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From Big Data to Precision Medicine.

Tim Hulsen1, Saumya S Jamuar2, Alan R Moody3

  • 1Department of Professional Health Solutions and Services, Philips Research, Eindhoven, Netherlands.

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|March 19, 2019
PubMed
Summary
This summary is machine-generated.

Big data analytics offers new potential in medical research but requires standardized data and improved training. It complements, rather than replaces, traditional hypothesis-driven science for better clinical practice.

Keywords:
big databig data analyticsdata scienceprecision medicinetranslational medicine

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Area of Science:

  • Biomedical Research
  • Data Science
  • Medical Informatics

Background:

  • The term "Big data" has evolved beyond mere data volume to encompass advanced analytical capabilities.
  • The increasing capacity to generate, store, and analyze vast datasets presents new opportunities and challenges in medicine.

Purpose of the Study:

  • To discuss the opportunities and challenges of Big data analytics in biomedical research.
  • To highlight the need for standardization, collaboration, and revised educational approaches in data science for medical researchers.

Main Methods:

  • Review of the evolving landscape of Big data and its analytical approaches.
  • Discussion of the integration of data-driven hypothesis generation with traditional scientific methods.

Main Results:

  • Big data analytics can drive significant changes in clinical practice, including personalized therapy and electronic health record mining.
  • Key challenges include data standardization, fostering collaborative networks, and enhancing analytic methodology education for medical researchers.

Conclusions:

  • Big data analytics offers synergistic benefits when combined with classical hypothesis-driven research, leading to improved clinical practice.
  • The true promise lies in integrating data-driven insights with rigorous experimental validation, enhancing rather than replacing traditional scientific paradigms.