Extracting social determinants of health from electronic health records using natural language processing: a systematic review
Open accessAbstract
Objective: Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. Materials and Methods: A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Results: Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). Conclusion: NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.
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?
Among 82 papers examined in a comprehensive review, only three SDoH factors were commonly addressed: smoking status, substance use, and homelessness.
“A comprehensive review6 revealed that among the 82 papers examined, only three SDoH factors were commonly addressed: smoking status (27), substance use (21), and homelessness (20).”
From Social determinants of health extraction from clinical notes across institutions using large language models (2025) · cites this paper as [6]
The Abstract (Results) gives the cited counts for smoking status (27), substance use (21), and homelessness (20), and the Results (SDoH identification/extraction) refers to 82 publications. But the abstract also lists alcohol use (15) among the most frequently studied categories, so the source does not support saying only three were commonly addressed. (LLM, conf=0.90)
Experimental — accuracy not yet measuredMobilizing SEDH data from the EHR is hindered by heterogeneous provider documentation practices and interoperability issues.
“Mobilizing this data from the electronic health record (EHR) is hindered by heterogeneous provider documentation practices and interoperability issues [1,2,9–22].”
From The relationship between perceptions of electronic health record usability and clinical importance of social and environmental determinants of health on provider documentation (2024) · cites this paper as [1]
The Introduction says that unstructured notes and a lack of standardized data elements and collection practices limit access to SDoH information, supporting the documentation-practices part of the claim. The passages in our copy do not identify interoperability issues as a barrier; a search for interoperability and data exchange found no such statement. Page numbers were not available. (LLM, conf=0.86)
Experimental — accuracy not yet measuredThe most common SDoH targeted in prior efforts include smoking history, substance use, alcohol use, and homelessness.
“The most common SDoH targeted in prior efforts include smoking history, substance use, alcohol use, and homelessness23.”
From Large language models to identify social determinants of health in electronic health records (2024) · cites this paper as [23]
A considerable number (22 out of 82) of NLP approaches for SDoH rely on rule-based methodologies that are highly dependent on specific lexicons.
“Secondly, a considerable number (22 out of 82) of these approaches rely on rule-based methodologies, which are highly dependent on specific lexicons.”
From Social determinants of health extraction from clinical notes across institutions using large language models (2025) · cites this paper as [6]
Variability in screening and documentation practices currently limits the ability to mobilize SEDH data for secondary uses.
“the variability in screening and documentation practices currently limits the ability to mobilize SEDH data for secondary uses”
Systematic screening and documentation of health determinants can directly impact patient care and research efforts.
“Systematic screening and documentation of health determinants can directly impact patient care [1–8] and research efforts [1,5].”
Providers often use non-structured formats to record health determinants information.
“providers often use non-structured formats to record health determinants information [1,14]”
By analyzing EHRs and integrating data on social and environmental factors, researchers have developed models that predict an individual's risk of experiencing health disparities or poor health outcomes.
“By analyzing EHRs and integrating data on social and environmental factors, researchers have developed models that predict an individual’s risk of experiencing health disparities or poor health outcomes [24].”
