Use of Natural Language Processing to Extract Information from Clinical Text
- June 15, 2017
- June 15, 2017
FDA, UCSF-Stanford CERSI, and San Francisco State University Collaborative Workshop
June 15, 2017
Goals and Objectives:
The objective of this workshop was to identify current and emerging natural language processing (NLP) efforts being applied to unstructured text such as clinical notes or narratives in electronic health records (EHRs). The workshop provided insights into utility and challenges in designing and implementing NLP systems to capture relevant or missing information from clinical notes or text for conducting postmarketing safety surveillance and informing the design and execution of clinical trials for medical products, which include drugs, biologics, and devices. The workshop included panel discussion sessions to provide stakeholders with a forum to discuss natural language processing with experts in the field.
The workshop focused on whether NLP can be applied to unstructured text in clinical notes to:
- Identify indication or reason for medical product use, adverse outcomes or events associated with use of these products, and confounders or personal behaviors that may modify risks associated with use of these products
- Support protocol design, feasibility, recruitment efforts and execution of clinical trials
Agenda and Workshop Summary:
For information on the agenda and workshop summary, see the UCSF-Stanford CERSI workshop website.
Food and Drug Administration
White Oak Campus
The Great Room (1503 B&C)
10903 New Hampshire Ave.
Silver Spring, MD 20993