ScaDiPaSi: An Effective Scalable and Distributable MapReduce-Based Method to Find Patient Similarity on Huge Healthcare Networks

Healthcare network information growth follows an exponential pattern, and current database management systems cannot adequately manage this huge amount of data. It is necessary to use a “big data” solution for healthcare problems. One of the most important problems in healthcare is finding Patient Similarity (PaSi). Current methods for finding PaSi are not adaptive and do not support all data sources, nor can they fulfill user requirements for a query tool. In this paper, we propose a scalable and distributable method to solve PaSi problems over MapReduce architecture. ScaDiPaSi, supports storage and retrieval of all kinds of data sources in a timely manner. The dynamic nature of the proposed method helps users to define conditions on all entered fields. Our evaluation shows that we can use this method with high confidence and low execution time.