Natural Language Processing

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Zolnoori, M., Zolnour, A., Vergez, S., Sridharan, S., Spens, I., Topaz, M., Noble, J. M., Bakken, S., Hirschberg, J., Bowles, K., Onorato, N., & McDonald, M. V. (2024). Beyond electronic health record data: leveraging natural language processing and machine learning to uncover cognitive insights from patient-nurse verbal communications. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocae300
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Bear Don’t Walk, O. J., Pichon, A., Reyes Nieva, H., Sun, T., Li, J., Joseph, J., Kinberg, S., Richter, L. R., Crusco, S., Kulas, K., Ahmed, S. A., Snyder, D., Rahbari, A., Ranard, B. L., Juneja, P., Demner-Fushman, D., & Elhadad, N. (2024). Contextualized race and ethnicity annotations for clinical text from MIMIC-III. Scientific Data, 11(1). https://doi.org/10.1038/s41597-024-04183-2
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Scroggins, J. K., Hulchafo, I. I., Harkins, S., Scharp, D., Moen, H., Davoudi, A., Cato, K., Tadiello, M., Topaz, M., & Barcelona, V. (2024). Identifying stigmatizing and positive/preferred language in obstetric clinical notes using natural language processing. Journal of the American Medical Informatics Association. https://doi.org/10.1093/jamia/ocae290
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Santomartino, S. M., Zech, J. R., Hall, K., Jeudy, J., Parekh, V., Yi, P. H., & Weintraub, E. (2024). Evaluating the Performance and Bias of Natural Language Processing Tools in Labeling Chest Radiograph Reports. Radiology, 313(1). https://doi.org/10.1148/radiol.232746
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Odlum, M., Moon, S., Broadwell, P., Huang, N., Sun, F., Tipani, D., Davis, N., Kim, M., & Yoon, S. (2024). Using Natural Language Processing on Expert Panel Discussions to Gain Insights for Recruitment, Retention and Intervention Adherence for Online Social Support Interventions on a Stage II-III Clinical Trial Among Hispanic and African American Dementia Care. Digital Health and Informatics Innovations for Sustainable Health Care Systems. https://doi.org/10.3233/shti240405
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Landau, A. Y., Blanchard, A., Kulkarni, P., Althobaiti, S., Idnay, B., Patton, D. U., Cato, K., & Topaz, M. (2024). Harnessing the Power of Machine Learning and Electronic Health Records to Support Child Abuse and Neglect Identification in Emergency Department Settings. Digital Health and Informatics Innovations for Sustainable Health Care Systems. https://doi.org/10.3233/shti240740
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Song, J., Topaz, M., Landau, A. Y., Klitzman, R. L., Shang, J., Stone, P. W., McDonald, M. V., & Cohen, B. (2024). Natural Language Processing to Identify Home Health Care Patients at Risk for Becoming Incapacitated With No Evident Advance Directives or Surrogates. Journal of the American Medical Directors Association, 25(8), 105019. https://doi.org/10.1016/j.jamda.2024.105019
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Barcelona, V., Scharp, D., Moen, H., Davoudi, A., Idnay, B. R., Cato, K., & Topaz, M. (2023). Using Natural Language Processing to Identify Stigmatizing Language in Labor and Birth Clinical Notes. Maternal and Child Health Journal, 28(3), 578–586. https://doi.org/10.1007/s10995-023-03857-4
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Hobensack, M., Song, J., Oh, S., Evans, L., Davoudi, A., Bowles, K. H., McDonald, M. V., Barrón, Y., Sridharan, S., Wallace, A. S., & Topaz, M. (2023). Social Risk Factors are Associated with Risk for Hospitalization in Home Health Care: A Natural Language Processing Study. Journal of the American Medical Directors Association, 24(12), 1874-1880.e4. https://doi.org/10.1016/j.jamda.2023.06.031
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Scharp, D., Hobensack, M., Davoudi, A., & Topaz, M. (2024). Natural Language Processing Applied to Clinical Documentation in Post-acute Care Settings: A Scoping Review. Journal of the American Medical Directors Association, 25(1), 69–83. https://doi.org/10.1016/j.jamda.2023.09.006
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Song, J., Zolnoori, M., Scharp, D., Vergez, S., McDonald, M. V., Sridharan, S., Kostic, Z., & Topaz, M. (2023). Is Auto-generated Transcript of Patient-Nurse Communication Ready to Use for Identifying the Risk for Hospitalizations or Emergency Department Visits in Home Health Care? A Natural Language Processing Pilot Study. AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022, 992–1001.

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Idnay, B., Fang, Y., Dreisbach, C., Marder, K., Weng, C., & Schnall, R. (2023). Clinical research staff perceptions on a natural language processing-driven tool for eligibility prescreening: An iterative usability assessment. International Journal of Medical Informatics, 171, 104985. https://doi.org/10.1016/j.ijmedinf.2023.104985
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Liu, C., Ta, C. N., Havrilla, J. M., Nestor, J. G., Spotnitz, M. E., Geneslaw, A. S., Hu, Y., Chung, W. K., Wang, K., & Weng, C. (2022). OARD: Open annotations for rare diseases and their phenotypes based on real-world data. The American Journal of Human Genetics, 109(9), 1591–1604. https://doi.org/10.1016/j.ajhg.2022.08.002
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Song, J., Topaz, M., Landau, A. Y., Klitzman, R., Shang, J., Stone, P., McDonald, M., & Cohen, B. (2022). Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers. PLOS ONE, 17(7), e0270220. https://doi.org/10.1371/journal.pone.0270220
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Chen, Z., Liu, H., Liao, S., Bernard, M., Kang, T., Stewart, L. A., & Weng, C. (2022). Representation and Normalization of Complex Interventions for Evidence Computing. MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation. https://doi.org/10.3233/shti220146
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Senathirajah, Y., Cho, H., Fawcett, J., Mondejar, K. M., Cato, K., Broadwell, P., & Yoon, S. (2022). Application of Natural Language Processing to Learn Insights on the Clinician’s Lived Experience of Electronic Health Records. Informatics and Technology in Clinical Care and Public Health. https://doi.org/10.3233/shti210864
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