Assessing Socioeconomic Bias in Machine Learning Algorithms in Health Care: A Case Study of the HOUSES Index
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Abstract
Objective: Artificial intelligence (AI) models may propagate harmful biases in performance and hence negatively affect the underserved. We aimed to assess the degree to which data quality of electronic health records (EHRs) affected by inequities related to low socioeconomic status (SES), results in differential performance of AI models across SES. Materials and Methods: This study utilized existing machine learning models for predicting asthma exacerbation in children with asthma. We compared balanced error rate (BER) against different SES levels measured by HOUsing-based SocioEconomic Status measure (HOUSES) index. As a possible mechanism for differential performance, we also compared incompleteness of EHR information relevant to asthma care by SES. Results: Asthmatic children with lower SES had larger BER than those with higher SES (eg, ratio = 1.35 for HOUSES Q1 vs Q2–Q4) and had a higher proportion of missing information relevant to asthma care (eg, 41% vs 24% for missing asthma severity and 12% vs 9.8% for undiagnosed asthma despite meeting asthma criteria). Discussion: Our study suggests that lower SES is associated with worse predictive model performance. It also highlights the potential role of incomplete EHR data in this differential performance and suggests a way to mitigate this bias. Conclusion: The HOUSES index allows AI researchers to assess bias in predictive model performance by SES. Although our case study was based on a small sample size and a single-site study, the study results highlight a potential strategy for identifying bias by using an innovative SES measure.
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EHR data gaps drive algorithmic bias.
“Balanced error rate ratio 1.35 for lowest versus other SES quartiles; 41% of lower‐SES children had missing EHR records versus 24% of higher‐SES peers; EHR data gaps drive algorithmic bias”
From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [23]
The Discussion reports more unavailable and inaccurate EHR data among lower-SES children with asthma and proposes that differential EHR completeness might contribute to algorithmic bias. It does not establish that EHR data gaps drive the bias; that causal wording is stronger than the paper’s stated conclusion. (LLM, conf=0.86)
Experimental — accuracy not yet measured41% of lower-SES children had missing EHR records versus 24% of higher-SES peers.
“Balanced error rate ratio 1.35 for lowest versus other SES quartiles; 41% of lower‐SES children had missing EHR records versus 24% of higher‐SES peers; EHR data gaps drive algorithmic bias”
From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [23]
The Results section says lower-SES children had more unavailable EHR data for asthma-related variables than higher-SES children. However, the passages from our copy do not give the claimed 41% and 24% figures, so the numerical comparison is not verified. (LLM, conf=0.85)
Experimental — accuracy not yet measuredIn Juhn et al. [23], 41% of lower-SES children had missing relevant records versus 24% in higher-SES children.
“(41% missing relevant records vs. 24% in higher‐SES children)”
From Artificial Intelligence and Machine Learning for Identifying Social Determinants of Health in Low‐Income Populations Within United States Health Systems: A Scoping Review (2026) · cites this paper as [23]
The Results section reports that lower-SES children had more unavailable asthma-related data than higher-SES children, supporting the direction of the comparison. The passages available from our copy do not establish the specific 41% versus 24% figures (searched: both percentages and Table 6). No page number was provided for these passages. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPredictive ML modeling was the most common approach, used in 10 studies.
“Predictive ML modeling was the most common approach, used in 10 studies [21, 22, 23, 25, 26, 27, 30, 31, 32, 34].”
The balanced error rate ratio was 1.35 for the lowest versus other SES quartiles.
“Balanced error rate ratio 1.35 for lowest versus other SES quartiles; 41% of lower‐SES children had missing EHR records versus 24% of higher‐SES peers; EHR data gaps drive algorithmic bias”
Juhn et al. [23] reported a balanced error rate ratio of 1.35 for the lowest socioeconomic status quartile compared with higher-SES peers.
“with a balanced error rate ratio of 1.35 compared with higher‐SES peers”
Juhn et al. [23] attributed worse asthma prediction performance for lower-SES children in part to greater EHR data incompleteness in lower-SES patients.
“attributable in part to greater EHR data incompleteness in lower‐SES patients”
Juhn et al. [23] found that asthma prediction models performed significantly worse for children in the lowest socioeconomic status quartile.
