Extract clinical events relations

**Extract clinical events relations:**

This model can identify and contextualize clinical events entities from clinical documentation, assign assertion statuses and determine temporal relations between those.

**Key Features**
- The model assigns assertion statuses like 'absent,' 'present,' 'conditional,' 'associated_with_someone_else,' 'hypothetical,' and 'possible' to these entities and identifies temporal relations among events, categorizing them as occurring 'AFTER,' 'BEFORE,' or in 'OVERLAP' with each other.
- This model excels in complex healthcare scenarios that require a deep understanding of clinical narratives. For instance, it can be used in predictive analytics to foresee patient risks based on historical and real-time data, thereby aiding in personalized treatment planning. It also finds utility in case management, where it can map out the entire patient journey, from admission through discharge, by determining the temporal relationships between various clinical events.

**Covered entities**: DATE, TIME, PROBLEM, TEST, TREATMENT, OCCURENCE, CLINICAL_DEPT, EVIDENTIAL, DURATION, FREQUENCY, ADMISSION, DISCHARGE.
**Relations**: AFTER, BEFORE, OVERLAP
**Assertion statuses**: absent, present, conditional, associated_with_someone_else, hypothetical, possible.

**Additional Model Information**
- [Industry Use-Case Demo](-)
- [Full model info on John Snow Labs Models Hub](https://nlp.johnsnowlabs.com/2023/06/17/explain_clinical_doc_era_en.html)
- **Domain:** Clinical Text Analysis
- **Subdomain:** Clinical Entity and Event Analysis

**How to run this model:**
1. Acquire a John Snow Labs license from [Sales](mailto:[e-mail])
2. Import this listing.
3. See the attached notebook to deploy and use the model.

This model comes with optimized CPU and GPU builds. You can select which one to deploy via the notebook.