From Integrating machine learning and artificial intelligence in life-course epidemiology: pathways to innovative public health solutions (2024) · cites this paper as [24]
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
Hutton, B., Catalá-López, F., & Moher, D. (2016) · Medicina Clínica · doi:10.1016/j.medcli.2016.02.025 · Cited by 591 · see citing works
Social determinants of breast cancer risk, stage, and survival
Coughlin, S. S. (2019) · Breast Cancer Research and Treatment · doi:10.1007/s10549-019-05340-7 · Cited by 570 · see citing works
A clinical text classification paradigm using weak supervision and deep representation
Wang, Y., Sohn, S., Liu, S., Shen, F., Wang, L., Atkinson, E. J., Amin, S., & Liu, H. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-018-0723-6 · Cited by 400 · see citing works
Zeng, Q. T., Goryachev, S., Weiss, S., Sordo, M., Murphy, S. N., & Lazarus, R. (2006) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-6-30 · Cited by 360 · see citing works
Assessing the level of healthcare information technology adoption in the United States: a snapshot
Poon, E. G., Jha, A. K., Christino, M., Honour, M. M., Fernandopulle, R., Middleton, B., Newhouse, J., Leape, L., Bates, D. W., Blumenthal, D., et al. (2006) · BMC Medical Informatics and Decision Making · doi:10.1186/1472-6947-6-1 · Cited by 318 · see citing works
The Intersection between Food Insecurity and Diabetes: A Review
Gucciardi, E., Vahabi, M., Norris, N., Monte, J. P. D., & Farnum, C. (2014) · Current Nutrition Reports · doi:10.1007/s13668-014-0104-4 · Cited by 289 · see citing works
Chen, M., Tan, X., & Padman, R. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa143 · Cited by 287 · see citing works
Housing Quality, Housing Instability, and Maternal Mental Health
Suglia, S. F., Duarte, C. S., & Sandel, M. T. (2011) · Journal of Urban Health · doi:10.1007/s11524-011-9587-0 · Cited by 265 · see citing works
Time Spent in Face-to-Face Patient Care and Work Outside the Examination Room
Gottschalk, A. (2005) · The Annals of Family Medicine · doi:10.1370/afm.404 · Cited by 238 · see citing works
Truong, H. P., Luke, A. A., Hammond, G., Wadhera, R. K., Reidhead, M., & Joynt Maddox, K. E. (2020) · Medical Care · doi:10.1097/mlr.0000000000001418 · Cited by 203 · see citing works
Managing Unstructured Big Data in Healthcare System
Kong, H.-J. (2019) · Healthcare Informatics Research · doi:10.4258/hir.2019.25.1.1 · Cited by 186 · see citing works
Hospital Readmission and Social Risk Factors Identified from Physician Notes
Navathe, A. S., Zhong, F., Lei, V. J., Chang, F. Y., Sordo, M., Topaz, M., Navathe, S. B., Rocha, R. A., & Zhou, L. (2018) · Health Services Research · doi:10.1111/1475-6773.12670 · Cited by 161 · see citing works
Patel, R., Wilson, R. P., Jackson, R., Ball, M., Shetty, H., Broadbent, M. T. M., Stewart, R. J., McGuire, P. K., & Bhattacharyya, S. (2016) · BMJ Open · doi:10.1136/bmjopen-2015-009888 · Cited by 147 · see citing works
Rahimian, F., Salimi‐Khorshidi, G., Payberah, A. H., Tran, J., Solares, R. A., Raimondi, F. E. D., Nazarzadeh, M., Canoy, D., & Rahimi, K. (2018) · PLoS Medicine · doi:10.1371/journal.pmed.1002695 · Cited by 140 · see citing works
The Value of Unstructured Electronic Health Record Data in Geriatric Syndrome Case Identification
Kharrazi, H., Anzaldi, L. J., Hernandez, L., Davison, A., Boyd, C. M., Leff, B., Kimura, J., & Weiner, J. P. (2018) · Journal of the American Geriatrics Society · doi:10.1111/jgs.15411 · Cited by 128 · see citing works
Hatef, E., Rouhizadeh, M., Tia, I., Lasser, E., Hill-Briggs, F., Marsteller, J., & Kharrazi, H. (2019) · JMIR Medical Informatics · doi:10.2196/13802 · Cited by 125 · see citing works
Blosnich, J. R., Montgomery, A. E., Dichter, M. E., Gordon, A. J., Kavalieratos, D., Taylor, L., Ketterer, B., & Bossarte, R. M. (2020) · Journal of General Internal Medicine · doi:10.1007/s11606-019-05447-z · Cited by 123 · see citing works
Identifying child abuse through text mining and machine learning
Amrit, C., Paauw, T., Aly, R., & Lavric, M. (2017) · Expert systems with applications · doi:10.1016/j.eswa.2017.06.035 · Cited by 115 · see citing works
Using natural language processing to identify problem usage of prescription opioids
