Using Natural Language Processing to Identify Stigmatizing Language in Labor and Birth Clinical Notes

Introduction Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natura...

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Published in:	Maternal and child health journal Vol. 28; no. 3; pp. 578 - 586
Main Authors:	Barcelona, Veronica, Scharp, Danielle, Moen, Hans, Davoudi, Anahita, Idnay, Betina R., Cato, Kenrick, Topaz, Maxim
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.03.2024 Springer Springer Nature B.V
Subjects:	Algorithms Analysis Bias Childrens health Computational linguistics Decision trees Deep learning Demographic aspects Electronic health records Gynecology Health aspects Health disparities Health problems Informatics Investment bankers Language Language processing Machine learning Maternal & child health Maternal and Child Health Maternal health services Medical records Medicine Medicine & Public Health Natural language interfaces Natural language processing Pediatrics Performance evaluation Population Economics Power Public Health Sociology Stigma Stigma (Social psychology) Support vector machines United States Electronic health records Bias Natural language processing
ISSN:	1092-7875, 1573-6628, 1573-6628
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Abstract	Introduction Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natural language processing (NLP) methods to identify two types of stigmatizing language: marginalizing language and its complement, power/privilege language, accurately and automatically in labor and birth notes. Methods We analyzed notes for all birthing people > 20 weeks’ gestation admitted for labor and birth at two hospitals during 2017. We then employed text preprocessing techniques, specifically using TF-IDF values as inputs, and tested machine learning classification algorithms to identify stigmatizing and power/privilege language in clinical notes. The algorithms assessed included Decision Trees, Random Forest, and Support Vector Machines. Additionally, we applied a feature importance evaluation method (InfoGain) to discern words that are highly correlated with these language categories. Results For marginalizing language, Decision Trees yielded the best classification with an F-score of 0.73. For power/privilege language, Support Vector Machines performed optimally, achieving an F-score of 0.91. These results demonstrate the effectiveness of the selected machine learning methods in classifying language categories in clinical notes. Conclusion We identified well-performing machine learning methods to automatically detect stigmatizing language in clinical notes. To our knowledge, this is the first study to use NLP performance metrics to evaluate the performance of machine learning methods in discerning stigmatizing language. Future studies should delve deeper into refining and evaluating NLP methods, incorporating the latest algorithms rooted in deep learning. Significance What is Already Known on this Subject? Traditional informatics methods include natural language processing, and these methods have been increasingly applied to the study of public health problems using electronic health records. What this Study Adds? We identified well-performing machine learning methods to automatically identify stigmatizing language in labor and birth clinical notes. These methods have not been applied to labor and birth clinical notes and have the potential to be a powerful tool in examining perinatal health inequities.
AbstractList	Introduction Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natural language processing (NLP) methods to identify two types of stigmatizing language: marginalizing language and its complement, power/privilege language, accurately and automatically in labor and birth notes. Methods We analyzed notes for all birthing people > 20 weeks' gestation admitted for labor and birth at two hospitals during 2017. We then employed text preprocessing techniques, specifically using TF-IDF values as inputs, and tested machine learning classification algorithms to identify stigmatizing and power/privilege language in clinical notes. The algorithms assessed included Decision Trees, Random Forest, and Support Vector Machines. Additionally, we applied a feature importance evaluation method (InfoGain) to discern words that are highly correlated with these language categories. Results For marginalizing language, Decision Trees yielded the best classification with an F-score of 0.73. For power/privilege language, Support Vector Machines performed optimally, achieving an F-score of 0.91. These results demonstrate the effectiveness of the selected machine learning methods in classifying language categories in clinical notes. Conclusion We identified well-performing machine learning methods to automatically detect stigmatizing language in clinical notes. To our