Publications

Kim, Y., Heider, P. M., Lally, I. R., & Meystre, S. M. (2021). A Hybrid Model for Family History Information Identification and Relation Extraction: Development and Evaluation of an End-to-End Information Extraction System. JMIR Medical Informatics, 9(4), e22797. https://doi.org/10.2196/22797
Meystre, S. M., Gouripeddi, R., Harper, J., & Talbert, J. (2021, March). Lessons Learned from Healthcare Organizations Contributing Clinical Data to the National COVID Cohort Collaborative (N3C). AMIA Summits Transl Sci Proc.
Meystre, S. M., Heider, P., & Kim, Y. (2021, March). COVID-19 Diagnostic Testing Prediction Using Natural Language Processing to Power a Data-Driven Symptom Checker. AMIA Summits Transl Sci Proc.
Bennett, T. D., Moffitt, R. A., Hajagos, J. G., Amor, B., Anand, A., Bissell, M. M., Bradwell, K. R., Bremer, C., Byrd, J. B., Denham, A., DeWitt, P. E., Gabriel, D., Garibaldi, B. T., Girvin, A. T., Guinney, J., Hill, E. L., Hong, S. S., Jimenez, H., Kavuluru, R., … N3C Consortium (including Meystre, Stephane). (2021). The National COVID Cohort Collaborative: Clinical Characterization and Early Severity Prediction. https://doi.org/10.1101/2021.01.12.21249511
Meystre, S. M., Kim, Y., & Heider, P. (2020, November). COVID-19 Information Extraction Rapid Deployment Using Natural Language Processing and Machine Learning. AMIA NLP WG Pre-Symposium.
Haendel, M., Chute, C., Gersing, K., & Consortium authors (including Stephane Meystre). (2020). The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment. J Am Med Inform Assoc, ocaa196. https://doi.org/10.1093/jamia/ocaa196
Ford, D., Harvey, J., McElligott, J., King, K., Simpson, K., Valenta, S., Warr, E., Walsh, T., Debenham, E., Teasdale, C., Meystre, S., Obeid, J. S., & Lenert, L. (2020). Leveraging Health System Telehealth and Informatics Infrastructure to Create a Continuum of Services for COVID-19 Screening, Testing, and Treatment. J Am Med Inform Assoc, 27(12), 1871–1877. https://doi.org/10.1093/jamia/ocaa157
Obeid, J. S., Davis, M., Turner, M., Meystre, S. M., Heider, P., & Lenert, L. (2020). An AI approach to COVID-19 infection risk assessment in virtual visits: a case report. J Am Med Inform Assoc. https://doi.org/https://doi.org/10.1093/jamia/ocaa105
Heider, P., Obeid, J. S., & Meystre, S. (2020). A Comparative Analysis of Speed and Accuracy for Three Off-the-Shelf De-Identification Tools. AMIA Jt Summits Transl Sci Proc, 241–250. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7233098/
Heider, P. M., Weng, C., & Meystre, S. (2020). Evaluation of a Study Cohort Query Formulation Tool: Criteria2Query. AMIA Jt Summits Transl Sci Proc, 911.
Kim, Y., & Meystre, S. (2020). A Study of Allergen Extraction from Electronic Health Record Narratives. AMIA Summits Transl Sci Proc, 804–805.
Meystre, S., Trice, A., Kim, Y., & Heider, P. (2020). Comparing Concept Normalization Accuracy and Speed for Medical Problems and Medication Allergies. AMIA Summits Transl Sci Proc, 818–819.
Kim, Y., & Meystre, S. M. (2020). Ensemble method-based extraction of medication and related information from clinical texts. J Am Med Inform Assoc, 27(1), 31–38. https://doi.org/10.1093/jamia/ocz100
Meystre, S., Petkov, V., Silverstein, J., Savova, G., & Malin, B. (2020). De-Identification of Clinical Text: Stakeholders’ Perspectives and Acceptance of Automatic De-Identification. AMIA Annu Symp Proc, 124–126.
Kim, Y., Heider, P., & Meystre, S. (2020). Comparative Study of Various Approaches for Ensemble-based De-identification of Electronic Health Record Narratives. AMIA Annu Symp Proc, 648–657. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075417/
Meystre, S. (2020). CliniDeID for Clinical Text De-Identification. AMIA Annu Symp Proc.
Kim, Y., & Meystre, S. (2020). Improving De-identification of Clinical Text with Contextualized Embeddings. AMIA Annu Symp Proc, 1813.
