Advantages and Challenges of Medical Big Data Mining

Data mining seems to be a good tool for showing underlying knowledge of Medical Big Data (MBD). Understanding characteristics of data and possible challenges are the first steps of the journey. This study endeavors to inspect reasons, effects, and solutions of challenges as well as benefits of MBD mining.

In so doing, PubMed, ScienceDirect, Springer, and Google Scholar databases were scrutinized using two groups of keywords for benefits and challenges in the years 2011-202. The search language was English. Single-purpose studies were excluded and those studies that were focused on MBD mining were included. Then, challenge was examined separately and the results were categorized.

Extracted knowledge from MBD enhances quality of care. However, low-quality performance in gathering and storing the data, properties of big data, and inherent structure of medical data cause many problems for mining methods. Inconsistency, veracity, privacy, and security issues are the major challenging problems. Standardization and enhancing quality of data gathering, storing, and representing tasks are the effective problem prevention strategies. Designing and using appropriate frameworks, algorithms, and structures as well as utilizing machine learning and artificial intelligence techniques are the most effective solutions for dealing with the challenges.

MBD was appeared and expanded when the world was not ready for it. Thus, it caused many challenges for mining methods. Some of them are traceable, preventable, and manageable. However, some challenges need novel and intelligent methods that are able to handle MBD.