Text Analytics in Healthcare—Two Promising Frameworks that Meet Its Unique Demands

Text analytics in healthcare is unique because it demands a higher degree of precision than what’s acceptable for a Google search or Yelp recommendation. So, it’s no surprise fewer than five percent of health systems are effectively leveraging clinical information in a truly significant way.

Fortunately, there are two promising frameworks for clinical awareness designed to meet the industry’s unique text analytics demands:

  1. ConText—solves for negation, experiencer, and temporality.
  2. cTAKES—includes the functionality of ConText, and adds the ability to identify the type of clinical term.

By integrating these frameworks into their solutions, health systems can overcome the unique challenges of text analytics in healthcare. They can evolve from simply finding matching text to understanding context and achieving the precision required in patient care.

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Text Analytics in Healthcare—Two Promising Frameworks that Meet Its Unique Demands

Text analytics in healthcare is unique because it demands a higher degree of precision than what’s acceptable for a Google search or Yelp recommendation. So, it’s no surprise fewer than five percent of health systems are effectively leveraging clinical information in a truly significant way.

Fortunately, there are two promising frameworks for clinical awareness designed to meet the industry’s unique text analytics demands:

  1. ConText—solves for negation, experiencer, and temporality.
  2. cTAKES—includes the functionality of ConText, and adds the ability to identify the type of clinical term.

By integrating these frameworks into their solutions, health systems can overcome the unique challenges of text analytics in healthcare. They can evolve from simply finding matching text to understanding context and achieving the precision required in patient care.

Read More
My Folder