knowledge, this is the first study to use NLP performance metrics to evaluate the performance of machine learning methods in discerning stigmatizing language. Future studies should delve deeper into refining and evaluating NLP methods, incorporating the latest algorithms rooted in deep learning. Introduction Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natural language processing (NLP) methods to identify two types of stigmatizing language: marginalizing language and its complement, power/privilege language, accurately and automatically in labor and birth notes.Methods We analyzed notes for all birthing people > 20 weeks’ gestation admitted for labor and birth at two hospitals during 2017. We then employed text preprocessing techniques, specifically using TF-IDF values as inputs, and tested machine learning classification algorithms to identify stigmatizing and power/privilege language in clinical notes. The algorithms assessed included Decision Trees, Random Forest, and Support Vector Machines. Additionally, we applied a feature importance evaluation method (InfoGain) to discern words that are highly correlated with these language categories.Results For marginalizing language, Decision Trees yielded the best classification with an F-score of 0.73. For power/privilege language, Support Vector Machines performed optimally, achieving an F-score of 0.91. These results demonstrate the effectiveness of the selected machine learning methods in classifying language categories in clinical notes.Conclusion We identified well-performing machine learning methods to automatically detect stigmatizing language in clinical notes. To our knowledge, this is the first study to use NLP performance metrics to evaluate the performance of machine learning methods in discerning stigmatizing language. Future studies should delve deeper into refining and evaluating NLP methods, incorporating the latest algorithms rooted in deep learning.SignificanceWhat is Already Known on this Subject?Traditional informatics methods include natural language processing, and these methods have been increasingly applied to the study of public health problems using electronic health records.What this Study Adds?We identified well-performing machine learning methods to automatically identify stigmatizing language in labor and birth clinical notes. These methods have not been applied to labor and birth clinical notes and have the potential to be a powerful tool in examining perinatal health inequities. Introduction Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natural language processing (NLP) methods to identify two types of stigmatizing language: marginalizing language and its complement, power/privilege language, accurately and automatically in labor and birth notes. Methods We analyzed notes for all birthing people > 20 weeks’ gestation admitted for labor and birth at two hospitals during 2017. We then employed text preprocessing techniques, specifically using TF-IDF values as inputs, and tested machine learning classification algorithms to identify stigmatizing and power/privilege language in clinical notes. The algorithms assessed included Decision Trees, Random Forest, and Support Vector Machines. Additionally, we applied a feature importance evaluation method (InfoGain) to discern words that are highly correlated with these language categories. Results For marginalizing language, Decision Trees yielded the best classification with an F-score of 0.73. For power/privilege language, Support Vector Machines performed optimally, achieving an F-score of 0.91. These results demonstrate the effectiveness of the selected machine learning methods in classifying language categories in clinical notes. Conclusion We identified well-performing machine learning methods to automatically detect stigmatizing language in clinical notes. To our knowledge, this is the first study to use NLP performance metrics to evaluate the performance of machine learning methods in discerning stigmatizing language. Future studies should delve deeper into refining and evaluating NLP methods, incorporating the latest algorithms rooted in deep learning. Significance What is Already Known on this Subject? Traditional informatics methods include natural language processing, and these methods have been increasingly applied to the study of public health problems using electronic health records. What this Study Adds? We identified well-performing machine learning methods to automatically identify stigmatizing language in labor and birth clinical notes. These methods have not been applied to labor and birth clinical notes and have the potential to be a powerful tool in examining perinatal health inequities. Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natural language processing (NLP) methods to identify two types of stigmatizing language: marginalizing language and its complement, power/privilege language, accurately and automatically in labor and birth notes.INTRODUCTIONStigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natural language processing (NLP) methods to identify two types of stigmatizing language: marginalizing language and its complement, power/privilege language, accurately and automatically in labor and birth notes.We analyzed notes for all birthing people > 20 weeks' gestation admitted for labor and birth at two hospitals during 2017. We then employed text preprocessing techniques, specifically using TF-IDF values as inputs, and tested machine learning classification algorithms to identify stigmatizing and power/privilege language in clinical notes. The algorithms assessed included Decision Trees, Random Forest, and Support Vector Machines. Additionally, we applied a feature importance evaluation method (InfoGain) to discern words that are highly correlated with these language categories.METHODSWe analyzed notes for all birthing people > 20 weeks' gestation admitted for labor and birth at two hospitals during 2017. We then employed text preprocessing techniques, specifically using TF-IDF values as inputs, and tested machine learning classification algorithms to identify stigmatizing and power/privilege language in clinical notes. The algorithms assessed included Decision Trees, Random Forest, and Support Vector Machines. Additionally, we applied a feature importance evaluation method (InfoGain) to discern words that are highly correlated with these language categories.For marginalizing language, Decision Trees yielded the best classification with an F-score of 0.73. For power/privilege language, Support Vector Machines performed optimally, achieving an F-score of 0.91. These results demonstrate the effectiveness of the selected machine learning methods in classifying language categories in clinical notes.RESULTSFor marginalizing language, Decision Trees yielded the best classification with an F-score of 0.73. For power/privilege language, Support Vector Machines performed optimally, achieving an F-score of 0.91. These results demonstrate the effectiveness of the selected machine learning methods in classifying language categories in clinical notes.We identified well-performing machine learning methods to automatically detect stigmatizing language in clinical notes. To our knowledge, this is the first study to use NLP performance metrics to evaluate the performance of machine learning methods in discerning stigmatizing language. Future studies should delve deeper into refining and evaluating NLP methods, incorporating the latest algorithms rooted in deep learning.CONCLUSIONWe identified well-performing machine learning methods to automatically detect stigmatizing language in clinical notes. To our knowledge, this is the first study to use NLP performance metrics to evaluate the performance of machine learning methods in discerning stigmatizing language. Future studies should delve deeper into refining and evaluating NLP methods, incorporating the latest algorithms rooted in deep learning. Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natural language processing (NLP) methods to identify two types of stigmatizing language: marginalizing language and its complement, power/privilege language, accurately and automatically in labor and birth notes. We analyzed notes for all birthing people > 20 weeks' gestation admitted for labor and birth at two hospitals during 2017. We then employed text preprocessing techniques, specifically using TF-IDF values as inputs, and tested machine learning classification algorithms to identify stigmatizing and power/privilege language in clinical notes. The algorithms assessed included Decision Trees, Random Forest, and Support Vector Machines. Additionally, we applied a feature importance evaluation method (InfoGain) to discern words that are highly correlated with these language categories. For marginalizing language, Decision Trees yielded the best classification with an F-score of 0.73. For power/privilege language, Support Vector Machines performed optimally, achieving an F-score of 0.91. These results demonstrate the effectiveness of the selected machine learning methods in classifying language categories in clinical notes. We identified well-performing machine learning methods to automatically detect stigmatizing language in clinical notes. To our knowledge, this is the first study to use NLP performance metrics to evaluate the performance of machine learning methods in discerning stigmatizing language. Future studies should delve deeper into refining and evaluating NLP methods, incorporating the latest algorithms rooted in deep learning. Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias is the study of stigmatizing language in the electronic health record. The objective of our study was to develop automated natural language processing (NLP) methods to identify two types of stigmatizing language: marginalizing language and its complement, power/privilege language, accurately and automatically in labor and birth notes. We analyzed notes for all birthing people > 20 weeks' gestation admitted for labor and birth at two hospitals during 2017. We then employed text preprocessing techniques, specifically using TF-IDF values as inputs, and tested machine learning classification algorithms to identify stigmatizing and power/privilege language in clinical notes. The algorithms assessed included Decision Trees, Random Forest, and Support Vector Machines. Additionally, we applied a feature importance evaluation method (InfoGain) to discern words that are highly correlated with these language categories. For marginalizing language, Decision Trees yielded the best classification with an F-score of 0.73. For power/privilege language, Support Vector Machines performed optimally, achieving an F-score of 0.91. These results demonstrate the effectiveness of the selected machine learning methods in classifying language categories in clinical notes. We identified well-performing machine learning methods to automatically detect stigmatizing language in clinical notes. To our knowledge, this is the first study to use NLP performance metrics to evaluate the performance of machine learning methods in discerning stigmatizing language. Future studies should delve deeper into refining and evaluating NLP methods, incorporating the latest algorithms rooted in deep learning.
Audience	Academic
Author	Barcelona, Veronica Scharp, Danielle Davoudi, Anahita Cato, Kenrick Idnay, Betina R. Moen, Hans Topaz, Maxim
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References	MartinJAOstermanMJKDescribing the increase in Preterm Births in the United States, 2014–2016NCHS data Brief2018(312)31218 CoyneITSampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries?Journal of Advanced Nursing19972636236301:STN:280:DyaK1c%2FitVylsA%3D%3D10.1046/j.1365-2648.1997.t01-25-00999.x DrewniakDKronesTWildVDo attitudes and behavior of health care professionals exacerbate health care disparities among immigrant and ethnic minority groups? An integrative literature reviewInternational Journal of Nursing Studies201770899810.1016/j.ijnurstu.2017.02.015 BeachMCSahaSParkJTaylorJDrewPPlankECheeBTestimonial injustice: Linguistic Bias in the Medical Records of Black Patients and womenJournal of General Internal Medicine20213661708171410.1007/s11606-021-06682-z[doi] KravitzESuhMRussellMOjedaALevisonJMcKinneyJScreening for Substance Use disorders during pregnancy: A decision at the intersection of racial and Reproductive JusticeAmerican Journal of Perinatology202110.1055/s-0041-1739433 EverettBGLimburgAMcKettaSHatzenbuehlerMLState-Level regulations regarding the protection of sexual minorities and birth outcomes: Results from a Population-based Cohort StudyPsychosomatic Medicine202284665866810.1097/psy.0000000000001092 KimHSefcikJSBradwayCCharacteristics of qualitative descriptive studies: A systematic reviewResearch in Nursing & Health201740123421:CAS:528:DC%2BC28XitFKlt77N10.1002/nur.21768 Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. MartinKStanfordCAn analysis of documentation language and word choice among forensic mental health nursesInternational Journal of Mental Health Nursing20202961241125210.1111/inm.12763 LockeSBAl-AdelyAMooreSWilsonJKitchenANatural language processing in medicine: A reviewTrends in Anaesthesia and Critical care2021384910.1016/j.tacc.2021.02.007 Manning, C. D. R., & Schütze, P., H (2008). Introduction to information retrieval (Vol. 39). Cambridge University Press. HsiehHFShannonSEThree approaches to qualitative content analysisQualitative Health Research20051591277128810.1177/1049732305276687 GohYCCaiXQTheseiraWKoGKhorKAEvaluating human versus machine learning performance in classifying research abstractsScientometrics20201252119712121:CAS:528:DC%2BB3cXhsVOjt7zM10.1007/s11192-020-03614-2 PhilipsbornRPSorscherEASexsonWEvansHHBorn on U.S. Soil: Access to Healthcare for neonates of non-citizensMaternal and Child Health Journal202125191410.1007/s10995-020-03020-3 MinehartRDBryantASJacksonJDalyJLRacial/Ethnic inequities in pregnancy-related morbidity and mortalityObstet Gynecol Clin North Am2021481315110.1016/j.ogc.2020.11.005 LandauAYBlanchardACatoKAtkinsNSalazarSPattonDUTopazMConsiderations for development of Child Abuse and neglect phenotype with implications for reduction of racial bias: A qualitative studyJournal of the American Medical Informatics Association202229351251910.1093/jamia/ocab275 Quinlan, J. R. (2014). C4. 5: Programs for Machine Learning. 