Hi Bubblers !
With this plugin, you can detect and return useful information in unstructured clinical text such as physician’s notes, discharge summaries, test results, and case notes.
Our plugin uses natural language processing (NLP) models to detect entities, which are textual references to medical information such as medical conditions, medications, or Protected Health Information (PHI).
This plugin provides an action to determine following classes:
- Entity: A text reference to the name of relevant objects, such as people, treatments, medications, and medical conditions. For example, ibuprofen.
- Category: The generalized grouping to which an entity belongs. For example, ibuprofen is part of the MEDICATION category.
- Type: The type of entity detected within a single category. For example, ibuprofen is in the GENERIC_NAME type in the MEDICATION category.
- Attribute: Information related to an entity, such as the dosage of a medication. For example, 200 mg is an attribute of the ibuprofen entity.
- Trait: Something that Amazon Comprehend Medical understands about an entity, based on context. For example, a medication has the NEGATION trait if a patient is not taking it.
- Relationship Type: The relationship between an entity and an attribute.
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