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      A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining

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          Abstract

          The growing healthcare industry is generating a large volume of useful data on patient demographics, treatment plans, payment, and insurance coverage—attracting the attention of clinicians and scientists alike. In recent years, a number of peer-reviewed articles have addressed different dimensions of data mining application in healthcare. However, the lack of a comprehensive and systematic narrative motivated us to construct a literature review on this topic. In this paper, we present a review of the literature on healthcare analytics using data mining and big data. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a database search between 2005 and 2016. Critical elements of the selected studies—healthcare sub-areas, data mining techniques, types of analytics, data, and data sources—were extracted to provide a systematic view of development in this field and possible future directions. We found that the existing literature mostly examines analytics in clinical and administrative decision-making. Use of human-generated data is predominant considering the wide adoption of Electronic Medical Record in clinical care. However, analytics based on website and social media data has been increasing in recent years. Lack of prescriptive analytics in practice and integration of domain expert knowledge in the decision-making process emphasizes the necessity of future research.

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          Most cited references153

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          Big data analytics in healthcare: promise and potential

          Objective To describe the promise and potential of big data analytics in healthcare. Methods The paper describes the nascent field of big data analytics in healthcare, discusses the benefits, outlines an architectural framework and methodology, describes examples reported in the literature, briefly discusses the challenges, and offers conclusions. Results The paper provides a broad overview of big data analytics for healthcare researchers and practitioners. Conclusions Big data analytics in healthcare is evolving into a promising field for providing insight from very large data sets and improving outcomes while reducing costs. Its potential is great; however there remain challenges to overcome.
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            Multiparameter Intelligent Monitoring in Intensive Care II: a public-access intensive care unit database.

            We sought to develop an intensive care unit research database applying automated techniques to aggregate high-resolution diagnostic and therapeutic data from a large, diverse population of adult intensive care unit patients. This freely available database is intended to support epidemiologic research in critical care medicine and serve as a resource to evaluate new clinical decision support and monitoring algorithms. Data collection and retrospective analysis. All adult intensive care units (medical intensive care unit, surgical intensive care unit, cardiac care unit, cardiac surgery recovery unit) at a tertiary care hospital. Adult patients admitted to intensive care units between 2001 and 2007. None. The Multiparameter Intelligent Monitoring in Intensive Care II (MIMIC-II) database consists of 25,328 intensive care unit stays. The investigators collected detailed information about intensive care unit patient stays, including laboratory data, therapeutic intervention profiles such as vasoactive medication drip rates and ventilator settings, nursing progress notes, discharge summaries, radiology reports, provider order entry data, International Classification of Diseases, 9th Revision codes, and, for a subset of patients, high-resolution vital sign trends and waveforms. Data were automatically deidentified to comply with Health Insurance Portability and Accountability Act standards and integrated with relational database software to create electronic intensive care unit records for each patient stay. The data were made freely available in February 2010 through the Internet along with a detailed user's guide and an assortment of data processing tools. The overall hospital mortality rate was 11.7%, which varied by critical care unit. The median intensive care unit length of stay was 2.2 days (interquartile range, 1.1-4.4 days). According to the primary International Classification of Diseases, 9th Revision codes, the following disease categories each comprised at least 5% of the case records: diseases of the circulatory system (39.1%); trauma (10.2%); diseases of the digestive system (9.7%); pulmonary diseases (9.0%); infectious diseases (7.0%); and neoplasms (6.8%). MIMIC-II documents a diverse and very large population of intensive care unit patient stays and contains comprehensive and detailed clinical data, including physiological waveforms and minute-by-minute trends for a subset of records. It establishes a new public-access resource for critical care research, supporting a diverse range of analytic studies spanning epidemiology, clinical decision-rule development, and electronic tool development.
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              Use of intensive care at the end of life in the United States: an epidemiologic study.

              Despite concern over the appropriateness and quality of care provided in an intensive care unit (ICU) at the end of life, the number of Americans who receive ICU care at the end of life is unknown. We sought to describe the use of ICU care at the end of life in the United States using hospital discharge data from 1999 for six states and the National Death Index. Retrospective analysis of administrative data to calculate age-specific rates of hospitalization with and without ICU use at the end of life, to generate national estimates of end-of-life hospital and ICU use, and to characterize age-specific case mix of ICU decedents. All nonfederal hospitals in the states of Florida, Massachusetts, New Jersey, New York, Virginia, and Washington. All inpatients in nonfederal hospitals in the six states in 1999. None. We found that there were 552,157 deaths in the six states in 1999, of which 38.3% occurred in hospital and 22.4% occurred after ICU admission. Using these data to project nationwide estimates, 540,000 people die after ICU admission each year. The age-specific rate of ICU use at the end of life was highest for infants (43%), ranged from 18% to 26% among older children and adults, and fell to 14% for those >85 yrs. Average length of stay and costs were 12.9 days and $24,541 for terminal ICU hospitalizations and 8.9 days and $8,548 for non-ICU terminal hospitalizations. One in five Americans die using ICU services. The doubling of persons over the age of 65 yrs by 2030 will require a system-wide expansion in ICU care for dying patients unless the healthcare system pursues rationing, more effective advanced care planning, and augmented capacity to care for dying patients in other settings.
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                Author and article information

                Journal
                Healthcare (Basel)
                Healthcare (Basel)
                healthcare
                Healthcare
                MDPI
                2227-9032
                23 May 2018
                June 2018
                : 6
                : 2
                : 54
                Affiliations
                [1 ]Mechanical and Industrial Engineering, Northeastern University, Boston, MA 02115, USA; islam.m@ 123456husky.neu.edu (M.S.I.); hasan.mdm@ 123456husky.neu.edu (M.M.H.); wang.xiaoyi@ 123456husky.neu.edu (X.W.); hayley.germack@ 123456yale.edu (H.D.G.)
                [2 ]National Clinician Scholars Program, Yale University School of Medicine, New Haven, CT 06511, USA
                [3 ]Bouvé College of Health Sciences, Northeastern University, Boston, MA 02115, USA
                Author notes
                [* ]Correspondence: mnalam@ 123456neu.edu ; Tel.: +1-617-373-2275
                Author information
                https://orcid.org/0000-0001-5353-9710
                Article
                healthcare-06-00054
                10.3390/healthcare6020054
                6023432
                29882866
                1c3cc10a-e26a-4cb1-8de9-f38af61876f5
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 28 April 2018
                : 21 May 2018
                Categories
                Review

                healthcare,data analytics,data mining,big data,healthcare informatics,literature review

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