– Amazon Comprehend Medical, Amazon’s HIPAA eligible natural language processing service that uses machine learning to find insights in unstructured texts, announced a new capability of linking information to medical ontologies.
– The new ontology-linking feature will help clinicians to detect medication and medical conditions in unstructured clinical text. Amazon will now support ICD-10-CM and RxNorm ontologies through Comprehend Medical.
Amazon Comprehend, a HIPAA eligible natural language processing service that makes it easy to use machine learning to extract relevant medical information from unstructured text. has announced a new capability of linking the information extracted by Comprehend Medical to medical ontologies. Using Comprehend Medical, you can quickly and accurately gather information, such as medical condition, medication, dosage, strength, and frequency from a variety of sources like doctors’ notes, clinical trial reports, and patient health records. The new ontology-linking feature will help clinicians to detect medication and medical conditions in unstructured clinical text. Amazon will now support ICD-10-CM and RxNorm ontologies through Comprehend Medical.
Understanding the Importance of Ontology
An ontology provides a declarative model of a domain that defines and represents the concepts existing in that domain, their attributes, and the relationships between them. It is typically represented as a knowledge base and made available to applications that need to use or share knowledge. Within health informatics, an ontology is a formal description of a health-related domain.
Ontology Link Capability Overview
The ontologies supported by Comprehend Medical are:
ICD-10-CM, to identify medical conditions as entities and link related information such as diagnosis, severity, and anatomical distinctions as attributes of that entity. This is a diagnosis code set that is very useful for population health analytics, and for getting payments from insurance companies based on medical services rendered.
RxNorm, to identify medications as entities and link attributes such as dose, frequency, strength, and route of administration to that entity. Healthcare providers use these concepts to enable use cases like medication reconciliation, which is is the process of creating the most accurate list possible of all medications a patient is taking.
For each ontology, Comprehend Medical returns a ranked list of potential matches. You can use confidence scores to decide which matches make sense, or what might need further review. Let’s see how this works with an example.
How Ontology Linking Works
In the Comprehend Medical console, I start by giving some unstructured, doctor notes in input:
At first, I use some functionalities that were already available in Comprehend Medical to detect medical and protected health information (PHI) entities.
Among the recognized entities (see this post for more info) there are some symptoms and medications. Medications are recognized as generics or brands. Let’s see how we can connect some of these entities to more specific concepts.
In this scenario, the new features are used to link those entities to RxNorm concepts for medications.
In the text, only the parts mentioning medications are detected. In the details of the answer, I see more information. For example, let’s look at one of the detected medications:
The first occurrence of the term “Clonidine” (in the second line in the input text above) is linked to the generic concept (on the left in the image below) in the RxNorm ontology.
The second occurrence of the term “Clonidine” (in the fourth line in the input text above) is followed by an explicit dosage, and is linked to a more prescriptive format that includes dosage (on the right in the image below) in the RxNorm ontology.
To look for medical conditions using ICD-10-CM concepts, a different input is given:
The goal of the linking feature again is to link the detected entities, like symptoms and diagnoses, to specific concepts.
As expected, diagnoses and symptoms are recognized as entities. In the detailed results, those entities are linked to the medical conditions in the ICD-10-CM ontology. For example, the two main diagnoses described in the input text are the top results, and specific concepts in the ontology are inferred by Comprehend Medical, each with its own score.
In production, users can use Comprehend Medical via API, to integrate these functionalities with your processing workflow. All the screenshots above render visually the structured information returned by the API in JSON format. For example, this is the result of detecting medications (RxNorm concepts):
Amazon Comprehend Ontology Linking Availability
Amazon Comprehend Medical is available via the console, AWS Command Line Interface (CLI), or AWS SDKs. With Comprehend Medical, you pay only for what you use. You are charged based on the amount of text processed on a monthly basis, depending on the features you use. For more information, please see the Comprehend Medical section in the Comprehend Pricing page. Ontology Linking is available in all regions were Amazon Comprehend Medical is offered, as described in the AWS Regions Table.
The new ontology linking APIs makes it easy to detect medications and medical conditions in unstructured clinical text and link them to RxNorm and ICD-10-CM codes respectively. This new feature can help users reduce the cost, time and effort of processing large amounts of unstructured medical text with high accuracy.