58–60. https://books.google.com/books/about/C4_5.html?id=b3ujBQAAQBAJ. GodduAPO’ConorKJLanzkronSSaheedMOSahaSPeekMEBeachMCDo words Matter? Stigmatizing Language and the transmission of Bias in the medical recordJournal of General Internal Medicine201833568569110.1007/s11606-017-4289-2[doi] Bridle, J. S. (1990). Probabilistic interpretation of Feedforward Classification Network Outputs, with relationships to Statistical Pattern Recognition. In F. F. Soulié, & J. Hérault (Eds.), Neurocomputing (Vol. 68). Springer. SandelowskiMWhat’s in a name? Qualitative description revisitedResearch in Nursing & Health2010331778410.1002/nur.20362 Tiwary, U. S. S., T (2008). Natural Language Processing and Information Retrieval. Oxford University Press, Inc. https://dl.acm.org/doi/abs/10.5555/1481140. TogiokaBMSeligmanKMDelgadoCMLimited English proficiency in the labor and delivery unitCurrent Opinion in Anaesthesiology202235328529110.1097/aco.0000000000001131 AlpertJMMorrisBBThomsonMDMatinKGeyerCEBrownRFOpenNotes in oncology: Oncologists’ perceptions and a baseline of the content and style of their clinician notes Translational Behavioral Medicine20199234735610.1093/tbm/iby029 OmenkaOIWatsonDPHendrieHCUnderstanding the healthcare experiences and needs of African immigrants in the United States: A scoping reviewBMC Public Health20202012710.1186/s12889-019-8127-9 Barcelona, V., Scharp, D., Idnay, B. R., Moen, H., Goffman, D., Cato, K., & Topaz, M. (2023b). A qualitative analysis of stigmatizing language in birth admission clinical notes. Nursing Inquiry, e12557. https://doi.org/10.1111/nin.12557. SunMOliwaTPeekMETungELNegative patient descriptors: Documenting racial BiasHealth Aff (Millwood)202241220321110.1377/hlthaff.2021.01423 BertholdMRCDillNGabrielFKotterTRMeinlTOhlTThielPWiswedelKKNIME – the Konstanz Information MinerAcM SIGKDD Explorations Newsletter2009111263110.1145/1656274.1656280 HooverKLockhartSCallisterCHoltropJSCalcaterraSLExperiences of stigma in hospitals with addiction consultation services: A qualitative analysis of patients’ and hospital-based providers’ perspectivesJournal of Substance Abuse Treatment20221381087081:CAS:528:DC%2BB38XitVehurjK10.1016/j.jsat.2021.108708 JindalMThorntonRLJMcRaeAUnakaNJohnsonTJMistryKBEffects of a curriculum addressing racism on Pediatric residents’ racial biases and EmpathyJ Grad Med Educ202214440741310.4300/jgme-d-21-01048.1 Braveman, P., Dominguez, T. P., Burke, W., Dolan, S. M., Stevenson, D. K., Jackson, F. M., & Waddell, L. (2021). Explaining the black-white disparity in Preterm Birth: A Consensus Statement from a Multi-disciplinary Scientific Work Group convened by the March of dimes [Review]. 3. https://doi.org/10.3389/frph.2021.684207. Ho, T. K. (1995). Random decision forests. The Institute of Electronical and Electronics Engineers (IEEE), In Proceedings of 3rd international conference on document analysis and recognition. MaloufRRedshawMKurinczukJJGrayRSystematic review of heath care interventions to improve outcomes for women with disability and their family during pregnancy, birth and postnatal periodBmc Pregnancy and Childbirth2014145810.1186/1471-2393-14-58 United States Department of Health and Human Services (2020). 08/04/2020). 21st Century Cures Act: Interoperability, information blocking, and the ONC health IT certification program National Archives. Retrieved November 5 from https://www.federalregister.gov/documents/2020/05/01/2020-07419/21st-century-cures-act-interoperability-information-blocking-and-the-onc-health-it-certification. HimmelsteinGBatesDZhouLExamination of stigmatizing Language in the Electronic Health RecordJAMA Netw Open202251e214496710.1001/jamanetworkopen.2021.44967 BarcelonaVHortonRLRivlinKHarkinsSGreenCRobinsonKTopazMThe Power of Language in Hospital Care for pregnant and Birthing people: A vision for changeObstetrics & Gynecology202310.1097/AOG.0000000000005333 ShattellMMStigmatizing language with unintended meanings: Persons with mental Illness or mentally ill persons?Issues in Mental Health Nursing200930319910.1080/01612840802694668 Park, J., Saha, S., Chee, B., Taylor, J., & Beach, M. C. (2021). Physician use of stigmatizing Language in Patient Medical records. JAMA Network open, 4(7). https://doi.org/10.1001/jamanetworkopen.2021.17052. FernándezLFossaADongZDelbancoTElmoreJFitzgeraldPDesRochesCWords Matter: What do patients find judgmental or Offensive in Outpatient notes?Journal of General Internal Medicine20213692571257810.1007/s11606-020-06432-7 Vaswani, A. S., Parmar, N., Uszkoreit, N., Jones, J., Gomez, L., Kaiser, A. N., & Polosukhin, L. (2017). I. Attention is all you need. Advances in neural information processing systems https://arxiv.org/abs/1706.03762. Alpaydin, E. (2020). Introduction to machine learning, fourth edition. MIT Press. https://books.google.com/books?id=tZnSDwAAQBAJ. HallWJChapmanMVLeeKMMerinoYMThomasTWPayneBKCoyne-BeasleyTImplicit Racial/Ethnic Bias among Health Care Professionals and its influence on Health Care outcomes: A systematic reviewAmerican Journal of Public Health201510512e607610.2105/AJPH.2015.302903[doi] Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. European conference on machine learning Berlin, Heidelberg. 