Heider, P., Kim, Y., & Meystre, S. (2020). A Meta-Analysis of Medical Concept Normalization Using Hierarchical Ontological Relations and Semantic Types. AMIA Annu Symp Proc, 1786.
Underwood, G., Trice, A., Kim, Y., Accetta, J.-K., & Meystre, S. (2019). Text De-Identification Impact on Subsequent Machine Learning Applications. AMIA Annu Symp Proc, 1795.
Heider, P. M., & Meystre, S. M. (2019). Targeted Terminology Generation Tool for Natural Language Processing Applications. AMIA NLP WG Pre-Symposium, 2.
Kim, Y., & Meystre, S. (2019, November). A Hybrid Model for Entity Identification and Relation Classification of Family History Information. N2c2 Shared Task and Workshop.
Kim, Y., Heider, P., & Meystre, S. M. (2019). Multistage Medical Concept Normalization for Clinical Narrative Text. N2c2 Shared Task and Workshop, 2.
Kim, Y., & Meystre, S. M. (2019). Cancer Type Classification by Jointly Using Words and Concepts from Electronic Health Record Text Notes. AMIA Annu Symp Proc, 1632.
Meystre, S. M., Heider, P. M., Kim, Y., Aruch, D. B., & Britten, C. D. (2019). Automatic trial eligibility surveillance based on unstructured clinical data. International Journal of Medical Informatics, 129, 13–19. https://doi.org/https://doi.org/10.1016/j.ijmedinf.2019.05.018
Heider, P., & Meystre, S. M. (2019, August 29). A Corpus Munging Tool for Profiling Approaches to Sentence Boundary Detection. Medinfo 2019, Lyon, France.
Heider, P. M., & Meystre, S. M. (2019). Patient-Pivoted Automated Trial Eligibility Pipeline: The First of Three Phases in a Modular Architecture. Studies in Health Technology and Informatics, 264, 1476–1477. https://doi.org/10.3233/SHTI190492
Kim, Y., & Meystre, S. M. (2019). A Study of Medical Problem Extraction for Better Disease Management. Studies in Health Technology and Informatics, 264, 193–197. https://doi.org/10.3233/SHTI190210
Obeid, J. S., Heider, P. M., Weeda, E. R., Matuskowitz, A. J., Carr, C. M., Gagnon, K., Crawford, T., & Meystre, S. M. (2019). Impact of De-Identification on Clinical Text Classification Using Traditional and Deep Learning Classifiers. Studies in Health Technology and Informatics, 264, 283–287. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6779034/
Heider, P., Accetta, J.-K., & Meystre, S. M. (2019, August). ETUDE: Demonstrating Multiple Matching Styles and Offset Calculators within a Natural Language Processing Evaluation Tool. Medinfo 2019.
Meystre, S. M., Heider, P., Kim, Y., Aruch, D., & Britten, C. (2019). Automatic Trial Eligibility Surveillance: Pilot Study Focused on Breast Cancer. AMIA Joint Summits on Translational Science Proceedings, 946–947.
Kim, Y., & Meystre, S. M. (2019). Voting Ensemble Pruning for De-identification of Electronic Health Records. AMIA Joint Summits on Translational Science Proceedings, 1083.
Meystre, S. M., & Gouripeddi, R. (2019). Clinical Research in the Postgenomic Era. In R. L. Richesson & J. E. Andrews (Eds.), Clinical Research Informatics (pp. 147–168). Springer.
Kim, Y., Heider, P., & Meystre, S. M. (2018). Ensemble-based Methods to Improve De-identification of Electronic Health Record Narratives. AMIA Annu Symp Proc, 663–672. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6371277/
Kim, Y., & Meystre, S. M. (2018, November). Ensemble Method-based Extraction of Medication and Related Information from Clinical Texts. N2c2 Shared Task and Workshop.
Heider, P., Accetta, J.-K., & Meystre, S. M. (2018, November). ETUDE for Easy and Efficient NLP Application Evaluation. AMIA NLP-WG Pre-Symposium.
Meystre, S., Carrell, D., Hirschman, L., Aberdeen, J., Fearn, P., Petkov, V., & Silverstein, J. C. (2018). Automatic Text De-Identification: How and When is it Acceptable? AMIA Annu Symp Proc, 124–126. blob:https://knowledge.amia.org/b87966c4-de92-4c73-9568-36fea7e4b65c
Heider, P., Kim, Y., AAlAbdulsalam, A. K., Kim, C., & Meystre, S. M. (2018). Hybrid Approaches for Automated Clinical Trial Cohort Selection. N2c2 Shared Task and Workshop, 1–2.
