ReDD-Observatory: Using the Web of Data for Evaluating the Research-Disease Disparity

The ReDD-Observatory is a project to evaluate the disparity between active areas of biomedical research and the global burden of disease using Linked Data and data-driven discovery.

Background

It is widely accepted that there is a large disparity between the availability of treatment options and the prevalence of diseases in the world, thus placing individuals in danger. This disparity is partially caused by the restricted access to information that would allow health- care and research policy makers to formulate more appropriate measures to mitigate this disparity. Specifically, this shortage of information is caused by the difficulty in reliably obtaining and integrating data regarding the disease burden for a given nation and the respective research investments.

In response to these challenges, the Linked Data paradigm provides a simple mechanism for publishing and interlinking structured information on the Web. In conjunction with the ever increasing data on diseases and healthcare research available as Linked Data, an opportunity is created to reduce this information gap that would allow for better policy in response to these disparities.

We present the ReDD-Observatory, an approach for evaluating the Research-Disease Disparity based on the interlinking and integrating of various biomedical data sources.

Methodology

The figure below provides a birds eye-view of the methodology involved in the ReDD-Observatory.

We first identified relevant datasets to be included that provided relevant information to evaluate the disparity. We not only consider the datasets already present as RDF but also those that are present in unstructured formats. These datasets are:

  1. LinkedCT - the RDF representation of ClinicalTrials.gov, which is the database of all clinical trials around the world.
  2. Bio2RDF's PubMed - the RDF representation of PubMed, which is a service of the US National Library of Medicine that includes bibliographic information and abstracts of over 19 million publications from MEDLINE and other life science journals.
  3. WHO's Global Health Observatory (GHO), which contains statistical information regarding the mortality and burden of disease classified according to the death and DALY (disability-adjusted life year) estimates grouped by countries and regions. However, since GHO is not available as Linked Data, as the next step we devised a method for representing unstructured data as RDF. We devised a plug-in in OntoWiki to represent statistical data from GHO as RDF. We used the Data Cube Vocabulary for this conversion. More information is present here. In order to ensure the completeness, conciseness and consistency for the selected datasets our next step is to assess the data quality of the datasets. The next challenging step is to interlink the datasets for a number of concepts such as (a) countries, (b) diseases and (c) publications. The assessment of the disparity is then performed with a number of parametrized SPARQL queries. We evaluate the results wrt. information quality and interlinking precision. As a consequence, we are, for the first time, able to provide reliable indicators for the extent of the research-disease disparity around the world in an semi- automated fashion, thus enabling healthcare professionals and policy makers to make more informed decisions.

Further Information

Project Team

Publications

by (Editors: ) [BibTex of ]

News

AKSW Colloquium, 22-06-2015, Concept Expansion Using Web Tables, Mining entities from the Web, Linked Data Stack ( 2015-06-22T12:11:56+02:00 by Mohamed Sherif)

2015-06-22T12:11:56+02:00 by Mohamed Sherif

Concept Expansion Using Web Tables by Chi Wang, Kaushik Chakrabarti, Yeye He,Kris Ganjam, Zhimin Chen, Philip A. Bernstein (WWW’2015), presented by Ivan Ermilov: Abstract. Read more about "AKSW Colloquium, 22-06-2015, Concept Expansion Using Web Tables, Mining entities from the Web, Linked Data Stack"

AKSW Colloquium, 15-06-2015, Caching for Link Discovery ( 2015-06-12T23:33:25+02:00 TommasoSoru)

2015-06-12T23:33:25+02:00 TommasoSoru

Using Caching for Local Link Discovery on Large Data Sets [PDF] by Mofeed Hassan Engineering the Data Web in the Big Data era demands the development of time- and space-efficient solutions for covering the lifecycle of Linked Data. Read more about "AKSW Colloquium, 15-06-2015, Caching for Link Discovery"

AKSW Colloquium, 08-06-2015, DBpediaSameAs, Dynamic-LOD ( 2015-06-05T02:00:35+02:00 by Dr. Amrapali Zaveri)

2015-06-05T02:00:35+02:00 by Dr. Amrapali Zaveri

DBpediaSameAs: An approach to tackling heterogeneity in DBpedia identifiers by Andre Valdestilhas This work provides an approach to tackle heterogeneity about a problem where several transient owl:sameAs redundant occurrences were found in DBpedia identifiers during searching for owl:sameAs occurrences that … Continue reading → Read more about "AKSW Colloquium, 08-06-2015, DBpediaSameAs, Dynamic-LOD"

Smart Data Web project kick-off ( 2015-06-01T13:55:29+02:00 MartinBruemmer)

2015-06-01T13:55:29+02:00 MartinBruemmer

Smart Data Web, a new BMWi funded project kicked-off in Berlin. Central goal of Smart Data Web is leveraging state-of-the-art data extraction and enrichment technologies as well as Linked Data to create value-added systems for German industry. Read more about "Smart Data Web project kick-off"

AKSW Colloquium, 01-06-2015, MEX – Publishing ML Experiment Results, Scaling DL-Learner – Status and Plans ( 2015-05-31T22:23:34+02:00 by Simon Bin)

2015-05-31T22:23:34+02:00 by Simon Bin

MEX – Publishing ML Experiment Results by Diego Esteves Over the decades many machine learning experiments have been published, collaborating with the scientific community progress. Read more about "AKSW Colloquium, 01-06-2015, MEX – Publishing ML Experiment Results, Scaling DL-Learner – Status and Plans"