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References_xml	– reference: ShattellMMStigmatizing language with unintended meanings: Persons with mental Illness or mentally ill persons?Issues in Mental Health Nursing200930319910.1080/01612840802694668 – reference: Vaswani, A. S., Parmar, N., Uszkoreit, N., Jones, J., Gomez, L., Kaiser, A. N., & Polosukhin, L. (2017). I. Attention is all you need. Advances in neural information processing systems https://arxiv.org/abs/1706.03762. – reference: Quinlan, J. R. (2014). C4. 5: Programs for Machine Learning. 58–60. https://books.google.com/books/about/C4_5.html?id=b3ujBQAAQBAJ. – reference: Manning, C. D. R., & Schütze, P., H (2008). Introduction to information retrieval (Vol. 39). Cambridge University Press. – reference: PhilipsbornRPSorscherEASexsonWEvansHHBorn on U.S. Soil: Access to Healthcare for neonates of non-citizensMaternal and Child Health Journal202125191410.1007/s10995-020-03020-3 – reference: DrewniakDKronesTWildVDo attitudes and behavior of health care professionals exacerbate health care disparities among immigrant and ethnic minority groups? An integrative literature reviewInternational Journal of Nursing Studies201770899810.1016/j.ijnurstu.2017.02.015 – reference: GohYCCaiXQTheseiraWKoGKhorKAEvaluating human versus machine learning performance in classifying research abstractsScientometrics20201252119712121:CAS:528:DC%2BB3cXhsVOjt7zM10.1007/s11192-020-03614-2 – reference: HooverKLockhartSCallisterCHoltropJSCalcaterraSLExperiences of stigma in hospitals with addiction consultation services: A qualitative analysis of patients’ and hospital-based providers’ perspectivesJournal of Substance Abuse Treatment20221381087081:CAS:528:DC%2BB38XitVehurjK10.1016/j.jsat.2021.108708 – reference: SandelowskiMWhat’s in a name? Qualitative description revisitedResearch in Nursing & Health2010331778410.1002/nur.20362 – reference: AlpertJMMorrisBBThomsonMDMatinKGeyerCEBrownRFOpenNotes in oncology: Oncologists’ perceptions and a baseline of the content and style of their clinician notes Translational Behavioral Medicine20199234735610.1093/tbm/iby029 – reference: Bridle, J. S. (1990). Probabilistic interpretation of Feedforward Classification Network Outputs, with relationships to Statistical Pattern Recognition. In F. F. Soulié, & J. Hérault (Eds.), Neurocomputing (Vol. 68). Springer. – reference: MartinJAOstermanMJKDescribing the increase in Preterm Births in the United States, 2014–2016NCHS data Brief2018(312)31218 – reference: BarcelonaVHortonRLRivlinKHarkinsSGreenCRobinsonKTopazMThe Power of Language in Hospital Care for pregnant and Birthing people: A vision for changeObstetrics & Gynecology202310.1097/AOG.0000000000005333 – reference: BeachMCSahaSParkJTaylorJDrewPPlankECheeBTestimonial injustice: Linguistic Bias in the Medical Records of Black Patients and womenJournal of General Internal Medicine20213661708171410.1007/s11606-021-06682-z[doi] – reference: JindalMThorntonRLJMcRaeAUnakaNJohnsonTJMistryKBEffects of a curriculum addressing racism on Pediatric residents’ racial biases and EmpathyJ Grad Med Educ202214440741310.4300/jgme-d-21-01048.1 – reference: Barcelona, V., Scharp, D., Idnay, B. R., Moen, H., Goffman, D., Cato, K., & Topaz, M. (2023b). A qualitative analysis of stigmatizing language in birth admission clinical notes. Nursing Inquiry, e12557. https://doi.org/10.1111/nin.12557. – reference: Ho, T. K. (1995). Random decision forests. The Institute of Electronical and Electronics Engineers (IEEE), In Proceedings of 3rd international conference on document analysis and recognition. – reference: LockeSBAl-AdelyAMooreSWilsonJKitchenANatural language processing in medicine: A reviewTrends in Anaesthesia and Critical care2021384910.1016/j.tacc.2021.02.007 – reference: HsiehHFShannonSEThree approaches to qualitative content analysisQualitative Health Research20051591277128810.1177/1049732305276687 – reference: FernándezLFossaADongZDelbancoTElmoreJFitzgeraldPDesRochesCWords Matter: What do patients find judgmental or Offensive in Outpatient notes?Journal of General Internal Medicine20213692571257810.1007/s11606-020-06432-7 – reference: BertholdMRCDillNGabrielFKotterTRMeinlTOhlTThielPWiswedelKKNIME – the Konstanz Information MinerAcM SIGKDD Explorations Newsletter2009111263110.1145/1656274.1656280 – reference: GodduAPO’ConorKJLanzkronSSaheedMOSahaSPeekMEBeachMCDo words Matter? Stigmatizing Language and the transmission of Bias in the medical recordJournal of General Internal Medicine201833568569110.1007/s11606-017-4289-2[doi] – reference: Alpaydin, E. (2020). Introduction to machine learning, fourth edition. MIT Press. https://books.google.com/books?id=tZnSDwAAQBAJ. – reference: Park, J., Saha, S., Chee, B., Taylor, J., & Beach, M. C. (2021). Physician use of stigmatizing Language in Patient