Challenges on Biomedical Information Retrieval and Question Answering

Every day, approximately 3000 new articles are published in biomedical journals. That averages to more than 2 articles every minute. Managing this large amount of data is a challenge in itself. Yet, ensuring that this wealth of knowledge is used for the sake of the patients in a timely manner is an even more demanding task for both computer scientists and biomedical experts.

The BioASQ project, which started on October 1st 2012 and runs for 2 years, aims to push research in information technology towards highly precise biomedical information retrieval systems. The project will achieve this goal through a competition (challenge), in which systems from teams around the world will compete. BioASQ will provide the data, software, hardware and the evaluation infrastructure for the challenge. By these means, the project will ensure that the biomedical experts of the future can rely on software tools to identify, process and present the fragments of the huge space of biomedical resources that address their personal questions.

The tasks included in the BioASQ challenges will help advance the state of the art in two fields. First, the automatic classification of biomedical documents will be improved. Here, systems will be required to tag large numbers of scientific biomedical articles with terms from a predefined biomedical vocabulary. Additionally, the challenge will evaluate how well systems identify text fragments in scientific articles, and related data in public knowledge bases, in order to answer questions set by the European biomedical expert team of BioASQ.

Further results of the project will include a set of open-source tools and a social network that will support experts in setting up similar challenges, beyond the end of the project.

The BioASQ team combines researchers with complementary expertise from 6 organisations in 3 countries: the Greek National Center for Scientific Research "Demokritos" (coordinator), participating with its Institutes of 'Informatics & Telecommunications' and 'Biosciences & Applications', the German IT company Transinsight GmbH, the French University Joseph Fourier, the German research Group for Agile Knowledge Engineering and Semantic Web at the University of Leipzig, the French University Pierre et Marie Curie-Paris 6 and the Research Center of the Athens University of Economics and Business in Greece. Moreover, biomedical experts from several countries assist in the creation of the evaluation data and a number of key players in the industry and academia from around the world participate in the advisory board of the project.

For further information, please visit:
http://www.bioasq.org

Most Popular Now

Do Fitness Apps do More Harm than Good?

A study published in the British Journal of Health Psychology reveals the negative behavioral and psychological consequences of commercial fitness apps reported by users on social media. These impacts may...

AI Tool Beats Humans at Detecting Parasi…

Scientists at ARUP Laboratories have developed an artificial intelligence (AI) tool that detects intestinal parasites in stool samples more quickly and accurately than traditional methods, potentially transforming how labs diagnose...

Making Cancer Vaccines More Personal

In a new study, University of Arizona researchers created a model for cutaneous squamous cell carcinoma, a type of skin cancer, and identified two mutated tumor proteins, or neoantigens, that...

AI can Better Predict Future Risk for He…

A landmark study led by University' experts has shown that artificial intelligence can better predict how doctors should treat patients following a heart attack. The study, conducted by an international...

A New AI Model Improves the Prediction o…

Breast cancer is the most commonly diagnosed form of cancer in the world among women, with more than 2.3 million cases a year, and continues to be one of the...

AI System Finds Crucial Clues for Diagno…

Doctors often must make critical decisions in minutes, relying on incomplete information. While electronic health records contain vast amounts of patient data, much of it remains difficult to interpret quickly...