Carrell, D., Cronkite, D., Palmer, R. E., Saunders, K. W., Gross, D. E., Masters, E. T., Hylan, T. R., & Korff, M. V. (2015) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2015.09.002 · Cited by 114 · see citing works
Bejan, C. A., Angiolillo, J., Conway, D., Nash, R., Shirey-Rice, J. K., Lipworth, L., Cronin, R. M., Pulley, J., Kripalani, S., Barkin, S., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocx059 · Cited by 106 · see citing works
Afshar, M., Phillips, A., Karnik, N., Mueller, J., To, D., Gonzalez, R., Price, R., Cooper, R., Joyce, C., & Dligach, D. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy166 · Cited by 94 · see citing works
Detecting Social and Behavioral Determinants of Health with Structured and Free-Text Clinical Data
Feller, D. J., Bear Don't Walk IV, O. J., Zucker, J., Yin, M. T., Gordon, P., & Elhadad, N. (2020) · Applied Clinical Informatics · doi:10.1055/s-0040-1702214 · Cited by 86 · see citing works
Hazlehurst, B. L., Sittig, D. F., Stevens, V. J., SMITH, K., Hollis, J. F., VOGT, T. M., Winickoff, J. P., GLASGOW, R., Palen, T. E., & Rigotti, N. A. (2005) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2005.08.007 · Cited by 83 · see citing works
Automated Prediction of Risk for Problem Opioid Use in a Primary Care Setting
Hylan, T. R., Korff, M. V., Saunders, K. W., Masters, E. T., Palmer, R. E., Carrell, D., Cronkite, D., Mardekian, J., & Gross, D. E. (2015) · Journal of Pain · doi:10.1016/j.jpain.2015.01.011 · Cited by 78 · see citing works
Lybarger, K., Mari Ostendorf, E., & Yetişgen, M. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103631 · Cited by 78 · see citing works
Wu, C., Chang, C., Robson, D., Jackson, R., Chen, S.-J., Hayes, R. D., & Stewart, R. J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0074262 · Cited by 77 · see citing works
Conway, M., Keyhani, S., Christensen, L., South, B. R., Vali, M., Walter, L. C., Mowery, D. L., Abdelrahman, S., & Chapman, W. W. (2019) · Journal of Biomedical Semantics · doi:10.1186/s13326-019-0198-0 · Cited by 74 · see citing works
Social Determinants of Health 201 for Health Care: Plan, Do, Study, Act
Magnan, S. J. (2021) · NAM Perspectives · doi:10.31478/202106c · Cited by 69 · see citing works
Identifying and classifying opioid‐related overdoses: A validation study
Green, C. A., Perrin, N., Hazlehurst, B. L., Janoff, S. L., DeVeaugh‐Geiss, A., Carrell, D., Grijalva, C. G., Liang, C., Enger, C. L., & COPLAN, P. M. (2019) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4772 · Cited by 68 · see citing works
The prevalence of problem opioid use in patients receiving chronic opioid therapy
Palmer, R. E., Carrell, D., Cronkite, D., Saunders, K. W., Gross, D. E., Masters, E. T., Donevan, S. D., Hylan, T. R., & Kroff, M. V. (2015) · Pain · doi:10.1097/j.pain.0000000000000145 · Cited by 62 · see citing works
Bompelli, A., Wang, Y., Wan, R., Singh, E., Zhou, Y., Xu, L., Oniani, D., Kshatriya, B. S. A., Balls-Berry, J. (. E., & Zhang, R. (2021) · Health Data Science · doi:10.34133/2021/9759016 · Cited by 58 · see citing works
(2013) · Europe PMC (PubMed Central) · Cited by 57 · see citing works
Jeddi, F. R., Nabovati, E., Hamidi, R., & Sharif, R. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1038-y · Cited by 57 · see citing works
Afshar, M., Joyce, C., Dligach, D., Sharma, B., Kania, R., Xie, M., Swope, K., Salisbury‐Afshar, E., & Karnik, N. S. (2019) · PLoS ONE · doi:10.1371/journal.pone.0219717 · Cited by 56 · see citing works
Zhu, V. J., Lenert, L. A., Bunnell, B. E., Obeid, J. S., Jefferson, M. S., & Halbert, C. H. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0795-y · Cited by 55 · see citing works
Kim, H. M., Smith, E. G., Ganoczy, D. A., Walters, H., Stano, C. M., Ilgen, M. A., Bohnert, A. M., & Valenstein, M. M. (2012) · The Journal of Clinical Psychiatry · doi:10.4088/jcp.12m07658 · Cited by 54 · see citing works
Stemerman, R., Arguello, J., Brice, J., Krishnamurthy, A., Houston, M., & Kitzmiller, R. (2021) · JAMIA Open · doi:10.1093/jamiaopen/ooaa069 · Cited by 54 · see citing works
Determinants of Smoking and Quitting in HIV-Infected Individuals
Regan, S., Meigs, J. B., Grinspoon, S. K., & Triant, V. A. (2016) · PLoS ONE · doi:10.1371/journal.pone.0153103 · Cited by 51 · see citing works
A study of transportability of an existing smoking status detection module across institutions.
