Amazon Comprehend Medical features
Overview
Amazon Comprehend Medical is a HIPAA-eligible natural language processing (NLP) service that uses machine learning to extract health data from medical text–no machine learning experience is required.
Much of health data today is in free-form medical text like doctors’ notes, clinical trial reports, and patient health records. Manually extracting the data is a time consuming process, while automated rule-based attempts to extract the data don’t capture the full story as they fail to take context into account. As a result, the data remains unusable in large-scale analytics needed to advance the healthcare and life sciences industry and improve patient outcomes and create efficiencies.
With a simple API call to Amazon Comprehend Medical you can quickly and accurately extract information such as medical conditions, medications, dosages, tests, treatments and procedures, and protected health information while retaining the context of the information. Amazon Comprehend Medical can identify the relationships among the extracted information to help you build applications for use cases like population health analytics, clinical trial management, pharmacovigilance, and summarization. You can also use Amazon Comprehend Medical to link the extracted information to medical ontologies such as ICD10-CM, RxNorm or SNOMED CT to help you build applications for use cases like revenue cycle management (medical coding), claim validation and processing, and electronic health record creation.
Amazon Comprehend Medical is fully managed, so there are no servers to provision, and no machine learning models to build, train, or deploy. You pay only for what you use, and there are no minimum fees and no upfront commitments.
The Medical NERe API returns the medical information such as medication, medical condition, test, treatment and procedures (TTP), anatomy, and Protected Health Information (PHI). It also identifies relationships between extracted sub-types associated to Medications and TTP. There is also contextual information provided as entity “traits” (negation, or if a diagnosis is a sign or symptom). The table below shows the extracted information with relevant sub-types and entity traits.
To only extract PHI, you can use the Protected Health Information Data Identification (PHId) API.
Example: In this example, we are looking at the admission note. The API identifies medical information, and returns a confidence score.
Sample Text: Mr. Smith is a 63-year-old gentleman with coronary artery disease and hypertension. CURRENT MEDICATIONS: taking a dose of LIPITOR 20 mg once daily.
