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The meaning of medical content is highly affected by modifiers, such as negative or conditional assertions, which can have critical implications if misrepresented. Text Analytics for health supports four categories of assertion detection for entities in the text:
Text Analytics for health returns assertion modifiers, which are informative attributes assigned to medical concepts that provide a deeper understanding of the concepts’ context within the text. These modifiers are divided into four categories, each focusing on a different aspect and containing a set of mutually exclusive values. Only one value per category is assigned to each entity. The most common value for each category is the Default value. The service’s output response contains only assertion modifiers that are different from the default value. In other words, if no assertion is returned, the implied assertion is the default value.
CERTAINTY – provides information regarding the presence (present vs. absent) of the concept and how certain the text is regarding its presence (definite vs. possible).
An example of assertion detection is shown below where a negated entity is returned with a negative value for the certainty category:
{
"offset": 381,
"length": 3,
"text": "SOB",
"category": "SymptomOrSign",
"confidenceScore": 0.98,
"assertion": {
"certainty": "negative"
},
"name": "Dyspnea",
"links": [
{
"dataSource": "UMLS",
"id": "C0013404"
},
{
"dataSource": "AOD",
"id": "0000005442"
},
...
}
CONDITIONALITY – provides information regarding whether the existence of a concept depends on certain conditions.
ASSOCIATION – describes whether the concept is associated with the subject of the text or someone else.
TEMPORAL - provides additional temporal information for a concept detailing whether it is an occurrence related to the past, present, or future.
Events
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