Meystre, S., Heider, P., Heider, Kim, Y., Trice, A., & Underwood, G. (2018). Clinical Text Automatic De-Identification to Support Large Scale Data Reuse and Sharing: Pilot Results. AMIA Annu Symp Proc, 2069. blob:https://knowledge.amia.org/266243dd-77fe-4c70-95d3-c5da534cc0af
Meystre, S. M., Lovis, C., Parra Calderon, C. L., E, W., J, M. A., & Obeid, J. S. (2018). Artificial Intelligence Applications Enabling Clinical Decision Support. EFMI STC, 1–3.
Carter, M., AAlAbdulsalam, A. K., Herget, K., McFadden, S., Garvin, J. H., Redd, A., Sweeney, C., & Meystre, S. M. (2018, June). Automated extraction and assignment of TNM stage to support cancer case consolidation. NAACCR Annual Conference.
AAlAbdulsalam, A. K., Garvin, J. H., Redd, A., Carter, M., Sweeney, C., & Meystre, S. M. (2018). Automated Extraction and Classification of AJCC TNM Stage Mentions from Unstructured Text Fields in a Central Cancer Registry. AMIA Joint Summits on Translational Science Proceedings, 16–25.
Garvin, J. H., Kim, Y., Gobbel, G. T., Matheny, M. E., Redd, A., Bray, B. E., Heidenreich, P., Bolton, D., Heavirland, J., Kelly, N., Reeves, R., Kalsy, M., Goldstein, M. K., & Meystre, S. M. (2018). Automating Quality Measures for Heart Failure Using Natural Language Processing: A Descriptive Study in the Department of Veterans Affairs. JMIR Medical Informatics, 6(1), e5. https://doi.org/10.2196/medinform.9150
Zhu, V., Walker, T. D., Warren, R. W., Jenny, P. B., Meystre, S. M., & Lenert, L. A. (2017). Identifying Falls Risk Screenings Not Documented with Administrative Codes Using Natural Language Processing. AMIA Annu Symp Proc, 1906–1913.
Kim, Y., Riloff, E., & Meystre, S. M. (2017). Exploiting Unlabeled Texts with Clustering-based Instance Selection for Medical Relation Classification. AMIA Annu Symp Proc, 1060–1069.
Garvin, J. H., Kim, Y., Heidenreich, P., Nativi-Nicolau, J., Inampudi, C., Zeng-Treitler, Q., Gobbel, G. T., & Meystre, S. M. (2017). Health Informatics Improves Heart Failure Data Capture for Quality Measures, Research and Decision Support in VA. Journal of Cardiac Failure, 23(8), S113.
Meystre, S. M., & Doing-Harris, K. (2017). Semi-automated Ontology Development and Management System Applied to Medically Unexplained Syndromes in the U.S. Veterans Population. In Artificial Intelligence in Medicine (pp. 345–350). Springer, Cham. https://doi.org/10.1007/978-3-319-59758-4_41
Meystre, S., Gouripeddi, R., Tieder, J., Simmons, J., Srivastava, R., & Shah, S. (2017). Enhancing Comparative Effectiveness Research With Automated Pediatric Pneumonia Detection in a Multi-Institutional Clinical Repository: A PHIS+ Pilot Study. Journal of Medical Internet Research, 19(5), e162. https://doi.org/10.2196/jmir.6887
Meystre, S. M., Lovis, C., Bürkle, T., Tognola, G., Budrionis, A., & Lehmann, C. U. (2017). Clinical Data Reuse or Secondary Use: Current Status and Potential Future Progress. Yearbook of Medical Informatics, 26(1). https://doi.org/10.15265/IY-2017-007
Meystre, S. M., Kim, Y., Gobbel, G. T., Matheny, M. E., Redd, A., Bray, B. E., & Garvin, J. H. (2017). Congestive heart failure information extraction framework for automated treatment performance measures assessment. J Am Med Inform Assoc, 24(e1), e40–e46. https://doi.org/10.1093/jamia/ocw097
Kim, Y., Garvin, J. H., Goldstein, M. K., Hwang, T. S., Redd, A., Bolton, D., Heidenreich, P. A., & Meystre, S. M. (2017). Extraction of Left Ventricular Ejection Fraction Information from Various Types of Clinical Reports. J Biomed Inform, 67, 42–48. https://doi.org/10.1016/j.jbi.2017.01.017

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