Finding Negative Associations from Medical Data Streams Based on Frequent and Regular Patterns

Abstract

Medical data flows in streams as the data related to clinical tests and administered drugs by the doctors flows continuously. The doctors must be immediately alerted if negative associations are found among the drugs they prescribe. Data streams are to be processed in single scans as it is not possible to rescan the data for any iterative processing. To detect negative drug connections, regular and frequent drug patterns must be processed. Negative correlations between disease-curing medications might create adverse responses that kill patients. This paper proposes an algorithm that finds the negative associations among regular and frequent patterns mined from medical data streams. The algorithm mines the most effective and critical negative associations. By enforcing optimum frequency and regularity, the number of negative associations reduced to 0.43 from 0.73 for 1,000 item sets mined.