, T., Wang, J., Mitra, R., Dobson, R., & Ibrahim, Z. M. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2025.3568778 · Cited by 6 · see citing works
Hospital readmission prediction based on long-term and short-term information fusion
Chen, Z., Lai, C., & Ren, J. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106690 · Cited by 6 · see citing works
When to re-order laboratory tests? Learning laboratory test shelf-life
Levy-Fix, G., Gorman, S. L., Sepulveda, J. L., & Elhadad, N. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.07.019 · Cited by 6 · see citing works
Self-citationWu, P., Xu, T., & Wang, Y. (2019) · IEEE International Conference on Data Science and Advanced Analytics (DSAA) · doi:10.1109/dsaa.2019.00054 · Cited by 5 · see citing works
ImputeRNN: Imputing Missing Values in Electronic Medical Records
Ouyang, J., Zhang, Y., Cai, X., Zhang, Y., & Yuan, X. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-73200-4_28 · Cited by 5 · see citing works
Multisite Pragmatic Cluster-Randomized Controlled Trial of the CONCERN Early Warning SystemPreprint
Rossetti, S. C., Dykes, P. C., Knaplund, C., Cho, S., Withall, J. B., Lowenthal, G., Albers, D. J., Lee, R. Y., Jia, H., Henry, S. B., et al. (2024) · medRxiv · doi:10.1101/2024.06.04.24308436 · Cited by 5 · see citing works
Self-citationTempini, N., & Teira, D. (2020) · Springer eBooks · doi:10.1007/978-3-030-37177-7_11 · Cited by 4 · see citing works
Rossetti, S. C., Yen, P., Dykes, P. C., Schnock, K. O., & Cato, K. (2019) · Health informatics · doi:10.1007/978-3-030-16916-9_8 · Cited by 4 · see citing works
A Simple Modeling Framework For Prediction In The Human Glucose-Insulin SystemPreprint
Sirlanci, M., Levine, M. E., Wang, C. C. L., Albers, D. J., & Stuart, A. M. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.14193 · Cited by 4 · see citing works
Self-citationDiagnostic suspicion bias and machine learning: Breaking the awareness deadlock for sepsis detection
Prasad, V., Aydemir, B., Kehoe, I. E., Kotturesh, C., O’Connell, A., Biebelberg, B., Wang, Y., Lynch, J. C., Pepino, J. A., Filbin, M. R., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000365 · Cited by 3 · see citing works
Srinivasaiah, B. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/sr24506012313 · Cited by 3 · see citing works
Yap, A. J. Y., Teo, D. C. H., Ang, P. S., Yap, E. S., Tan, S. H., Loke, C. W. P., & Dorajoo, S. R. (2024) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.5875 · Cited by 3 · see citing works
Inferring EHR Utilization Workflows through Audit Logs.
Zhang, X., Zhao, Y., Yan, C., Derr, T., & Chen, Y. (2022) · PubMed Central · Cited by 3 · see citing works
Doku, R., Osafo, N. Y. A., Kwagyan, J., & Southerland, W. M. (2026) · The Journal of Applied Laboratory Medicine · doi:10.1093/jalm/jfag098 · Cited by 2 · see citing works
Fairness Analysis in AI Algorithms in Healthcare: A Study on Post-Processing Approaches
Martini, V. G., & Berton, L. (2024) · doi:10.5753/eniac.2024.244467 · Cited by 1 · see citing works
Artefacts in continuous neuromonitoring
Chen, X., Bögli, S. Y., Olakorede, I., Xu, W., Beqiri, E., Tang, C., Occhipinti, L. G., Gao, S., & Smielewski, P. (2026) · Nature Reviews Bioengineering · doi:10.1038/s44222-025-00378-3 · Cited by 2 · see citing works
Anantharama, N., Buntine, W., & Nunn, A. K. (2021) · ACI Open · doi:10.1055/s-0041-1735975 · Cited by 1 · see citing works
Precision Medicine and a Learning Health System for Mental Health
Ranallo, P. A., & Tenenbaum, J. D. (2021) · Health informatics · doi:10.1007/978-3-030-70558-9_1 · Cited by 1 · see citing works
Davis, L. K., Sealock, J., Lee, Y. A., Moscati, A., Venkatesh, S., Voloudakis, G., Straub, P., Singh, K., Feng, Y. A., Ge, T., et al. (2020) · Research Square · doi:10.21203/rs.3.rs-126004/v1
Lamer, A., Fruchart, M., Paris, N., Popoff, B., Payen, A., Balcaen, T., Gacquer, W., Bouzillé, G., Marc Cuggia, A., Doutreligne, M., et al. (2022) · doi:10.2196/preprints.38936
Partition-Based Technique to Enhance Missing Data Prediction
Jozan, M. M. B., & Tabesh, H. (2024) · Indonesian Journal of Electrical Engineering and Informatics (IJEEI) · doi:10.52549/ijeei.v12i1.4440
LifeGuardAI-Artificial Intelligence for Predicting Mortality Due to Sepsis
Joshi, K., Janardhan, K., Manohar, M., Harshavardhan, L., & Bindu, M. D. H. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-18039
Thate, J., Rossetti, S. C., Yen, P., Dykes, P. C., Schnock, K. O., & Cato, K. (2025) · Cognitive informatics in biomedicine and healthcare · doi:10.1007/978-3-031-82971-0_10
Luo, J., Zhang, A., Gao, W., Bu, X., Li, R., Ye, Z., Zhang, X., Ren, L., Yin, J., Guan, Z., et al. (2025) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.70079
Personalized clinical reference intervals for routine precision medical carePreprint
Zhang, C., Chen, Y.-L., Jamilov, A., Liu, E., Shree, S., Lam, B. D., & Foy, B. H. (2026) · medRxiv · doi:10.64898/2026.05.28.26354363
- Authors
- Pivovarov, R., Albers, D. J., Sepulveda, J. L., & Elhadad, N.
- Affiliations
- Columbia University (US)
- Venue
- Journal of Biomedical Informatics
- Publisher
- Elsevier BV
- Published
- 2014-10
- Volume
- 51
- Pages
- 24-34
- ISSN
- 1532-0464 (ISSN-L)
- PMC
- PMC4194228
- OpenAlex
- W2152726215
- Cited by
- 91
- References
- 39
- Citations by year
- 2026: 3 · 2025: 6 · 2024: 7 · 2023: 6 · 2022: 6 · 2021: 15 · 2020: 8 · 2019: 9 · 2018: 11 · 2017: 7 · 2016: 3 · 2015: 7 · 2014: 3
- FWCI
- 6.65
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Journal of Biomedical Informatics (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Science Foundation (1344668, 1144854)
- U.S. National Library of Medicine (R01:LM06910, LM06910, R01LM006910, T15LM007079, R01LM010027)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Physiology › Clinical Laboratory Practices and Quality Control
- Keywords
- electronic health records, measurement frequency, temporal patterns, confounding, bias mitigation, lipase, acute pancreatitis, creatinine, measurement context, disease association studies
- MeSH terms
- Clinical Laboratory Information Systems, Data Interpretation, Statistical, New York, Pattern Recognition, Automated, Confounding Factors, Epidemiologic, Artifacts, 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 ›
Are you an author, or do you see a problem with this page? Request a review
