A review of data mining using big data in health informatics

Abstract

The amount of data produced within Health Informatics has grown to be quite vast, and analysis of this Big Data grants potentially limitless possibilities for knowledge to be gained. In addition, this information can improve the quality of healthcare offered to patients. However, there are a number of issues that arise when dealing with these vast quantities of data, especially how to analyze this data in a reliable manner. The basic goal of Health Informatics is to take in real world medical data from all levels of human existence to help advance our understanding of medicine and medical practice. This paper will present recent research using Big Data tools and approaches for the analysis of Health Informatics data gathered at multiple levels, including the molecular, tissue, patient, and population levels. In addition to gathering data at multiple levels, multiple levels of questions are addressed: human-scale biology, clinical-scale, and epidemic-scale. We will also analyze and examine possible future work for each of these areas, as well as how combining data from each level may provide the most promising approach to gain the most knowledge in Health Informatics.

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“…can mean the difference between life and death. [8][9]] v) Value: It refers to the quality of data. The data of EMR's and EHR's are recognized as high value data normally.…”

The Big Data Era in Healthcare: Promises and Challenges

“…As in all areas, serious problems emerge in the field storage, processing and analysis of large-scale data [28][29][30]:…”

Learning from a Class Imbalanced Public Health Dataset: a Cost-based Comparison of Classifier Performance

“…In the public health domain, classification techniques can be used to analyze the effect of various social and environmental factors, such as work environment, living conditions, education on the health of the population. The relationship between patient features and diseases could help formulate effective public health interventions [8]. For instance, Takeda et al [9] utilized multiple logistic analyses to report the significant associations between mental health and psychosocial stressors like family relationship, pregnancy, and income.…”

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