Presentation Title
P-32 Using Evidence-based medicine summaries to help answer health economic questions
Presenter Status
Department of Engineering and Computer Science
Location
Buller Hallway
Start Date
31-10-2014 1:30 PM
End Date
31-10-2014 3:00 PM
Presentation Abstract
High quality meta-analyses, systematic reviews, and structured literature reviews are extremely useful for understanding the quality, and strength of published findings. However, high quality review studies, are time consuming and many published studies are suboptimal – lacking rigor, statistical power, or sufficiently specified models, a particular concern for cost-related studies. ACRES (Automatic Clinical Result Extraction and Summarization) is a machine learning-based software program designed to read abstracts from PubMed, extract the key trial elements, compute ratios (e.g., absolute risk reduction (ARR)) for proposed treatments, and generate summaries for the purpose of evidence-based medicine decision making. By generating detailed summaries and three ranking categories for PubMed search results, ACRES reduced time spent examining irrelevant papers, and was 4% more accurate in identifying relevant papers than was a systematic review on diabetes education and cost that was conducted by humans in 2008.
P-32 Using Evidence-based medicine summaries to help answer health economic questions
Buller Hallway
High quality meta-analyses, systematic reviews, and structured literature reviews are extremely useful for understanding the quality, and strength of published findings. However, high quality review studies, are time consuming and many published studies are suboptimal – lacking rigor, statistical power, or sufficiently specified models, a particular concern for cost-related studies. ACRES (Automatic Clinical Result Extraction and Summarization) is a machine learning-based software program designed to read abstracts from PubMed, extract the key trial elements, compute ratios (e.g., absolute risk reduction (ARR)) for proposed treatments, and generate summaries for the purpose of evidence-based medicine decision making. By generating detailed summaries and three ranking categories for PubMed search results, ACRES reduced time spent examining irrelevant papers, and was 4% more accurate in identifying relevant papers than was a systematic review on diabetes education and cost that was conducted by humans in 2008.