Liu, M., Shah, A., Jiang, M., et al. (2012) · PubMed · Cited by 49 · see citing works
Greenwald, J. L., Cronin, P. R., Carballo, V., Danaei, G., & Choy, G. (2017) · Medical Care · doi:10.1097/mlr.0000000000000651 · Cited by 47 · see citing works
Automated Extraction of Substance Use Information from Clinical Texts.
Wang, Y., Chen, E. S., Pakhomov, S., et al. (2015) · PubMed · Cited by 45 · see citing works
Comparison of Three Information Sources for Smoking Information in Electronic Health Records
Wang, L., Ruan, X., Yang, P. C., et al. (2016) · Cancer Informatics · doi:10.4137/cin.s40604 · Cited by 42 · see citing works
Young‐Wolff, K. C., Klebaner, D., Folck, B. F., et al. (2017) · Preventive Medicine · doi:10.1016/j.ypmed.2017.08.009 · Cited by 41 · see citing works
Machine learning for phenotyping opioid overdose events
Badger, J. C., LaRose, E. R., Mayer, J. G., Bashiri, F. S., Page, D., & Peissig, P. L. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103185 · Cited by 39 · see citing works
Sharma, B., Dligach, D., Swope, K., Salisbury‐Afshar, E., Karnik, N. S., Joyce, C., & Afshar, M. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-1099-y · Cited by 39 · see citing works
Suresh, S., Saladino, R. A., Fromkin, J. B., et al. (2018) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocy025 · Cited by 38 · see citing works
Integration of physical abuse clinical decision support at 2 general emergency departments
Rosenthal, B. W., Skrbin, J., Fromkin, J. B., et al. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz069 · Cited by 37 · see citing works
Hazlehurst, B. L., Green, C. A., Perrin, N., Brandes, J., Carrell, D., Baer, A. B., DeVeaugh‐Geiss, A., & COPLAN, P. M. (2019) · Pharmacoepidemiology and Drug Safety · doi:10.1002/pds.4810 · Cited by 35 · see citing works
Automated identification of drug and food allergies entered using non-standard terminology
Epstein, R. H., Jacques, P. S., Stockin, M., Rothman, B. S., Ehrenfeld, J. M., & Denny, J. C. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001756 · Cited by 34 · see citing works
(2018) · Europe PMC (PubMed Central) · Cited by 34 · see citing works
Goodday, S. M., Kormilitzin, A., Vaci, N., Liu, Q., Cipriani, A., Smith, T., & Nevado-Holgado, A. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2020.103429 · Cited by 34 · see citing works
Bellows, B. K., DuVall, S. L., Kamauu, A. W. C., Supina, D., Babcock, T., & LaFleur, J. (2015) · International Journal of Eating Disorders · doi:10.1002/eat.22427 · Cited by 33 · see citing works
Perron, B. E., Victor, B. G., Bushman, G., Moore, A., Ryan, J. P., Lu, A. J., & Piellusch, E. K. (2019) · Child Abuse & Neglect · doi:10.1016/j.chiabu.2019.104180 · Cited by 32 · see citing works
Extracting Alcohol and Substance Abuse Status from Clinical Notes: The Added Value of Nursing Data
Topaz, M., Murga, L., Bar‐Bachar, O., Cato, K., & Collins, S. (2019) · Studies in health technology and informatics · doi:10.3233/shti190386 · Cited by 32 · see citing works
Bellows, B. K., LaFleur, J., Kamauu, A. W. C., Ginter, T., Forbush, T. B., Stephen, A., Supina, D., Hodgkins, P., & DuVall, S. L. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001859 · Cited by 30 · see citing works
Lindemann, E. A., Chen, E. S., Wang, Y., Skube, S. J., & Melton, G. B. (2017) · PubMed Central · Cited by 30 · see citing works
Predictive modeling of housing instability and homelessness in the Veterans Health Administration
Byrne, T. H., Montgomery, A. E., & Fargo, J. D. (2018) · Health Services Research · doi:10.1111/1475-6773.13050 · Cited by 30 · see citing works
(2018) · Europe PMC (PubMed Central) · Cited by 30 · see citing works
Chen, E. S., Carter, E. W., Sarkar, I. N., Winden, T., & Melton, G. B. (2014) · PubMed Central · Cited by 29 · see citing works
Natural language processing of lifestyle modification documentation
Shoenbill, K. A., Song, Y., Gress, L., Johnson, H. M., Smith, M. A., & Mendonça, E. A. (2019) · Health Informatics Journal · doi:10.1177/1460458218824742 · Cited by 28 · see citing works
