The NIH National Institute for General Medical Sciences (NIGMS) has awarded a new technology transfer grant to Clinacuity with a focus on clinical data de-identification. This large effort will be realized in collaboration with our...
Paul Heider and Stephane Meystre presented multiple publications at the international Medinfo 2019 conference in Lyon, France: Kim, Y., & Meystre, S. M. (2019). A Study of Medical Problem Extraction for Better Disease Management. Studies...
This new research contract will support collaborative efforts between MUSC and Vanderbilt University (Glenn Gobbel and his team). Our general objective is to develop and assess new Natural Language Processing (NLP) methods to improve the...
Youngjun Kim and Stephane Meystre. J Am Med Inform Assoc. 2019. Available at https://doi.org/10.1093/jamia/ocz100
Clinical Information Abstractor Powering a Dynamic Patient Clinical Summary Dashboard. To provide clinicians with a complete and timely summary of all key patient information, updated in real time, we propose a Clinical Information...
Stéphane M. Meystre, Paul M.Heider, Youngjun Kim, Daniel B. Aruch, Carolyn D.Britten. Int J Med Inform. 2019. Article available at: https://doi.org/10.1016/j.ijmedinf.2019.05.018
The National Cancer Institute (NCI) has awarded a new grant (Phase II STTR) for technology transfer efforts focused on “Automated Problem and Allergy Lists Enrichment Based on High Accuracy Information Extraction from the Electronic...
The American College of Medical Informatics (ACMI) elected Stephane Meystre as new Fellow this year, a great honor introduced with humour during the annual ACMI reception in November 2016.
The American Medical Informatics Association features a member they think represents a good example for their membership, as “faces of AMIA“, and in October 2016, it was Stephane Meystre (detailed article here).
Meystre, S. M., Kim, Y., Gobbel, G. T., Matheny, M. E., Redd, A., Bray, B. E., & Garvin, J. H. (2016). Congestive heart failure information extraction framework for automated treatment performance measures assessment. J Am Med Inform...
The National Institute for General Medical Sciences (NIGMS) awarded a technology transfer grant to Clinacuity, Inc., for Clinical Text Automatic De- Identification to Support Large Scale Data Reuse and Sharing. This project will be realized in...
The VentureWrench project focuses on the development and evaluation of an innovative web semantic search engine for entrepreneurship web resources. It includes a natural language processing and machine learning system that analyzes the content...
Khalifa A, Meystre SM (2015). Adapting Existing Natural Language Processing Resources for Cardiovascular Risk Factors Identification in Clinical Notes. J Biomed Inform, 58(S), 128-132.
Meystre SM (2015). De-identification of Unstructured Clinical Data for Patient Privacy Protection. In Gkoulalas-Divanis A, Loukides G (Eds.), Medical Data Privacy Handbook (pp. 697-716). Springer.
Peter Szolovits, John Aberdeen, Stephane Meystre, Mehmet Kayaalp (2015). State of the Art of Clinical Narrative Report De-Identification and Its Future. AMIA Annu Symp Proc, San Francisco, CA.
Kim,Y, Garvin J, Heavirland J, Meystre S (2015). Improving Detection of Reasons Not to Take a Medication by Leveraging Medication Prescription Status. AMIA Annu Symp Proc, San Francisco, CA, 1528.
This study was realized in the context of the ADAHF (Automated Data Acquisition for Heart Failure) project, a U.S. Department of Veterans Administration (VA) project that aims to automatically extract data for heart failure treatment...
Meystre,SM, Lovis,C, Prokosch,HU, Mykkanen J, Hripcsak,G, Burkle,T, Lehmann,C (2015). eHealth-Enabled Clinical Data Reuse. Medinfo 2015, São Paulo, Brazil. Workshop website
Meystre,S.M., Kim,Y., Heavirland,J., Williams,J., Bray,B.E., Garvin,J.H (2015). Heart Failure Medications Detection and Prescription Status Classification in Clinical Narrative Documents. Stud Health Technol Inform, 216, 609-13. Download
Kim,Y., Garvin,J., Goldstein,M.K., Meystre,S.M (2015). Classification of Contextual Use of Left Ventricular Ejection Fraction Assessments. Stud Health Technol Inform, 216, 599-603. Download