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Data Analytics in Healthcare: A Tertiary Study.

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Data analytics in healthcare automates areas like medical imaging and disease recognition. This review of reviews highlights machine learning and data mining as key techniques, guiding future research.

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Area of Science:

  • Healthcare Data Analytics
  • Medical Informatics
  • Computational Health

Background:

  • Healthcare has rapidly adopted data analytics over the past decades.
  • Applications include medical image analysis, disease recognition, outbreak monitoring, and clinical decision support.
  • Numerous secondary studies exist at the intersection of healthcare and data analytics.

Purpose of the Study:

  • To provide a comprehensive overview of data analytics applications in healthcare.
  • To conduct a tertiary study, specifically a systematic review of systematic reviews.
  • To identify trends and popular topics in healthcare data analytics research.

Main Methods:

  • Systematic review of 45 systematic secondary studies.
  • Analysis of data analytics applications across various healthcare sectors.
  • Identification of commonly used databases, primary study counts, and research guidelines (e.g., PRISMA).

Main Results:

  • Machine learning and data mining are the most prevalent data analytics techniques in healthcare.
  • A rising trend in the popularity of these techniques was observed.
  • Commonly used databases, typical numbers of primary studies, and increasing adherence to guidelines like PRISMA were identified.

Conclusions:

  • This review offers a high-level perspective on prominent data analytics applications in healthcare.
  • It highlights popular topics and provides a broad overview of a rapidly evolving field.
  • Findings can assist researchers in conducting timely literature reviews and empirical studies.