“Juhn et al. [23] found that asthma prediction models performed significantly worse for children in the lowest socioeconomic status quartile”
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Cites
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137 · see citing works
Making Neighborhood-Disadvantage Metrics Accessible — The Neighborhood Atlas
Kind, A. J. H., & Buckingham, W. R. (2018) · New England Journal of Medicine · doi:10.1056/nejmp1802313 · Cited by 3,310 · see citing works
Fair prediction with disparate impact: a study of bias in recidivism prediction instruments
Chouldechova, A. (2017) · Big Data · doi:10.1089/big.2016.0047 · Cited by 2,159 · see citing works
Socioeconomic Disparities In Health: Pathways And Policies
Adler, N. E., & Newman, K. (2002) · Health Affairs · doi:10.1377/hlthaff.21.2.60 · Cited by 2,102 · see citing works
Feudtner, C., Feinstein, J. A., Zhong, W., Hall, M., & Dai, D. (2014) · BMC Pediatrics · doi:10.1186/1471-2431-14-199 · Cited by 1,770 · see citing works
Ensuring fairness in machine learning to advance health equity.
Rajkomar, A., Hardt, M., Howell, M. D., Corrado, G., & Chin, M. H. (2018) · Annals of Internal Medicine · doi:10.7326/m18-1990 · Cited by 1,341 · see citing works
Benjamens, S., Dhunnoo, P., & Meskó, B. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00324-0 · Cited by 1,215 · see citing works
Primary Care Physicians Who Treat Blacks and Whites
Bach, P. B., Pham, H. H., Schrag, D., Tate, R. C., & Hargraves, J. L. (2004) · New England Journal of Medicine · doi:10.1056/nejmsa040609 · Cited by 987 · see citing works
AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias
Bellamy, R. K. E., Dey, K., Hind, M., Hoffman, S. C., Houde, S., Kannan, K., Lohia, P., Martino, J., Mehta, S., Mojsilović, A., et al. (2019) · IBM Journal of Research and Development · doi:10.1147/jrd.2019.2942287 · Cited by 884 · see citing works
The measurement of SES in health research: current practice and steps toward a new approach
Oakes, J. M., & Rossi, P. H. (2003) · Social Science & Medicine · doi:10.1016/s0277-9536(02)00073-4 · Cited by 740 · see citing works
Shippee, N. D., Shah, N. D., May, C. R., Mair, F. S., & Montori, V. M. (2012) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2012.05.005 · Cited by 728 · see citing works
Good intentions are not enough: how informatics interventions can worsen inequality
Veinot, T. C., Mitchell, H., & Ancker, J. S. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy052 · Cited by 704 · see citing works
Social determinants of health and survival in humans and other animals
Snyder-Mackler, N., Burger, J. R., Gaydosh, L., Belsky, D. W., Noppert, G. A., Campos, F. A., Bartolomucci, A., Yang, Y. C., Aiello, A. E., O’Rand, A., et al. (2020) · Science · doi:10.1126/science.aax9553 · Cited by 679 · see citing works
Sauver, J. L. S., Grossardt, B. R., Leibson, C. L., Yawn, B. P., Melton, L. J., & Rocca, W. A. (2012) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2011.11.009 · Cited by 652 · see citing works
Data Resource Profile: The Rochester Epidemiology Project (REP) medical records-linkage system
Sauver, J. L. S., Grossardt, B. R., Yawn, B. P., Melton, L. J., Pankratz, J. J., Brue, S. M., & Rocca, W. A. (2012) · International Journal of Epidemiology · doi:10.1093/ije/dys195 · Cited by 613 · see citing works
Warnecke, R. B., Oh, A., Breen, N., Gehlert, S. J., Paskett, E. D., Tucker, K. L., Lurie, N., Rebbeck, T. R., Goodwin, J., Flack, J. M., et al. (2008) · American Journal of Public Health · doi:10.2105/ajph.2006.102525 · Cited by 554 · see citing works
Changing Trends in Asthma Prevalence Among Children
Akinbami, L. J., Simon, A. E., & Rossen, L. M. (2015) · PEDIATRICS · doi:10.1542/peds.2015-2354 · Cited by 439 · see citing works
Yao, X., Rushlow, D. R., Inselman, J. W., McCoy, R. G., Thacher, T. D., Behnken, E. M., Bernard, M. E., Rosas, S. L., Akfaly, A., Misra, A., et al. (2021) · Nature Medicine · doi:10.1038/s41591-021-01335-4 · Cited by 424 · see citing works
Lantz, P. M., Lynch, J. W., House, J. S., Lepkowski, J. M., Mero, R. P., Musick, M. A., & Williams, D. R. (2001) · Social Science & Medicine · doi:10.1016/s0277-9536(00)00319-1 · Cited by 413 · see citing works
Controlling Disease and Creating Disparities: A Fundamental Cause Perspective
Phelan, J. C., & Link, B. G. (2005) · The Journals of Gerontology Series B: Psychological Sciences and Social Sciences · doi:10.1093/geronb/60.special_issue_2.s27 · Cited by 409 · see citing works
Can AI Help Reduce Disparities in General Medical and Mental Health Care?
