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Beyond Two Cultures: Cultural Infrastructure for Data-driven Decision Support.

Nikki L B Freeman1, John Sperger1, Helal El-Zaatari1

  • 1Department of Biostatistics, University of North Carolina at Chapel Hill.

Observational Studies
|February 2, 2022
PubMed
Summary
This summary is machine-generated.

Algorithmic modeling is now common, but failures highlight the need to focus on data and problems. Emerging data-driven decision support requires social awareness and technical skill for precision health.

Keywords:
Decision SupportMachine LearningPrecision healthTwo Cultures

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Area of Science:

  • Statistics
  • Data Science
  • Decision Support Systems

Background:

  • Algorithmic modeling has transitioned from controversial to commonplace in statistics over the past two decades.
  • Despite widespread adoption, high-profile failures of algorithmic models indicate a deviation from foundational principles emphasizing data and problem context.
  • Dr. Leo Breiman's seminal paper, 'Statistical Modeling: The Two Cultures,' remains a critical reference point.

Purpose of the Study:

  • To introduce and advocate for the emerging field of data-driven decision support within statistics.
  • To highlight the necessity of integrating social awareness and accountability with technical expertise for effective decision support.
  • To explore the application of these principles within the domain of precision health.

Main Methods:

  • Review of the evolution of statistical modeling techniques.
  • Analysis of high-profile failures in algorithmic modeling.
  • Conceptual framework development for data-driven decision support.

Main Results:

  • Algorithmic modeling is now a standard statistical tool, yet its application is sometimes suboptimal.
  • A gap exists between the technical application of models and the crucial emphasis on problem and data.
  • Data-driven decision support is identified as a key emerging area.

Conclusions:

  • Realizing the full potential of decision support, particularly in precision health, necessitates a dual focus on technical rigor and socio-cultural factors.
  • A culture of social awareness and accountability is crucial for the successful implementation of data-driven decision support.
  • Continued attention to complex technical challenges must be balanced with an understanding of the broader context of data and problems.