Improving biomedical signal search results in big data case-based reasoning environments

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 paper proposes a randomized Monte Carlo sampling method to broaden search criteria with minimal increases in computational and memory complexities over RR-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 significant degradation to precision over RR-NN matching.