(2019) · AMA Journal of Ethics · doi:10.1001/amajethics.2019.167 · Cited by 401 · see citing works
Knighton, A. J., Savitz, L. A., Belnap, T., Stephenson, B., & VanDerslice, J. A. (2016) · eGEMs (Generating Evidence & Methods to improve patient outcomes) · doi:10.13063/2327-9214.1238 · Cited by 322 · see citing works
Prevalence of asthma‐like symptoms in young children
Bisgaard, H., & Szefler, S. J. (2007) · Pediatric Pulmonology · doi:10.1002/ppul.20644 · Cited by 304 · see citing works
An algorithmic approach to reducing unexplained pain disparities in underserved populations
Pierson, E., Cutler, D. M., Leskovec, J., Mullainathan, S., & Obermeyer, Z. (2021) · Nature Medicine · doi:10.1038/s41591-020-01192-7 · Cited by 268 · see citing works
Influence of Patients' Socioeconomic Status on Clinical Management Decisions: A Qualitative Study
Bernheim, S. M., Ross, J. S., Krumholz, H. M., & Bradley, E. H. (2008) · The Annals of Family Medicine · doi:10.1370/afm.749 · Cited by 212 · see citing works
Vital Directions for Health and Health Care
Dzau, V. J., McClellan, M., McGinnis, J. M., Burke, S. P., Coye, M. J., Díaz, Á., Daschle, T. A., Frist, W. H., Gaines, M. E., Hamburg, M. A., et al. (2017) · JAMA · doi:10.1001/jama.2017.1964 · Cited by 205 · see citing works
Development and Initial Testing of a New Socioeconomic Status Measure Based on Housing Data
Juhn, Y. J., Beebe, T. J., Finnie, D. M., Sloan, J. A., Wheeler, P. H., Yawn, B. P., & Williams, A. R. (2011) · Journal of Urban Health · doi:10.1007/s11524-011-9572-7 · Cited by 200 · see citing works
Self-citationCheng, E. R., Hawkins, S. S., Rifas-Shiman, S. L., Gillman, M. W., & Taveras, E. M. (2016) · BMC Public Health · doi:10.1186/s12889-016-3110-1 · Cited by 181 · see citing works
Grembowski, D., Schaefer, J., Johnson, K. E., Fischer, H., Moore, S. L., Tai-Seale, M., Ricciardi, R., Fraser, J. R., Miller, D., & LeRoy, L. (2014) · Medical Care · doi:10.1097/mlr.0000000000000045 · Cited by 179 · see citing works
Flores, G., Snowden-Bridon, C., Torres, S., Perez, R. A., Walter, T., Brotanek, J. M., Lin, H., & Tomany-Korman, S. (2009) · Journal of Asthma · doi:10.1080/02770900802712971 · Cited by 155 · see citing works
Social Determinants of Health in Managed Care Payment Formulas
Ash, A. S., Mick, E. O., Ellis, R. P., Kiefe, C. I., Allison, J. J., & Clark, M. A. (2017) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2017.3317 · Cited by 137 · see citing works
Cumulative Exposure to Neighborhood Context
Clarke, P., Morenoff, J. D., Debbink, M. P., Golberstein, E., Elliott, M. R., & Lantz, P. M. (2013) · Research on Aging · doi:10.1177/0164027512470702 · Cited by 132 · see citing works
Pardo-Crespo, M. R., Narla, N. P., Williams, A. R., Beebe, T. J., Sloan, J. A., Yawn, B. P., Wheeler, P. H., & Juhn, Y. J. (2013) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2012-201742 · Cited by 127 · see citing works
Self-citationHoskins, K. F., Danciu, O. C., Ko, N. Y., & Calip, G. S. (2021) · JAMA Oncology · doi:10.1001/jamaoncol.2020.7320 · Cited by 117 · see citing works
Comparison of Methods to Reduce Bias From Clinical Prediction Models of Postpartum Depression
Park, Y., Hu, J., Singh, M., Sylla, I., Dankwa‐Mullan, I., Koski, E., & Das, A. K. (2021) · JAMA Network Open · doi:10.1001/jamanetworkopen.2021.3909 · Cited by 116 · see citing works
Income is an independent risk factor for worse asthma outcomes
