Info
Albuquerque, New Mexico USA | Published on: February 21, 2013
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3D #Data #Visualization Could Improve Infectious Disease Detection #science #UNMHSC #disease http://pr.co/p/000une
Summary
For decades, the detection of infectious diseases has been limited by how data are analyzed. This new research explores the possibility that medical data might have different structural levels, which involve several dimensions. The new method explores rotating three-dimensional (3D) data structures instead of analyzing data on a flat format – uni-dimensional (2D) data as reported in any table.
Details

For decades, the detection of infectious diseases has been limited by how data are analyzed. With the classic data analysis method, some truly infected individuals can be missed (‘false negative’ results), and a substantial number of infected and non-infected observations can be mixed – problems that result in misdiagnoses.           

In a publication released today by PLOS ONE, University of New Mexico researchers Drs. Ariel L. Rivas and Douglas J. Perkins, along with an international network of collaborators, show a new way to address this double problem. Their approach explores the possibility that medical data might have different structural levels, which involve several dimensions. Instead of analyzing data on a flat format – uni-dimensional data as reported in any table; or bi-dimensional (2D) data, as any figure reported on a page or screen – the new method explores rotating three-dimensional (3D) data structures. This new method facilitates the expression of any feature the data might have, which cannot be revealed by ‘flat’ formats.

When the 3D-based system is applied, humans, birds and cows display similar data patterns, regardless of whether they are infected by bacteria, virus, or parasites. The new model is robust, revealing patterns well-conserved throughout evolution.

“Our approach is similar to the way meaning or information emerges in general,” Rivas explains. “We need information, not just data. For example, when we look at any one letter, no information is obtained. However, when we combine letters, words emerge and, with them, meaning. If, in addition, we combine words; sentences emerge, which provide even more information. The higher the level or structure (words, sentences, paragraphs and beyond), the greater the information generated. We apply the same concept to biomedical data: the more dimensions and levels considered; the more interpretable and usable the information retrieved.”

Because this new model can detect false negatives, it drastically reduces the amount of mixed (infected and non-infected) observations, and to be implemented, it only requires leukocytes (the “white” cells found in fluids, such as blood and milk). This approach can be applied in virtually any medical setting in the world – one of the goals of UNM’s Center for Global Health.

Contributing authors:

Ariel L Rivas, Prakasha Kempaiah, Tom Were, John M. Ong’echa, and  Douglas J. Perkins (Center for Global Health, University of New Mexico, Albuquerque, NM); Mark D. Jankowski (Department of Zoology, University of Wisconsin-Madison, Madison, WI); Renata Piccinini and Rachel Pilla (Animal Pathology, Università degli Studi di Milano, Milan, Italy); Gabriel Leitner and Shlomo Blum (Kimron Veterinary Institute, Bet Dagan, Israel); Daniel Schwarz, Ulrike S. Diesterbeck, and Claus-Peter Czerny (Animal Sciences Department, Faculty of Agricultural Sciences, Georg-August-University, Göttingen, Germany); Kevin L Anderson, Population Health and Pathobiology, North Carolina State University, Raleigh, NC); Jeanne M. Fair (Biosecurity & P. Health, Los Alamos National Laboratory, Los Alamos, NM); Almira L. Hoogesteijn (Human Ecology, CINVESTAV, Merida, Mexico); Wilfried Wolter (Regierungspräsidium Gießen, Wetzlar, Germany); Marcelo Chaffer (Department of Health Management, Atlantic Veterinary College, University of Prince Edward Island, Charlottetown, Canada); James B Hittner (Department of Psychology, College of Charleston, Charleston, SC); and James M. Hyman (Department of Mathematics, Tulane University, New Orleans, LA).