Improving Biomedical Signal Search Results in Big Data Case-Based Reasoning Environments. Academic Article

abstract

• Time series subsequence matching has importance in a variety of areas in healthcare informatics. These include case-based diagnosis and treatment as well as discovery of trends among patients. However, few medical systems employ subsequence matching due to high computational and memory complexities. This manuscript proposes a randomized Monte Carlo sampling method to broaden search criteria with minimal increases in computational and memory complexities over R-NN indexing. Information gain improves while producing result sets that approximate the theoretical result space, query results increase by several orders of magnitude, and recall is improved with no signi cant degradation to precision over R-NN matching.

authors

• Mortazavi, Bobak

published proceedings

• Pervasive Mob Comput

altmetric score

• 0.25

author list (cited authors)

• Woodbridge, J., Mortazavi, B., Bui, A., & Sarrafzadeh, M.

citation count

• 7

complete list of authors

• Woodbridge, Jonathan||Mortazavi, Bobak||Bui, Alex AT||Sarrafzadeh, Majid

publication date

• January 2016

publisher

• Elsevier  Publisher

published in

• Pervasive and Mobile Computing  Journal

keywords

• Biomedical Signal Search
• Case-based Reasoning
• Locality Sensitive Hashing
• Monte Carlo Sampling
• Time-series Subsequence Matching

Digital Object Identifier (DOI)

• 10.1016/j.pmcj.2015.09.006

start page

• 69

end page

• 80

volume

• 28

URL

• http://dx.doi.org/10.1016/j.pmcj.2015.09.006