Medical records. JAMA Network open, 4(7). https://doi.org/10.1001/jamanetworkopen.2021.17052. – reference: OmenkaOIWatsonDPHendrieHCUnderstanding the healthcare experiences and needs of African immigrants in the United States: A scoping reviewBMC Public Health20202012710.1186/s12889-019-8127-9 – reference: MartinKStanfordCAn analysis of documentation language and word choice among forensic mental health nursesInternational Journal of Mental Health Nursing20202961241125210.1111/inm.12763 – reference: Braveman, P., Dominguez, T. P., Burke, W., Dolan, S. M., Stevenson, D. K., Jackson, F. M., & Waddell, L. (2021). Explaining the black-white disparity in Preterm Birth: A Consensus Statement from a Multi-disciplinary Scientific Work Group convened by the March of dimes [Review]. 3. https://doi.org/10.3389/frph.2021.684207. – reference: MaloufRRedshawMKurinczukJJGrayRSystematic review of heath care interventions to improve outcomes for women with disability and their family during pregnancy, birth and postnatal periodBmc Pregnancy and Childbirth2014145810.1186/1471-2393-14-58 – reference: KimHSefcikJSBradwayCCharacteristics of qualitative descriptive studies: A systematic reviewResearch in Nursing & Health201740123421:CAS:528:DC%2BC28XitFKlt77N10.1002/nur.21768 – reference: SunMOliwaTPeekMETungELNegative patient descriptors: Documenting racial BiasHealth Aff (Millwood)202241220321110.1377/hlthaff.2021.01423 – reference: HallWJChapmanMVLeeKMMerinoYMThomasTWPayneBKCoyne-BeasleyTImplicit Racial/Ethnic Bias among Health Care Professionals and its influence on Health Care outcomes: A systematic reviewAmerican Journal of Public Health201510512e607610.2105/AJPH.2015.302903[doi] – reference: Joachims, T. (1998). Text categorization with support vector machines: Learning with many relevant features. European conference on machine learning Berlin, Heidelberg. – reference: KravitzESuhMRussellMOjedaALevisonJMcKinneyJScreening for Substance Use disorders during pregnancy: A decision at the intersection of racial and Reproductive JusticeAmerican Journal of Perinatology202110.1055/s-0041-1739433 – reference: TogiokaBMSeligmanKMDelgadoCMLimited English proficiency in the labor and delivery unitCurrent Opinion in Anaesthesiology202235328529110.1097/aco.0000000000001131 – reference: EverettBGLimburgAMcKettaSHatzenbuehlerMLState-Level regulations regarding the protection of sexual minorities and birth outcomes: Results from a Population-based Cohort StudyPsychosomatic Medicine202284665866810.1097/psy.0000000000001092 – reference: LandauAYBlanchardACatoKAtkinsNSalazarSPattonDUTopazMConsiderations for development of Child Abuse and neglect phenotype with implications for reduction of racial bias: A qualitative studyJournal of the American Medical Informatics Association202229351251910.1093/jamia/ocab275 – reference: Tiwary, U. S. S., T (2008). Natural Language Processing and Information Retrieval. Oxford University Press, Inc. https://dl.acm.org/doi/abs/10.5555/1481140. – reference: HimmelsteinGBatesDZhouLExamination of stigmatizing Language in the Electronic Health RecordJAMA Netw Open202251e214496710.1001/jamanetworkopen.2021.44967 – reference: MinehartRDBryantASJacksonJDalyJLRacial/Ethnic inequities in pregnancy-related morbidity and mortalityObstet Gynecol Clin North Am2021481315110.1016/j.ogc.2020.11.005 – reference: CoyneITSampling in qualitative research. Purposeful and theoretical sampling; merging or clear boundaries?Journal of Advanced Nursing19972636236301:STN:280:DyaK1c%2FitVylsA%3D%3D10.1046/j.1365-2648.1997.t01-25-00999.x – reference: United States Department of Health and Human Services (2020). 08/04/2020). 21st Century Cures Act: Interoperability, information blocking, and the ONC health IT certification program National Archives. Retrieved November 5 from https://www.federalregister.gov/documents/2020/05/01/2020-07419/21st-century-cures-act-interoperability-information-blocking-and-the-onc-health-it-certification. – reference: Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. – volume: 35 start-page: 285 issue: 3 year: 2022 ident: 3857_CR40 publication-title: Current Opinion in Anaesthesiology doi: 10.1097/aco.0000000000001131 – volume: 138 start-page: 108708 year: 2022 ident: 3857_CR19 publication-title: Journal of Substance Abuse Treatment doi: 10.1016/j.jsat.2021.108708 – ident: 3857_CR18 – volume: 5 start-page: e2144967 issue: 1 year: 2022 ident: 3857_CR17 publication-title: JAMA Netw Open doi: 10.1001/jamanetworkopen.2021.44967 – ident: 3857_CR28 doi: 10.1017/CBO9780511809071 – ident: 3857_CR39 – volume: (312) start-page: 1 issue: 312 year: 2018 ident: 3857_CR29 publication-title: NCHS data Brief – volume: 29 start-page: 512 issue: 3 year: 2022 ident: 3857_CR25 publication-title: Journal of the American Medical Informatics Association doi: 10.1093/jamia/ocab275 – ident: 3857_CR4 doi: 10.1111/nin.12557 – volume: 29 start-page: 