# Capturing health-related outcomes in unstructured data

**URL:** <https://discourse.cogstack.org/t/capturing-health-related-outcomes-in-unstructured-data/37>\
**Category:** Healthcare Use Cases\
**Created:** [April 1, 2022, 2:37pm UTC](https://discourse.cogstack.org/t/capturing-health-related-outcomes-in-unstructured-data/37 "2022-04-01T14:37:25Z")\
**Posts on this page:** 1\
**Page:** 1

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**Author:** ![Jthteo](https://avatars.discourse-cdn.com/v4/letter/j/898d66/32.png) [@Jthteo](https://discourse.cogstack.org/u/Jthteo)\
**Post date:** [April 1, 2022, 2:37pm UTC](https://discourse.cogstack.org/t/capturing-health-related-outcomes-in-unstructured-data/37/1 "2022-04-01T14:37:25Z")

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Traditionally health outcome capture has operated on two extremely structured approaches:

- Dichotomous (Live or Dead; Event happened or didn’t happen)
- Categorical (ordinal outcome scales like EQ5D5L)

Within unstructured text, both of those types may be mentioned explicitly as entities (ala Variable-Value pair) or they may only be captured through the value or subcomponents (“Walking without walking aid” implies “Fully independent”). This is of particular value when translating patient-reported narratives.