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Designing and plotting a curve using field data requires precise calculations and execution. A horizontal curve with a radius of 200 meters and an intersection angle of 20 degrees is established using the method of perpendicular offsets from the long chord. The long chord, which spans between the curve's endpoints, is calculated to be 69.46 meters in length. To maintain accuracy in plotting, intervals of 3 meters are selected along the chord.The engineer determines the offset distances for each...
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Big data: More than big data sets.

Adrienne N Cobb1, Andrew J Benjamin2, Erich S Huang3

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Area of Science:

  • Medical Informatics
  • Health Data Science
  • Clinical Research Methodology

Background:

  • Big data refers to datasets too large for traditional analysis.
  • While established in business, big data concepts are emerging in medicine.
  • Electronic health records, genomic sequencing, and large registries present opportunities for medical big data.

Purpose of the Study:

  • To define big data in the medical context.
  • To outline methodologies for analyzing big data.
  • To illustrate practical clinical applications of big data.

Main Methods:

  • Review of literature and concepts related to big data in medicine.
  • Discussion of analytical approaches applicable to large-scale health datasets.
  • Synthesis of information from a panel discussion at the Central Surgical Association Annual Meeting.

Main Results:

  • Big data analytics can uncover complex patterns in healthcare delivery and patient outcomes.
  • Methodologies include machine learning, statistical modeling, and data mining.
  • Clinical applications span personalized medicine, disease prediction, and operational efficiency.

Conclusions:

  • Big data holds significant potential to advance medical research and patient care.
  • Understanding big data methodologies is crucial for healthcare professionals.
  • The integration of big data analytics is key to future medical innovation.