Cardet, J. C., Louisias, M., King, T. S., Castro, M., Codispoti, C. D., Dunn, R. M., Engle, L. L., Giles, B. L., Holguín, F., Lima, J. J., et al. (2017) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2017.04.036 · Cited by 100 · see citing works
Association of Social Determinants With Children’s Hospitals’ Preventable Readmissions Performance
Sills, M. R., Hall, M., Colvin, J. D., Macy, M. L., Cutler, G. J., Bettenhausen, J. L., Morse, R. B., Auger, K. A., Raphael, J. L., Gottlieb, L. M., et al. (2016) · Archives of Pediatrics and Adolescent Medicine · doi:10.1001/jamapediatrics.2015.4440 · Cited by 93 · see citing works
A longitudinal study of the prevalence of asthma in a community population of school-age children
Yawn, B. P., Wollan, P. C., Kurland, M. J., & Scanlon, P. (2002) · The Journal of Pediatrics · doi:10.1067/mpd.2002.123764 · Cited by 92 · see citing works
The Medicalization of Population Health: Who Will Stay Upstream?
Lantz, P. M. (2018) · Milbank Quarterly · doi:10.1111/1468-0009.12363 · Cited by 91 · see citing works
Assessment of the Effect of Adjustment for Patient Characteristics on Hospital Readmission Rates
Roberts, E. T., Zaslavsky, A. M., Barnett, M. L., Landon, B. E., Ding, L., & McWilliams, J. M. (2018) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2018.4481 · Cited by 89 · see citing works
Wi, C.-I., Sohn, S., Rolfes, M. C., Seabright, A., Ryu, E., Voge, G., Bachman, K. A., Park, M. A., Kita, H., Croghan, I. T., et al. (2017) · American Journal of Respiratory and Critical Care Medicine · doi:10.1164/rccm.201610-2006oc · Cited by 88 · see citing works
Self-citationSeol, H. Y., Shrestha, P., Muth, J. F., Wi, C.-I., Sohn, S., Ryu, E., Park, M., Ihrke, K., Moon, S., King, K., et al. (2021) · PLOS ONE · doi:10.1371/journal.pone.0255261 · Cited by 82 · see citing works
Self-citationWu, S., Sohn, S., Ravikumar, K. E., Wagholikar, K. B., Jonnalagadda, S., Liu, H., & Juhn, Y. J. (2013) · Annals of Allergy Asthma & Immunology · doi:10.1016/j.anai.2013.07.022 · Cited by 79 · see citing works
Self-citationFranks, P. J., & Fiscella, K. A. (2002) · Medical Care · doi:10.1097/00005650-200208000-00011 · Cited by 78 · see citing works
Quality Reporting That Addresses Disparities in Health Care
Jha, A. K., & Zaslavsky, A. M. (2014) · JAMA · doi:10.1001/jama.2014.7204 · Cited by 75 · see citing works
Burden of preschool wheeze and progression to asthma in the UK: Population-based cohort 2007 to 2017
Bloom, C. I., Franklin, C., Bush, A., Saglani, S., & Quint, J. K. (2021) · Journal of Allergy and Clinical Immunology · doi:10.1016/j.jaci.2020.12.643 · Cited by 71 · see citing works
Association of Parental Socioeconomic Status and Newborn Telomere Length
Martens, D. S., Janssen, B. G., Bijnens, E. M., Clemente, D. B. P., Vineis, P., Plusquin, M., & Nawrot, T. S. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.4057 · Cited by 67 · see citing works
Ghawi, H., Crowson, C. S., Rand-Weaver, J., Krusemark, E. A., Gabriel, S. E., & Juhn, Y. J. (2015) · BMJ Open · doi:10.1136/bmjopen-2014-006469 · Cited by 66 · see citing works
Self-citationAsthma-related emergency department use: current perspectives
Dexheimer, J. W., Chambers, P., & Johnson, L. H. (2016) · Open Access Emergency Medicine · doi:10.2147/oaem.s69973 · Cited by 65 · see citing works
Natural language processing for asthma ascertainment in different practice settings
