Presentation Title

P-38 Automatic summarization of clinical abstracts for evidence-based medicine

Presenter Status

Department of Engineering and Computer Science

Second Presenter Status

Department of Computer Science

Third Presenter Status

MS Student in Computer Science

Fourth Presenter Status

MS Student in Computer Science

Fifth Presenter Status

Department of Pediatrics

Sixth Presenter Status

Department of Pediatrics and Department of Medical Education

Location

Buller Hallway

Start Date

1-11-2013 1:30 PM

End Date

1-11-2013 3:00 PM

Presentation Abstract

A central concern in Evidence Based Medicine (EBM) is how to convey research results effectively to practitioners. One important idea is to summarize results by key summary statistics that describe the effectiveness (or lack thereof) of a given intervention, specifically the absolute risk reduction (ARR) and number needed to treat (NNT). Manual summarization is slow and expensive, thus, with the exponential growth of the biomedical research literature, automated solutions are needed. We have developed a novel machine learning-based software system that generates EBM-oriented summaries from research abstracts of randomized controlled trials. The system has learned to identify descriptions of the treatment groups and outcomes, as well as various associated quantities. It distills these elements into summaries that include summary statistics calculated from the reported outcome values found in the text.

This document is currently not available here.

Share

COinS
 
Nov 1st, 1:30 PM Nov 1st, 3:00 PM

P-38 Automatic summarization of clinical abstracts for evidence-based medicine

Buller Hallway

A central concern in Evidence Based Medicine (EBM) is how to convey research results effectively to practitioners. One important idea is to summarize results by key summary statistics that describe the effectiveness (or lack thereof) of a given intervention, specifically the absolute risk reduction (ARR) and number needed to treat (NNT). Manual summarization is slow and expensive, thus, with the exponential growth of the biomedical research literature, automated solutions are needed. We have developed a novel machine learning-based software system that generates EBM-oriented summaries from research abstracts of randomized controlled trials. The system has learned to identify descriptions of the treatment groups and outcomes, as well as various associated quantities. It distills these elements into summaries that include summary statistics calculated from the reported outcome values found in the text.