Applications of natural language processing in clinical research and practice

Yanshan Wang, Ahmad P. Tafti, Sunghwan Sohn, Rui Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations
Original languageEnglish (US)
Title of host publicationNAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics
Subtitle of host publicationHuman Language Technologies - Tutorial Abstracts
PublisherAssociation for Computational Linguistics (ACL)
Number of pages4
ISBN (Electronic)9781950737178
StatePublished - 2019
Event2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019 - Minneapolis, United States
Duration: Jun 2 2019 → …

Publication series

NameNAACL HLT 2019 - 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies - Tutorial Abstracts


Conference2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL HLT 2019
Country/TerritoryUnited States
Period6/2/19 → …

Bibliographical note

Funding Information:
This tutorial has been made possible by partial support from the National Center for Advancing Translational Sciences (NCATS) Open Health Natural Language Processing (OHNLP) Consortium (U01TR002062).

Funding Information:
Yanshan Wang is a Research Associate at Mayo Clinic. His current work is centered on developing novel NLP and artificial intelligence (AI) methodologies for facilitating clinical research and solving real-world clinical problems. Since he joined Mayo Clinic in 2015, he has been leading several NIH-funded projects, which aims to leverage and develop novel NLP techniques to automatically retrieve cohorts from clinical data repository using free-text EHR data. Dr. Wang has extensive collaborative research experience with physicians, epidemiology researchers, statisticians, NLP researchers, and IT technicians. He collaborated with rheumatologists and developed a NLP system for automatic identification of skeletal site-specific fractures from radiology reports for osteoporosis patients. He has had ongoing collaboration with epidemiologists and clinical neurologists on developing novel AI solutions to provide better care for elders. Dr. Wang has published over 40 peer-reviewed articles at referred computational linguistic conferences (e.g., NAACL), and medical informatics journals and conference (e.g., JBI, JAMIA, JMIR and AMIA). He has served on program committees for EMNLP, NAACL, IEEE-ICHI, IEEE-BIBM, etc. ([email protected]) Ahmad P. Tafti is a Research Associate at Mayo Clinic, with a deep passion for improving health informatics using diverse medical data sources combined with advanced computational methods. Dr. Tafti’s major interests are AI, machine learning, and computational health informatics. He completed his PhD in Computer Science at University of Wisconsin-Milwaukee, and some part of his international studies were carried out at Oracle Education Center, Technical University of Vienna, and Medical University of Vienna, Austria. He won the General Electric Honorable Mention Award and received the 3rd place in the Larry Hause Student Poster Competition at an IEEE conference as part of his PhD project. Dr. Tafti has published over 20 first-author peer-reviewed publications in prestigious journals and conferences (e.g., CVPR, AMIA, ISVC, JMIR, PLOS, IEEE Big Data), addressing medical text and medical image analysis and understanding using advanced computational strategies. In addition, Dr. Tafti has served as a workshop organizer, steering committee member, technical reviewer, and a program committee member for several reputable conferences and journals, including KDD 2017, AMIA, IEEE ICHI, ISMCO, ISVC, IEEE Journal of Biomedical and Health Informatics, and International Journal of Computer Vision and Image Processing. He was awarded a NVIDIA GPU Grant for his accomplishments in deep learning community. ([email protected]) Sunghwan Sohn is an Associate Professor of Biomedical Informatics at Mayo Clinic. He has expertise in mining large-scale EHRs to unlock unstructured and hidden information using natural language processing and machine learning, thus creating new capacities for clinical research and practice in order to achieve better patient solutions. He has been involved in the development of cTAKES, the most popular NLP tool in the clinical domain. Dr. Sohns research facilitates the best use of EHRs to solve clinical problems and improve public health. His work provides biomedical scientists and clinicians access to unstructured information from clinical narratives and clinical text analytics necessary for clinical research and patient care. Dr. Sohns research goal is the best utilization of informatics to facilitate translational research and precision medicine across heterogeneous EHR data and systems in a large population. ([email protected]) Rui Zhang is an Assistant Professor in the College of Pharmacy and the Institute for Health Informatics (IHI), and also graduate faculty in Data Science at the University of Minnesota (UMN). He is the Leader of NLP Services in Clinical and Transnational Science Institution (CTSI) at the UMN. Dr. Zhangs research focuses on health and biomedical informatics, especially biomedical NLP and text mining. His research interests include the secondly analysis of EHR data for patient care as well as pharmacovigilance knowledge discovery through mining biomedical literature. His researcher program is funded by federal agencies with over 3.5 million dollars including National Institutes of Health, the Agency for Health and Research Quality (AHRQ), and a medical device industry - Medtronic Inc. He also a co-investigator of a 42.6 million of CTSI grant. His work has been recognized on a national scale including Journal of Biomedical Informatics Editors Choice, nominated for Distinguished paper in AMIA Annual Symposium and Marco Ra-moni Distinguished Paper Award for Translational Bioinformatics, as well as highlighted by The Wall Street Journal. ([email protected])

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