Validation of an alcohol misuse classifier in hospitalized patients
To, D., Sharma, B., Karnik, N. S., Joyce, C., Dligach, D., & Afshar, M. (2019) · Alcohol · doi:10.1016/j.alcohol.2019.09.008 · Cited by 27 · see citing works
Evaluating Healthcare Quality Using Natural Language Processing
Baldwin, K. B. (2008) · Journal for Healthcare Quality · doi:10.1111/j.1945-1474.2008.tb01151.x · Cited by 26 · see citing works
Hollister, B. M., RESTREPO, N. A., Farber‐Eger, E. H., Crawford, D. C., Aldrich, M. C., & Non, A. L. (2016) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813207813_0023 · Cited by 26 · see citing works
Automating Assessment of Lifestyle Counseling in Electronic Health Records
Hazlehurst, B. L., Lawrence, J. M., Donahoo, W. T., Sherwood, N. E., Kurtz, S. E., Xu, S., & Steiner, J. F. (2014) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2014.01.001 · Cited by 24 · see citing works
Extracting Concepts Related to Homelessness from the Free Text of VA Electronic Medical Records.
Gundlapalli, A. V., Carter, M. E., Divita, G., Shen, S., Palmer, M. N., South, B. R., Durgahee, B. S. B., Redd, A. M., & Samore, M. H. (2014) · PubMed Central · Cited by 23 · see citing works
Erickson, J., Abbott, K. L., & Susienka, L. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.04.012 · Cited by 23 · see citing works
Building a tobacco user registry by extracting multiple smoking behaviors from clinical notes
Palmer, E. L., Hassanpour, S., Higgins, J. H., Doherty, J. A., & Onega, T. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0863-3 · Cited by 23 · see citing works
Tobacco use status from clinical notes using Natural Language Processing and rule based algorithm
Hegde, H., Shimpi, N. A., Glurich, I., & Acharya, A. (2018) · Technology and Health Care · doi:10.3233/thc-171127 · Cited by 21 · see citing works
Bucher, B. T., Shi, J., Pettit, R. J., Ferraro, J. P., Chapman, W. W., & Gundlapalli, A. V. (2019) · PubMed Central · Cited by 21 · see citing works
Bettencourt‐Silva, J. H., Natalia, M., Sbodio, M. L., et al. (2020) · Studies in health technology and informatics · doi:10.3233/shti200145 · Cited by 21 · see citing works
Detecting Opioid-Related Aberrant Behavior using Natural Language Processing.
Lingeman, J. M., Wang, P., Becker, W. C., et al. (2017) · PubMed · Cited by 20 · see citing works
Dillahunt-Aspillaga, C. J., Finch, D. K., Massengale, J., Kretzmer, T. S., Luther, S. L., & McCart, J. A. (2014) · PLoS ONE · doi:10.1371/journal.pone.0115873 · Cited by 18 · see citing works
Wang, Y., Chen, E. S., Pakhomov, S., et al. (2016) · PubMed · Cited by 18 · see citing works
Gundlapalli, A. V., Brignone, E., Divita, G., et al. (2017) · PubMed · Cited by 16 · see citing works
Extracting Smoking Status from Electronic Health Records Using NLP and Deep Learning.
Rajendran, S., & Topaloĝlu, Ü. (2020) · PubMed · Cited by 15 · see citing works
Winden, T., Chen, E. S., Monsen, K. A., Wang, Y., & Melton, G. B. (2018) · Europe PMC (PubMed Central) · Cited by 14 · see citing works
Detecting Earlier Indicators of Homelessness in the Free Text of Medical Records
Redd, A. M., Carter, M., Divita, G., Shuying, S., Palmer, M. N., Samore, M. H., & Gundlapalli, A. V. (2014) · Studies in health technology and informatics · doi:10.3233/978-1-61499-423-7-153 · Cited by 13 · see citing works
Wray, C. M. (2021) · Federal Practitioner · doi:10.12788/fp.0080 · Cited by 11 · see citing works
Ontology-based specification and generation of search queries for post-market surveillance
Uciteli, A., Kropf, S., Weiland, T., et al. (2019) · Journal of Biomedical Semantics · doi:10.1186/s13326-019-0203-7 · Cited by 10 · see citing works
Extracting Sexual Trauma Mentions from Electronic Medical Notes Using Natural Language Processing
Divita, G., Brignone, E., Carter, M. E., et al. (2017) · Studies in health technology and informatics · doi:10.3233/978-1-61499-830-3-351 · Cited by 9 · see citing works
Residence, Living Situation, and Living Conditions Information Documentation in Clinical Practice.