1241 issue: 6 year: 2020 ident: 3857_CR30 publication-title: International Journal of Mental Health Nursing doi: 10.1111/inm.12763 – volume: 125 start-page: 1197 issue: 2 year: 2020 ident: 3857_CR15 publication-title: Scientometrics doi: 10.1007/s11192-020-03614-2 – volume: 84 start-page: 658 issue: 6 year: 2022 ident: 3857_CR12 publication-title: Psychosomatic Medicine doi: 10.1097/psy.0000000000001092 – ident: 3857_CR33 doi: 10.1001/jamanetworkopen.2021.17052 – volume: 105 start-page: e60 issue: 12 year: 2015 ident: 3857_CR16 publication-title: American Journal of Public Health doi: 10.2105/AJPH.2015.302903 – ident: 3857_CR8 doi: 10.1007/978-3-642-76153-9_28 – ident: 3857_CR1 – volume: 48 start-page: 31 issue: 1 year: 2021 ident: 3857_CR31 publication-title: Obstet Gynecol Clin North Am doi: 10.1016/j.ogc.2020.11.005 – volume: 15 start-page: 1277 issue: 9 year: 2005 ident: 3857_CR20 publication-title: Qualitative Health Research doi: 10.1177/1049732305276687 – volume: 41 start-page: 203 issue: 2 year: 2022 ident: 3857_CR38 publication-title: Health Aff (Millwood) doi: 10.1377/hlthaff.2021.01423 – volume: 14 start-page: 58 year: 2014 ident: 3857_CR27 publication-title: Bmc Pregnancy and Childbirth doi: 10.1186/1471-2393-14-58 – ident: 3857_CR41 – year: 2023 ident: 3857_CR3 publication-title: Obstetrics & Gynecology doi: 10.1097/AOG.0000000000005333 – volume: 11 start-page: 26 issue: 1 year: 2009 ident: 3857_CR6 publication-title: AcM SIGKDD Explorations Newsletter doi: 10.1145/1656274.1656280 – year: 2021 ident: 3857_CR24 publication-title: American Journal of Perinatology doi: 10.1055/s-0041-1739433 – volume: 30 start-page: 199 issue: 3 year: 2009 ident: 3857_CR37 publication-title: Issues in Mental Health Nursing doi: 10.1080/01612840802694668 – ident: 3857_CR22 doi: 10.1007/BFb0026683 – volume: 36 start-page: 1708 issue: 6 year: 2021 ident: 3857_CR5 publication-title: Journal of General Internal Medicine doi: 10.1007/s11606-021-06682-z – ident: 3857_CR7 doi: 10.3389/frph.2021.684207 – volume: 33 start-page: 685 issue: 5 year: 2018 ident: 3857_CR14 publication-title: Journal of General Internal Medicine doi: 10.1007/s11606-017-4289-2 – volume: 20 start-page: 27 issue: 1 year: 2020 ident: 3857_CR32 publication-title: BMC Public Health doi: 10.1186/s12889-019-8127-9 – ident: 3857_CR10 – volume: 38 start-page: 4 year: 2021 ident: 3857_CR26 publication-title: Trends in Anaesthesia and Critical care doi: 10.1016/j.tacc.2021.02.007 – volume: 14 start-page: 407 issue: 4 year: 2022 ident: 3857_CR21 publication-title: J Grad Med Educ doi: 10.4300/jgme-d-21-01048.1 – ident: 3857_CR35 – volume: 26 start-page: 623 issue: 3 year: 1997 ident: 3857_CR9 publication-title: Journal of Advanced Nursing doi: 10.1046/j.1365-2648.1997.t01-25-00999.x – volume: 70 start-page: 89 year: 2017 ident: 3857_CR11 publication-title: International Journal of Nursing Studies doi: 10.1016/j.ijnurstu.2017.02.015 – volume: 9 start-page: 347 issue: 2 year: 2019 ident: 3857_CR2 publication-title: Translational Behavioral Medicine doi: 10.1093/tbm/iby029 – volume: 33 start-page: 77 issue: 1 year: 2010 ident: 3857_CR36 publication-title: Research in Nursing & Health doi: 10.1002/nur.20362 – volume: 40 start-page: 23 issue: 1 year: 2017 ident: 3857_CR23 publication-title: Research in Nursing & Health doi: 10.1002/nur.21768 – ident: 3857_CR42 – volume: 36 start-page: 2571 issue: 9 year: 2021 ident: 3857_CR13 publication-title: Journal of General Internal Medicine doi: 10.1007/s11606-020-06432-7 – volume: 25 start-page: 9 issue: 1 year: 2021 ident: 3857_CR34 publication-title: Maternal and Child Health Journal doi: 10.1007/s10995-020-03020-3
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Snippet	Introduction Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to... Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to identify bias... Introduction Stigma and bias related to race and other minoritized statuses may underlie disparities in pregnancy and birth outcomes. One emerging method to...
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SubjectTerms	Algorithms Analysis Bias Childrens health Computational linguistics Decision trees Deep learning Demographic aspects Electronic health records Gynecology Health aspects Health disparities Health problems Informatics Investment bankers Language Language processing Machine learning Maternal & child health Maternal and Child Health Maternal health services Medical records Medicine Medicine & Public Health Natural language interfaces Natural language processing Pediatrics Performance evaluation Population Economics Power Public Health Sociology Stigma Stigma (Social psychology) Support vector machines
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Title	Using Natural Language Processing to Identify Stigmatizing Language in Labor and Birth Clinical Notes
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