Wi, C.-I., Sohn, S., Ali, M., Krusemark, E., Ryu, E., Liu, H., & Juhn, Y. J. (2018) · The Journal of Allergy and Clinical Immunology In Practice · doi:10.1016/j.jaip.2017.04.041 · Cited by 62 · see citing works
Self-citationIndividual housing-based socioeconomic status predicts risk of accidental falls among adults
Ryu, E., Juhn, Y. J., Wheeler, P. H., Hathcock, M. A., Wi, C., Olson, J. E., Cerhan, J. R., & Takahashi, P. Y. (2017) · Annals of Epidemiology · doi:10.1016/j.annepidem.2017.05.019 · Cited by 58 · see citing works
Self-citationTakahashi, P. Y., Ryu, E., Hathcock, M. A., Olson, J. E., Bielinski, S. J., Cerhan, J. R., Rand-Weaver, J., & Juhn, Y. J. (2015) · Journal of Epidemiology & Community Health · doi:10.1136/jech-2015-205925 · Cited by 58 · see citing works
Self-citationBjur, K. A., Wi, C., Ryu, E., Crow, S. S., King, K. S., & Juhn, Y. J. (2019) · Hospital Pediatrics · doi:10.1542/hpeds.2018-0091 · Cited by 57 · see citing works
Self-citationFerryman, K. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa133 · Cited by 55 · see citing works
Boehmer, K. R., Shippee, N. D., Beebe, T. J., & Montori, V. M. (2016) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2016.01.006 · Cited by 52 · see citing works
Chien, A. T., Wroblewski, K., Damberg, C., Williams, T. R., Yanagihara, D., Yakunina, Y., & Casalino, L. (2012) · Journal of general internal medicine · doi:10.1007/s11606-011-1946-8 · Cited by 50 · see citing works
Wi, C., Sauver, J. L. S., Jacobson, D. J., Pendegraft, R. S., Lahr, B. D., Ryu, E., Beebe, T. J., Sloan, J. A., Rand-Weaver, J., Krusemark, E. A., et al. (2016) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2016.02.011 · Cited by 49 · see citing works
Self-citationHarris, M. N., Lundien, M. C., Finnie, D. M., Williams, A. R., Beebe, T. J., Sloan, J. A., Yawn, B. P., & Juhn, Y. J. (2014) · npj Primary Care Respiratory Medicine · doi:10.1038/npjpcrm.2014.18 · Cited by 48 · see citing works
Self-citationConcordance between Individual vs. Area-Level Socioeconomic Measures in an Urban Setting
Narla, N. P., Pardo-Crespo, M. R., Beebe, T. J., Sloan, J. A., Yawn, B. P., Williams, A. R., & Juhn, Y. J. (2015) · Journal of Health Care for the Poor and Underserved · doi:10.1353/hpu.2015.0122 · Cited by 46 · see citing works
Self-citationJohnson, M. D., Urm, S.-H., JUNG, J. A., YUN, H. D., MUNITZ, G. E., Tsigrelis, C., Baddour, L. M., & Juhn, Y. J. (2012) · Epidemiology and Infection · doi:10.1017/s0950268812001252 · Cited by 45 · see citing works
Self-citationA two-county comparison of the HOUSES index on predicting self-rated health
Butterfield, M., Williams, A., Beebe, T. J., Finnie, D. M., Liu, H., Liesinger, J. T., Sloan, J. A., Wheeler, P. H., Yawn, B. P., & Juhn, Y. J. (2010) · Journal of Epidemiology & Community Health · doi:10.1136/jech.2008.084723 · Cited by 45 · see citing works
Self-citationBarwise, A. K., Juhn, Y. J., Wi, C., Novotny, P. J., Jaramillo, C., Gajic, O., & Wilson, M. E. (2018) · American Journal of Hospice and Palliative Medicine® · doi:10.1177/1049909118812431 · Cited by 45 · see citing works
Self-citationStevens, M. A., Beebe, T. J., Wi, C.-I., Taler, S. J., Sauver, J. L. S., & Juhn, Y. J. (2020) · Transplantation · doi:10.1097/tp.0000000000003131 · Cited by 44 · see citing works
Self-citationBjur, K. A., Wi, C., Ryu, E., Derauf, C., Crow, S. S., King, K. S., & Juhn, Y. J. (2019) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2018.06.030 · Cited by 43 · see citing works
Self-citationInvited Commentary: Using Area-based Socioeconomic Measures—Think Conceptually, Act Cautiously