Winden, T., Chen, E. S., Wang, Y., Lindemann, E. A., & Melton, G. B. (2017) · PubMed Central · Cited by 9 · see citing works
Natural Language Processing-Identified Problem Opioid Use and Its Associated Health Care Costs
Masters, E. T., Ramaprasan, A., Mardekian, J., Palmer, R. E., Gross, D. E., Cronkite, D., Von Korff, M., & Carrell, D. S. (2018) · Journal of Pain & Palliative Care Pharmacotherapy · doi:10.1080/15360288.2018.1488794 · Cited by 9 · see citing works
Skaljic, M., Patel, I. H., Pellegrini, A. M., et al. (2019) · JAMA Network Open · doi:10.1001/jamanetworkopen.2019.10399 · Cited by 9 · see citing works
Jones, A. L., Pettey, W. B. P., Carter, M. E., Brignone, E., Redd, A. M., Suo, Y., Divita, G., Blais, R. K., Fargo, J. D., & Gundlapalli, A. V. (2019) · PubMed Central · Cited by 9 · see citing works
Bhavsar, V., Sanyal, J., Patel, R., Shetty, H., Velupillai, S. U., Stewart, R. J., Broadbent, M. T. M., MacCabe, J. H., Das‐Munshi, J., & Howard, L. M. (2020) · BJPsych Open · doi:10.1192/bjo.2020.52 · Cited by 9 · see citing works
Gordon, D. D., Patel, I. H., Pellegrini, A. M., & Perlis, R. H. (2018) · JAMA Network Open · doi:10.1001/jamanetworkopen.2018.4178 · Cited by 8 · see citing works
How Confounder Strength Can Affect Allocation of Resources in Electronic Health Records.
Lynch, K. E., Whitcomb, B. W., & DuVall, S. L. (2018) · Europe PMC (PubMed Central) · Cited by 3 · see citing works
Zhu, V. J., Lenert, L. A., Bunnell, B. E., et al. (2019) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-019-0815-y · Cited by 2 · see citing works
Flagged first, then by citations
Research works printed but not linked: 4
- [25]Velupillai S, Mowery DL, Conway M, Hurdle J, Kious B. Vocabulary development to support information extraction of substance abuse from psychiatry notes. In: Proceedings of the 15th Workshop on Biomedical Natural Language Processing. Association for Computational Linguistics; 2016: 92– 101.
- [27]Mowery DL, South B, Patterson O, Zhu S-H, Conway M. Investigating the documentation of electronic cigarette use in the veteran affairs electronic health record: a pilot study. In: BioNLP 2017. Association for Computational Linguistics; 2017: 282– 286.
- [36]Jonnagaddala J, Dai H-J, Ray P, Liaw S-T. A preliminary study on automatic identification of patient smoking status in unstructured electronic health records. In: Proceedings of BioNLP 15. NLP; 2015: 147– 51.
- [52]Hammond KW, Laundry RJ. Application of a hybrid text mining approach to the study of suicidal behavior in a large population. In: 2014 47th Hawaii International Conference on System Sciences, IEEE, 2014: 2555– 2561; Waikoloa, HI.
Cited by
Sorted by citations
Large language models to identify social determinants of health in electronic health records
Guevara, M., Chen, S., Thomas, S., Chaunzwa, T. L., Franco, I., Kann, B. H., Moningi, S., Qian, J. M., Goldstein, M., Harper, S., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-023-00970-0 · Cited by 278 · see citing works
Ong, J. C. L., Seng, B. J. J., Law, J. Z. F., Low, L. L., Kwa, A. L. H., Giacomini, K. M., & Ting, D. S. W. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101356 · Cited by 111 · see citing works
Chen, S., Yu, J., Chamouni, S., Wang, Y., & Li, Y. (2024) · BMC Medicine · doi:10.1186/s12916-024-03566-x · Cited by 73 · see citing works
“By analyzing EHRs and integrating data on social and environmental factors, researchers have developed models that predict an individual’s risk of experiencing health disparities or poor health outcomes [24].”
