Data mining used to develop comprehensive disease database

Data mining used to develop comprehensive disease database

on Oct 22, 14 • by Chris Bubinas • with No Comments

In an effort to combat global disease outbreaks, researchers at the University of Liverpool are working to develop the world's most comprehensive disease database. To achieve this goal, the scientists are turning to data mining solutions...

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Despite tremendous progress in recent years, contagious diseases remain a worldwide challenge. The current Ebola outbreak in western Africa is a powerful reminder of the damage that these pathogens can cause.

In an effort to combat global disease outbreaks, researchers at the University of Liverpool are working to develop the world's most comprehensive disease database, Labmate Online reported. And to achieve this goal, the scientists are turning to data mining solutions.

Data mining diseases
The source explained that the Liverpool University Climate and Infectious Diseases of Animals team aims to describe and map the connections between diseases and their hosts. All of this information will go into the group's Enhanced Infectious Diseases database, known as EID2.

To develop EID2, the researchers are applying advanced data mining techniques to the massive amount of scientific literature and relevant information already existent in disparate databases, the source explained. A significant portion of this data exists in unstructured or semistructured states, which previously made it difficult to collect and utilize in a single, coherent database. By applying big data analytics tools combined with high performance computing, though, the researchers hope to create a useful resource for anyone studying these pathogens.

Complex matters
According to Labmate Online, the Liverpool researchers have and will continue to utilize the data accumulated in EID2 for a variety of purposes. For example, the scientists have worked to examine the history of different human and animal diseases, tracing their spread and development over many years.

Additionally, the research will prove invaluable for predicting the impact climate change will have on numerous diseases. With this insight, researchers can create maps that reveal where certain diseases are more likely to spring up, and where they are most likely to spread.

Finally, the EID2 data can help disease researchers better understand the often-complex relationships between human and animal carriers and hosts. Improved categorization in this area could lead scientists to discover previously hidden connections between pathogens, which in turn could lead to new avenues for cures and treatments.

Data mining health care
While the EID2 project focuses on global health trends, data mining is also being applied to health-related matters on a more granular basis.

For example, Bloomberg Businessweek reported last month that the Carolinas HealthCare hospital chain uses this technology to analyze patient credit card data. By doing so, the organization is able to identify those patients who are most likely to require treatment in the near future and then take preventative steps to minimize the risk. 

Michael Dulin, chief clinical officer for analytics and outcomes research at Carolinas HealthCare, told the news source that providers can gain a lot more insight into a patient's health by data mining consumer-related information than through a single appointment at the doctor's office. He stated that his organization aims to assign risk scores to patients and deliver this information to the relevant doctors and nurses. These care professionals can then decide if and when to reach out to the affected individuals to provide lifestyle recommendations or encourage a visit to the hospital if they are at risk.

As more hospitals, clinics, doctor's offices and research facilities pursue data mining strategies, it is important for decision-makers to ensure that the right tools are in place to support such efforts. For example, personnel will need access to comprehensive numerical libraries, which can provide reliable, embeddable algorithms that can be incorporated into the organization's applications easily and effectively. Without such assets, many data mining efforts will yield suboptimal results.

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