Geronimus, A. T. (2006) · American Journal of Epidemiology · doi:10.1093/aje/kwj314 · Cited by 43 · see citing works
Effect of Multiple Chronic Diseases on Health Care Expenditures in Childhood
Zhong, W., Finnie, D. M., Shah, N. D., Wagie, A. E., Sauver, J. L. S., Jacobson, D. J., & Naessens, J. M. (2014) · Journal of Primary Care & Community Health · doi:10.1177/2150131914540916 · Cited by 41 · see citing works
A Novel Socioeconomic Measure Using Individual Housing Data in Cardiovascular Outcome Research
Bang, D. W., Manemann, S. M., Gerber, Y., Roger, V. L., Lohse, C. M., Rand-Weaver, J., Krusemark, E. A., Yawn, B. P., & Juhn, Y. J. (2014) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph111111597 · Cited by 36 · see citing works
Self-citationHolding Providers Accountable for Health Care Outcomes
Baker, D. W., & Chassin, M. R. (2017) · Annals of Internal Medicine · doi:10.7326/m17-0691 · Cited by 32 · see citing works
Barwise, A. K., Wi, C., Frank, R. D., Milekić, B., Andrijasevic, N. M., VEERABATTINI, N. K., Singh, S., Wilson, M. E., Gajic, O., & Juhn, Y. J. (2020) · Journal of Intensive Care Medicine · doi:10.1177/0885066620931020 · Cited by 26 · see citing works
Self-citationWi, C., Gauger, J., Bachman, M. J., Rand-Weaver, J., Krusemark, E. A., Ryu, E., King, K. S., Katusic, S. K., & Juhn, Y. J. (2016) · Annals of Epidemiology · doi:10.1016/j.annepidem.2016.05.001 · Cited by 25 · see citing works
Self-citationCooper, S., Rahme, E., Tse, S. M., Grad, R. M., Dorais, M., & Li, P. (2022) · BMC Family Practice · doi:10.1186/s12875-021-01605-7 · Cited by 22 · see citing works
Ryu, E., Olson, J. E., Juhn, Y. J., Hathcock, M. A., Wi, C., Cerhan, J. R., Yost, K. J., & Takahashi, P. Y. (2018) · BMJ Open · doi:10.1136/bmjopen-2017-020054 · Cited by 19 · see citing works
Self-citationAngstman, K. B., Wi, C., Williams, M. D., Bohn, B. A., & Garrison, G. M. (2021) · Journal of Affective Disorders · doi:10.1016/j.jad.2021.05.098 · Cited by 19 · see citing works
Self-citationRole of Geographic Risk Factors in COVID-19 Epidemiology: Longitudinal Geospatial Analysis
Juhn, Y. J., Wheeler, P. H., Wi, C., Bublitz, J. T., Ryu, E., Ristagno, E. H., & Patten, C. A. (2021) · Mayo Clinic Proceedings Innovations Quality & Outcomes · doi:10.1016/j.mayocpiqo.2021.06.011 · Cited by 19 · see citing works
Self-citationMacLaughlin, K. L., Jacobson, R. M., Sauver, J. L. S., Jacobson, D. J., Fan, C. L., Wi, C., & Rutten, L. J. F. (2020) · Vaccine · doi:10.1016/j.vaccine.2020.07.026 · Cited by 19 · see citing works
Self-citationRyu, E., Wi, C., Crow, S. S., Armasu, S. M., Wheeler, P. H., Sloan, J. A., Yawn, B. P., Beebe, T. J., Williams, A. R., & Juhn, Y. J. (2016) · BMJ Open · doi:10.1136/bmjopen-2016-011564 · Cited by 19 · see citing works
Self-citationRyan, C. S., Juhn, Y. J., Kaur, H., Wi, C., Ryu, E., King, K. S., & Lachance, D. H. (2019) · Neuro-Oncology Practice · doi:10.1093/nop/npz065 · Cited by 18 · see citing works
Self-citationThe relationship of 25-hydroxyvitamin D concentrations and individual-level socioeconomic status
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Self-citationWi, C., Wheeler, P. H., Kaur, H., Ryu, E., Kim, D., & Juhn, Y. J. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-025521 · Cited by 11 · see citing works
Self-citationBelsky, D. W., & Snyder‐Mackler, N. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx145 · Cited by 9 · see citing works
