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 natur...
| Publicado en: | Maternal & Child Health Journal Vol. 28; no. 3; pp. 578 - 587 |
|---|---|
| Autores principales: | , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Springer Nature
Mar2024
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=175846869&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 175846869 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10927875 N9J jtl: Maternal & Child Health Journal issn: 10927875 maglogo: N pubinfo: dt: Mar2024 vid: 28 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 175846869 174436135 175846869 175846869 10.1007/s10995-023-03857-4 175846869 ppf: 578 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Using Natural Language Processing to Identify Stigmatizing Language in Labor and Birth Clinical Notes. aug: au: Barcelona, Veronica Scharp, Danielle Moen, Hans Davoudi, Anahita Idnay, Betina R. Cato, Kenrick Topaz, Maxim affil: https://ror.org/00hj8s172 School of Nursing, Columbia University, 560 West 168th St, Mail Code 6, 10032, New York, NY, USA sug: subj: Maternal Health Services Patient Abuse Diagnosis Natural Language Processing Utilization Machine Learning Utilization Stigma Language Electronic Health Records Data Mining Methods Human Secondary Analysis Record Review Qualitative Studies Descriptive Research Thematic Analysis Algorithms Funding Source ab: 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. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|