CoMeT | <%=title %>
Collaborative Management of Talks Hello! sign in or register
Bookmark Talks, Share with Friends, and We Recommend More!
Advanced Search
Home
Calendar
Series
Speaker
Groups
Connections
 
 
 
 
Talk Detail
Posted: ISP Admin on  Oct 11 03:32:38 PM
Title: Towards Interactive NLP in clinical environments  
Speaker:
Gaurav Trivedi
Intelligent Systems Program
Sponsor: University of Pittsburgh  >  School of Computing and Information  >  Intelligent Systems Program
Series: ISP Colloquium Series
Date: Oct 20, 2017 1:00 PM - 1:30 PM
URL:
Location: 5317 Sennott Square
Groups Posted: Computational/Systems Biology, Bioinformatics Intelligent Systems Program 
Detail:

Narratives and first-person stories allow clinicians to easily document rich information about the patient and their care progress in free-text reports. However, they can get bloated quickly making it hard to browse through them and find relevant information. Using Natural Language Processing (NLP) methods to address this problem is challenging as contextual examples are required to train models for every different use case. Clinicians who can understand medical jargon and create training examples are usually not versed in informatics techniques to be able to use NLP directly. Interactive tools supporting the review and revision of models have the potential to narrow the gap between domain experts and informaticians, making NLP more valuable for clinical applications.

 

Building on my prior work on applying interactive learning for retrospective research on procedure notes, I am proposing its application for identifying important or relevant portions of clinical notes in electronic medical records. Many current clinical practices involve manually curating information in different summary forms such as sign-out notes, discharge summaries etc. These are time consuming processes and can be improved using interactive NLP methods. By considering an example use case for identifying important sentences in full-text reports for signout note preparation, I would like to study how interactive machine learning methods can be designed to help clinicians build and interactively revise NLP models for their own use.

Interest Area: Computer & Information Science & Engineering, General Interests, Health Sciences
 
 
People Who Viewed This Talk, Also Viewed
 
 
Export
RSS Feed: RSS 2.0
ATOM Feed: Atom
iCalendar: iCal
Share: Bookmark and Share
 
Google Calendar:
 
 
 
 
CoMeT Blog
©2009-2019 CoMeT - Supported by Google Grant
School of Information Sciences, University of Pittsburgh, 135 North Bellefield Avenue, Pittsburgh, PA 15260
Real Time Web Analytics