Machine Learning versus Natural Language Processing: What is the Difference?

July 22, 2020 - By Ashley Binford

Artificial intelligence is utilized for many use cases across the healthcare industry. However, just as all humans have different cognitive abilities, each type of artificial intelligence is distinct, and some applications are more advanced than others.

Artificial intelligence (AI) is a broad term referring to the field of technology that teaches machines to think and learn in order to perform tasks and solve problems like people. 

There are many types of artificial intelligence, but Iodine focuses on two: 

  1. Natural Language Processing, commonly referred to as NLP, interprets raw, arbitrary written text and transforms it into something a computer can understand. 
  2. Machine Learning, a form of applied statistics, solves problems based on large amounts of data by connecting the dots between many inputs without any human intervention. It answers questions similarly to how humans do, but automatically and on a much larger scale. 

What is the difference between the two? NLP interprets written language, whereas Machine Learning makes predictions based on patterns learned from experience. 

Iodine leverages both Machine Learning and NLP to power its CognitiveML™ Engine. These AI technologies work together to analyze, interpret, and understand the information within a patient’s medical record. Specifically, Iodine uses NLP to identify mentions of symptoms, diseases, procedures, medications, anatomical parts, and other key information. These mentions augment discrete clinical evidence including orders, results, medications, and demographic information and are evaluated by Iodine’s machine learning models. Machine learning models are mathematical representations of real world processes that are trained by analyzing vast amounts of data–billions of data points in Iodine’s case. Through pattern matching, these machine learning models emulate physician thought processes to determine the likelihood specific conditions exist and whether there are discrepancies between the patient’s clinical state and what has been documented.

Both NLP and Machine Learning are essential components of Iodine’s technology. Like CDI offerings available from other companies, Iodine uses NLP to determine what the documentation says which can help identify inconsistencies within the documentation and issues with specificity. By itself, however, NLP cannot find many financial and quality accuracy improvement opportunities because:

  • NLP cannot determine when patient information that is supported by medical data is not written in a patient’s chart. 
  • NLP cannot perform clinical validation, in which clinical evidence does not support something that has been documented, which increases the risk of audit. 

The objective of CDI is to determine whether the written documentation aligns with a patient’s clinical reality, and this requires more than NLP. This is where Machine Learning comes in. Machine Learning considers the entire clinical picture and makes connections or predictions based on learnings from other data, including: lab results, vital signs, medications, radiology results.

The result of combining NLP and Machine Learning is intelligent software that evaluates complex medical data similar to a physician’s approach to diagnosing and treating patients. Iodine’s Cognitive Emulation approach (via CognitiveML™ engine) augments the work of health system professionals with software that can actually “think” and learn from each new data point, just as physicians do. Iodine applies physician-like judgment to the clinical evidence in a patient’s chart and leverages previous learnings to more accurately determine the likelihood a condition exists. Conditions often present in a variety of ways, and by relying on clinical evidence rather than ambiguous thresholds, Iodine is able to identify and learn from these unique instances. 

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