Meystre, S., & Heider, P. (2024). High Accuracy Open-Source Clinical Data De-Identification: The CliniDeID Solution. In J. Bichel-Findlay, P. Otero, P. Scott, & E. Huesing (Eds.), Studies in Health Technology and Informatics. IOS Press. https://doi.org/10.3233/SHTI231199

Heider, P. M., & Meystre, S. M. (2023). An Open Evaluation Framework for NLP Systems Applied to a Selection of Clinical Text De-Identification Tools and Corpora (Preprint) [Preprint]. Journal of Medical Internet Research. https://doi.org/10.2196/preprints.55676

Meystre, S., Wang, Y., Roberts, K., Tseng, V., & Hersh, W. R. (2023). Generative Applications of Large Language Models for Medical Education and Knowledge Searching: Shall We Count on ChatGPT and Co? AMIA Annu Symp Proc, 50–52.

Meystre, S., Underwood, G., & Heider, P. (2023). Clinical Text De-identification with a Free and Open-Source Tool (CliniDeID). AMIA Annu Symp Proc, 1844.

Meystre, S., Van Stiphout, R., Goris, A., & Gaitan, S. (2023). AI-Based Gut-Brain Axis Digital Twins. Studies in Health Technology and Informatics, 302, 1007–1008. https://doi.org/10.3233/SHTI230327

Meystre, S. M., Underwood, G., & Heider, P. (2023, May). CliniDeID, an Open Source Solution for Accurate Clinical Text De-Identification. Medical Informatics Europe (MIE) 2023.

Meystre, S. M., Parra Calderon, C. L., Newman-Griffis, D., & Bellazzi, R. (2023, May). Responsible Artificial Intelligence: A Need for Healthcare Applications. Medical Informatics Europe (MIE) 2023.

Meystre, S. M., Heider, P. M., Cates, A., Bastian, G., Pittman, T., Gentilin, S., & Kelechi, T. J. (2023). Piloting an automated clinical trial eligibility surveillance and provider alert system based on artificial intelligence and standard data models. BMC Medical Research Methodology, 23(1), 88. https://doi.org/10.1186/s12874-023-01916-6

Meystre, S. M., Gouripeddi, R., & Alekseyenko, A. (2023). Molecular, Genetic, and Other Omics Data. In R. L. Richesson, J. E. Andrews, & K. Fultz (Eds.), Clinical Research Informatics (3rd edition, pp. 309–328). Springer.

Heider, P., Chen, K., Pipaliya, R., & Meystre, S. M. (2022). Post-Hoc Ensemble Generation for Clinical NLP: A Study of Concept Recognition, Normalization, and Context Attributes. AMIA Annu Symp Proc, 747.

Meystre, S. M. (2022, October). AI-Enabled Digital Twins Along the Gut-Brain Axis. BioIT World Conference and Expo, Berlin, Germany. https://www.bio-itworldeurope.com/bioinformatics

Meystre, S. M., Heider, P. M., Kim, Y., Davis, M., Obeid, J., Madory, J., & Alekseyenko, A. V. (2022). Natural Language Processing Enabling COVID-19 Predictive Analytics to Support Data-Driven Patient Advising and Pooled Testing. Journal of the American Medical Informatics Association: JAMIA, 29(1), 12–21. https://doi.org/10.1093/jamia/ocab186

Kim, Y., Heider, P. M., & Meystre, S. M. (2021). Clinical Concept Extraction Using Contextual String Embeddings. AMIA Annu Symp Proc, 1428–1429.

Heider, P. M., & Meystre, S. M. (2021). Overview and Descriptive Analysis of a New Ontology for Normalizing Section Types in Unstructured Clinical Notes. AMIA Annu Symp Proc, 1678.

Heider, P. M., & Meystre, S. M. (2021). Evaluating the Downstream Performance Impact of Various Common Off-the-Shelf Clinical NLP Components. AMIA Annu Symp Proc, 1679.

Alekseyenko, A. V., Hamidi, B., & Meystre, S. (2021). Colonizing Microbiome as a Determinant of COVID-19 Outcome: A Pilot Study. AMIA Annu Symp Proc, 1590.

Meystre, S., Heider, P. M., Obeid, J., Alekseyenko, A., & Madory, J. (2021). Natural Language Processing and COVID-19 Predictive Analytics to Enable and Optimize SARS-CoV-2 Pooled Testing. AMIA Annu Symp Proc, 1751.

Pipaliya, R., Heider, P., & Meystre, S. M. (2021). Comparing Multiple Models for Section Header Classification with Feature Evaluation. Medinfo 2021, 303.

Heider, P., Pipaliya, R., & Meystre, S. M. (2021). A natural language processing tool offering data extraction for COVID-19 related information (DECOVRI). Medinfo 2021, 249.

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., … National COVID Cohort Collaborative (N3C) Consortium. (2021). Clinical Characterization and Prediction of Clinical Severity of SARS-CoV-2 Infection Among US Adults Using Data From the US National COVID Cohort Collaborative. JAMA Network Open, 4(7), e2116901. https://doi.org/10.1001/jamanetworkopen.2021.16901

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

Haendel, M., Chute, C., Gersing, K., & Consortium authors (including Stephane Meystre). (2021). The National COVID Cohort Collaborative (N3C): Rationale, Design, Infrastructure, and Deployment. J Am Med Inform Assoc, 28(3), 427–443. https://doi.org/10.1093/jamia/ocaa196

Meystre, S. M., Heider, P., & Kim, Y. (2021). COVID-19 Diagnostic Testing Prediction Using Natural Language Processing to Power a Data-Driven Symptom Checker. AMIA Summits Transl Sci Proc, 745–746.

Meystre, S. M., Gouripeddi, R., Harper, J., & Talbert, J. (2021). Lessons Learned from Healthcare Organizations Contributing Clinical Data to the National COVID Cohort Collaborative (N3C). AMIA Summits Transl Sci Proc, 47–49.

Kalsy, M., Nelly, N., Meystre, S. M., Kim, Y., Bray, B., Bolton, D., Goldstein, M., & Garvin, J. H. (2021). Assessing Organizational Context for Implementation. AMIA Summits Transl Sci Proc, 832.

Heider, P., Kim, Y., & Meystre, S. M. (2021). Semi-Automated Corpus Augmentation Methods for Enriching Lab Name Annotations with Lab Value Annotations. AMIA Summits Transl Sci Proc, 826.

Heider, P., Underwood, G., & Meystre, S. M. (2021). Resynthesizing MIMIC-III Personally Identifiable Information Tags to Increase Corpus Utility:  Process and Impact Assessment. AMIA Summits Transl Sci Proc, 825.

Kim, Y., Heider, P., & Meystre, S. (2021). Automated Category Alignment Applied to Different De-identification Annotation Schemata. AMIA Summits Transl Sci Proc, 836.

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.

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.

Kalsy, M., Kelly, N., Meystre, S. M., Kim, Y., Bray, B. E., Bolton, D., Goldstein, M. K., & Garvin, J. H. (2020). Stakeholder Engagement for a Planned Automated Quality Measurement System. SAGE Open, 10(2), 215824402091945. https://doi.org/10.1177/2158244020919459

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