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Data-driven healthcare: from patterns to actions.

M Grossglauser1, H Saner2

  • 1School of Computer and Communication Sciences, Ecole Polytechnique Fédérale de Lausanne, Switzerland matthias.grossglauser@epfl.ch.

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Summary
This summary is machine-generated.

Big data in healthcare enables personalized medicine and chronic disease management. This paper explores autonomous decision-making in data-driven healthcare, examining challenges and future research directions.

Keywords:
Big dataanalyticsdata miningpreventive care

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Area of Science:

  • Healthcare technology
  • Data science
  • Behavioral science

Background:

  • Big data presents new opportunities in personalized medicine, preventive care, and chronic disease management.
  • Online data offers insights into human behavior and its link to health.
  • Autonomous decision-making is a key aspect of data-driven healthcare.

Purpose of the Study:

  • To explore autonomous decision-making in data-driven healthcare.
  • To examine examples of technology-driven autonomous decisions with limited human intervention.
  • To identify technical and practical challenges and outline a research agenda.

Main Methods:

  • Review of current applications and potential future scenarios of autonomous decision-making in healthcare.
  • Analysis of data-driven healthcare models.
  • Exploration of the interplay between behavioral patterns and health data.

Main Results:

  • Identified three key areas where autonomous data-driven decisions are expected.
  • Highlighted the potential for technology to make health decisions with minimal human input.
  • Outlined anticipated technical and practical challenges.

Conclusions:

  • Autonomous decision-making in healthcare is a growing field with significant potential.
  • Addressing technical and practical challenges is crucial for successful implementation.
  • Further research is needed to develop a robust agenda for data-driven autonomous healthcare.