BackgroundGolinelli, D., Pecoraro, V., Tedesco, D., Negro, A., Berti, E., Camerlingo, M. D., Alberghini, L., Lippi Bruni, M., Rolli, M., & Grilli, R. (2025) · BMC Health Services Research · doi:10.1186/s12913-025-12690-0 · Cited by 30 · see citing works
Keloth, V. K., Selek, S., Chen, Q., Gilman, C., Fu, S., Dang, Y., Chen, X., Hu, X., Zhou, Y., He, H., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01645-8 · Cited by 24 · see citing works
“This aligns with the comprehensive de fi nition of SDoH asoutlinedby Healthy People 2030 5 and is supported by a systematic review of prior literature on SDoH 6 .”
Bhate, N. J., Mittal, A., He, Z., & Luo, X. (2023) · 2023 IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386811 · Cited by 18 · see citing works
Iott, B. E., Rivas, S., Gottlieb, L. M., Adler-Milstein, J., & Pantell, M. S. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocad261 · Cited by 17 · see citing works
Abbott, E. E., Apakama, D., Richardson, L. D., Chan, L., & Nadkarni, G. N. (2024) · JMIR Medical Informatics · doi:10.2196/57124 · Cited by 9 · see citing works
Sood, N., Stetter, C., Kunselman, A., & Jasani, S. (2024) · PLOS Digital Health · doi:10.1371/journal.pdig.0000428 · Cited by 6 · see citing works
Development and Validation of Computable Social Phenotypes for Health‐Related Social Needs
Gregory, M. E., Kasthurirathne, S. N., Magoc, T., McNamee, C., Harle, C. A., & Vest, J. R. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae150 · Cited by 2 · see citing works
- Authors
- Patra, B. G., Sharma, M. M., Vekaria, V., Adekkanattu, P., Patterson, O. V., Glicksberg, B., Lepow, L. A., Ryu, E., Biernacka, J. M., Furmanchuk, A., George, T. J., Hogan, W., Wu, Y., Yang, X., Bian, J., Weissman, M., Wickramaratne, P., Mann, J. J., Olfson, M., Campion, T. R., Weiner, M., & Pathak, J.
- Affiliations
- Weill Cornell Medicine (US)
- University of Utah (US)
- Icahn School of Medicine at Mount Sinai (US)
- Mayo Clinic (US)
- Northwestern University (US)
- University of Florida Health (US)
- Columbia University (US)
- Corresponding author
- Pathak, Jyotishman
- ORCID
- Patra, Braja G
- Sharma, Mohit M
- Vekaria, Veer
- Adekkanattu, Prakash
- Patterson, Olga V
- Glicksberg, Benjamin
- Lepow, Lauren A
- Ryu, Euijung
- Biernacka, Joanna M
- Furmanchuk, Al’ona
- George, Thomas J
- Hogan, William
- Wu, Yonghui
- Yang, Xi
- Bian, Jiang
- Weissman, Myrna
- Wickramaratne, Priya
- Mann, J John
- Olfson, Mark
- Campion, Thomas R
- Weiner, Mark
- Pathak, Jyotishman
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2021-10-06
- Volume
- 28
- Issue
- 12
- Pages
- 2716-2727
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC8633615
- OpenAlex
- W3202375701
- Cited by
- 248
- References
- 88
- Citations by year
- 2026: 36 · 2025: 67 · 2024: 64 · 2023: 53 · 2022: 28
- FWCI
- 2.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Journal of the American Medical Informatics Association (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Funders
- NIH (R01MH119177, R01MH121907, R01MH121922)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning in Healthcare
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Health Sciences › Nursing › Issues, ethics and legal aspects › Nursing Diagnosis and Documentation
- Keywords
- social determinants of health, electronic health records, natural language processing, clinical text extraction, unstructured clinical notes, smoking status, substance use, homelessness, alcohol consumption, rule-based extraction, machine learning, clinical decision support, risk prediction model
- MeSH terms
- Machine Learning, Data Management, Humans, Natural Language Processing, Electronic Health Records, Social Determinants of Health
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