Aul, A. J., Dudenkov, D. V., Mara, K. C., Juhn, Y. J., Wi, C., Maxson, J. A., & Thacher, T. D. (2020) · Osteoporosis International · doi:10.1007/s00198-020-05436-7 · Cited by 7 · see citing works
Self-citationPatel, A. A., Wheeler, P. H., Wi, C., Derauf, C., Ryu, E., Zahrieh, D., Bjur, K. A., & Juhn, Y. J. (2020) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.34 · Cited by 4 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 3
- Warnecke RB, Oh A, Breen N, et al. Approaching health disparities from a population perspective: the National Institutes of Health Centers for Population Health and Health Disparities. Am J Public Health. 2008;98(9):1608-1615.
- Narayanan A. Translation tutorial: 21 fairness definitions and their politics. Paper presented at: Proc. Conf. Fairness Accountability Transp., New York, USA2018.
- Hammer R, Capili C, Wi C-I, Ryu E, Rand-Weaver J, Juhn YJ. A new socioeconomic status measure for vaccine research in children using individual housing data: a population-based case-control study. BMC Public Health. 2016;16(1):1-9.
Cited by
Sorted by citations
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Lokaj, B., Pugliese, M., Kinkel, K., Lovis, C., & Schmid, J. (2023) · European Radiology · doi:10.1007/s00330-023-10181-6 · Cited by 57 · see citing works
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Templin, T., Fort, S., Padmanabham, P. S., Seshadri, P., Rimal, R., Oliva, J. B., Lich, K. H., Sylvia, S., & Sinnott-Armstrong, N. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01786-w · Cited by 53 · see citing works
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Chary, A. N., Suh, M., Ordoñez, E., Cameron‐Comasco, L., Ahmad, S., Zirulnik, A. W., Hardi, A., Landry, A., Spencer, V., Obi, T., et al. (2024) · Journal of the American Geriatrics Society · doi:10.1111/jgs.19052 · Cited by 35 · see citing works
Branda, F., Ciccozzi, M., & Scarpa, F. (2025) · Journal of Informetrics · doi:10.1016/j.joi.2025.101727 · Cited by 26 · see citing works
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- Authors
- Juhn, Y. J., Ryu, E., Wi, C.-I., King, K. S., Malik, M., Romero-Brufau, S., Weng, C., Sohn, S., Sharp, R. R., & Halamka, J. D.
- Affiliations
- Mayo Clinic (US)
- Department of Internal Medicine, Mayo Clinic , Rochester, Minnesota, USA
- Columbia University (US)
- Corresponding author
- Juhn, Young J
- ORCID
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2022-04-09
- Volume
- 29
- Issue
- 7
- Pages
- 1142-1151
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC9196683
- OpenAlex
- W4223468055
- Cited by
- 83
- References
- 82
- Citations by year
- 2026: 21 · 2025: 36 · 2024: 16 · 2023: 8 · 2022: 2
- FWCI
- 5.10
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- medRxiv (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institute of Health (NIH)-funded (R01 grant (R01 HL126667), R21 grant (R21AG65639), R21 grant (R21AI142702))
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- socioeconomic bias, algorithmic bias, socioeconomic status, electronic health records, data quality, missing data, asthma exacerbation prediction, childhood asthma, health care inequities, prediction model performance
- MeSH terms
- Machine Learning, Artificial Intelligence, Asthma, Child, Delivery of Health Care, Humans, Social Class, Bias
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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