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

Atrak: a MapReduce-based data warehouse for big data

“…This method can be used in graph processing [33], data mining [34] and specific problems like finding patient similarity [35]. For future works, this method can also be used for interactive query processing, online data mining and stream processing.…”

Kavosh: an effective Map-Reduce-based association rule mining method

“…Data format unification can be applied to problems in other fields. This method can be used in data warehouse like Aras and Atrak methods [22,23], graph processing [24], integrating multidimensional data sources [25] and specific problems like finding patient similarity [26]. For future works, this method can also be used for interactive query processing, online data mining and stream processing.…”

Patient Similarity: Emerging Concepts in Systems and Precision Medicine

“…There are certain challenges in patient similarity, such as network bottlenecks, low hardware performance (processing power and memory), and data locality (Osman et al, 2013; Karapiperis and Verykios, 2014; Barkhordari and Niamanesh, 2015). Given the observational or retrospective nature of patient similarity, interpretation of data analysis will be imperfect.…”

“…Others have produced algorithms that address scalability and uncertainty, by requiring parallel or distributed algorithm implementations built to scale, and enhancing interpretability by conveying the certainty of results presented (Feldman et al, 2015). One such algorithm or platform is scalable and distributable patient similarity (ScaDiPaSi), a dynamic method for investigating patient similarity that spreads the algorithm over several self-sufficient hardware nodes to process query data from various sources of different formats simultaneously (Barkhordari and Niamanesh, 2015). Another tool, MapReduce, employs several optimization techniques, such as job scheduling and cascading work flows over multiple interdependent hardware nodes (Dean and Ghemawat, 2008).…”

“…Healthcare data generates a huge volume of information in various formats at high velocity with sometimes questionable veracity (Barkhordari and Niamanesh, 2015) (4V). As a result, big data tools such as patient similarity are necessary to facilitate analytics, which reduces costs (Srinivasan and Arunasalam, 2013) and improves healthcare systems (Jee and Kim, 2013).…”