A2 Review article, Literature review, Systematic review
Data Analytics in Healthcare : A Tertiary Study (2023)

Taipalus, T., Isomöttönen, V., Erkkilä, H., & Äyrämö, S. (2023). Data Analytics in Healthcare : A Tertiary Study. SN Computer Science, 4(1), Article 87. https://doi.org/10.1007/s42979-022-01507-0

JYU authors or editors

Publication details

All authors or editors: Taipalus, Toni; Isomöttönen, Ville; Erkkilä, Hanna; Äyrämö, Sami

Journal or series: SN Computer Science

ISSN: 2662-995X

eISSN: 2661-8907

Publication year: 2023

Publication date: 09/12/2022

Volume: 4

Issue number: 1

Article number: 87

Publisher: Springer Science and Business Media LLC

Publication country: Singapore

Publication language: English

DOI: https://doi.org/10.1007/s42979-022-01507-0

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/84553

Additional information: The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Abstract

The field of healthcare has seen a rapid increase in the applications of data analytics during the last decades. By utilizing different data analytic solutions, healthcare areas such as medical image analysis, disease recognition, outbreak monitoring, and clinical decision support have been automated to various degrees. Consequently, the intersection of healthcare and data analytics has received scientific attention to the point of numerous secondary studies. We analyze studies on healthcare data analytics, and provide a wide overview of the subject. This is a tertiary study, i.e., a systematic review of systematic reviews. We identified 45 systematic secondary studies on data analytics applications in different healthcare sectors, including diagnosis and disease profiling, diabetes, Alzheimer’s disease, and sepsis. Machine learning and data mining were the most widely used data analytics techniques in healthcare applications, with a rising trend in popularity. Healthcare data analytics studies often utilize four popular databases in their primary study search, typically select 25–100 primary studies, and the use of research guidelines such as PRISMA is growing. The results may help both data analytics and healthcare researchers towards relevant and timely literature reviews and systematic mappings, and consequently, towards respective empirical studies. In addition, the meta-analysis presents a high-level perspective on prominent data analytics applications in healthcare, indicating the most popular topics in the intersection of data analytics and healthcare, and provides a big picture on a topic that has seen dozens of secondary studies in the last 2 decades.

Keywords: data mining; data; big data; public health service; artificial intelligence; machine learning

Free keywords: data analytics; healthcare; machine learning; data mining; artificial intelligence

Contributing organizations

Ministry reporting: Yes

Reporting Year: 2023

Preliminary JUFO rating: 1