Healthcare organizations that leverage both text analytics and machine learning are better positioned to improve patient outcomes. Used in tandem, text analytics and machine learning can significantly improve the accuracy of risk scores, used widely in healthcare to help clinicians identify patients at high risk for certain conditions and, therefore, intervene. Health systems can run machine learning models with input from text analytics to provide tailored risk predictions on both unstructured and structured data. The result? More accurate risk scores and the ability to identify every patient’s level of risk in time to inform decisions about their care.
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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:
- ConText—solves for negation, experiencer, and temporality.
- cTAKES—includes the functionality of ConText, and adds the ability to identify the type of clinical term.