Comunicación

The practice of Evidence Based Medicine (EBM) requires the physician
to access the most up to date clinical evidence reported in the medical
research literature. However good practice of EBM is seriously affected by
the enormous amount of medical literature available and the limited time
that the physician has. In this talk I will focus on my research group's
current work on natural language processing for EBM. We address the task as
one of query-focused multi-document summarisation where a clinical question
retrieves several documents, and the clinical evidence must be extracted,
summarised and presented in the most effective manner. We have recently
gathered a corpus of clinical questions, their evidence summaries, and the
related references, and we are currently using it to produce and test various
tasks related to the ultimate goal of finding and summarising the evidence.

Dr. Diego Molla-Aliod is a senior lecturer at Macquarie University. His
research focuses in query-based information extraction systems. He was
the principal researcher in the ExtrAns answer extraction system of
unix documentation at University of Zurich, and then the project leader
of AnswerFinder, a question-answering system developed at Macquarie
University. Since 2009 his research has focused on the medical domain
and recently he has made available a corpus for summarisation in evidence
based medicine. He is a co-founder and past secretary and president of the
Australasian Language Technology Association (ALTA), and was the leader of
the Search Technology priority area of the HCSNet ARC Research Network.