Creating Order Out of Chaos: Developing AI for Big Medical Data

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White Paper: Creating Order Out of Chaos: Developing AI for Big Medical Data

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Creating Order Out of Chaos: Developing AI for Big Medical Data

Medical data is generally messy and complicated. Electronic health records (EHR) are often sparse or incomplete. EHR is frequently presented in different and inconsistent units; in some cases, computer errors and lab tests even generate false results.

This white paper gives healthcare systems and professionals, physicians, CMIOs, Chief Quality Officers, population health managers, and payors valuable insights on the challenges inherent in making sense of big medical data, and why machine learning is a valuable tool for doing this.

 Developing AI for big medical data: Creating order out of chaos

 

They will come away with better understanding of how difficult and rewarding it is to have develop healthcare algorithms to find patient-specific risk trajectories. They will discover early care opportunities that this can offer to make a difference in the lives of patients and their families.

Highlights of the report include:

  • Why building healthcare algorithmic models is so difficult
  • How machine learning can identify real and false data outliers
  • Why imputing past and future EHR and EMR values is an asset
  • The critical importance of validating healthcare algorithms
  • Much, much more.

 

Read the white paper today.

 

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