Page 57 - Big Data Analytics for Intelligent Healthcare Management

3.5 RECENT WORKS IN BIG DATA ANALYTICS IN HEALTHCARE DATA 49

Patel et al. [21] discuss the reasons behind the consideration of data in healthcare and the results of
various surveys to demonstrate the influence of big data in healthcare. They also present some case
studies on big data analytics in healthcare industries. Different tools for handling big data problems
are discussed.
Simpao et al. [22] focuses on big data analytics in the anesthesia and healthcare units. They also
focus on visual analytics, which is the science of analytical reasoning simplified by interactive visual
interfaces. This can also assist with the performance of cognitive activities concerning big data. The
Anesthesia Quality Institute, and the Multicenter Preoperative Outcomes Group have led significant
efforts to gather anesthesia big data for outcomes research and this also aids quality improvement. They
suggested that the efficient use of data combined with quantitative and qualitative analysis to make
decisions can be employed to big data for excellence and performance enhancement. For instance, sev-
eral important applications are clinical decision support, predictive risk assessment, and resource
management.
Jokonya et al. [23] propose a big data integration framework to support deterrence and control of
HIV/AIDS, TB, and silicosis (HATS) in the mining industry. The link among HIV/AIDS, TB, and sil-
icosis is the focus in this work. The authors claim that their proposed approach is the first one to use big
data in understanding the linkage between HATS in the mining industry. The proposed big data frame-
work addresses the needs of predictive epidemiology, which is important in forecasting and disease
control in the mining industry. They suggest the use of a viable systems model and big data to tackle
the challenges of HATS in the mining industry.
Weider et al. [24] introduce a Big Data Based Recommendation Engine for early identification of
diseases in the modern health care environment. A classification algorithm, Naı ¨ve Bayes (NB), is used
to build this system. This algorithm runs on top of Apache Mahout and it advises the health conditions
of users, readmission rates, treatment optimization, and adverse occurrences. The proposed work fo-
cuses on analyzing and using new big data methodologies. The proposed approach is a very efficient
one in the sense that once the disease is identified, it will be easy to deliver the correct care to the pa-
tients and in this way, the average life expectancy of people can be increased if they are given suitable
care from the early stages.
Chrimes et al. [25] propose a framework built to form a big data analytics (BDA) platform via the
use of real volumes of healthcare big data. The existing high-performance computing (HPC) architec-
ture is utilized with HBase (NoSQL database) and Hadoop (HDFS). The generated NoSQL database
was imitated from metadata and inpatient profiles of the Vancouver Island Health Authority’s hospital
system. A special modification of Hadoop’s ecosystem and HBase with the addition of “salt buckets” to
ingest was utilized. The authors claim that data migration performance requirements of the proposed
BDA platform can capture large volumes of data while decreasing data retrieval times and its associ-
ations to innovative processes and configurations.
Chawla et al. [26] propose a personalized patient-centered framework, CARE. The proposed sys-
tem serves as a data-driven computational support for physicians evaluating the disease risks facing
their patients. It has the ability of early caution indicators of possible disease risks of an individual,
which can then be converted into a dialogue between the physician and patient and this will aid in
patient empowerment. CARE can be utilized in full potential to explore broader disease histories, rec-
ommend previously unconsidered concerns, and facilitate discussion about early testing and preven-
tion, as well as wellness strategies that may be more recognizable to the individual and easy to
implement.