OMOP/Documentation/CommonDataModel_Wiki_Files/StandardizedClinicalDataTables/NOTE_NLP.md

The NOTE_NLP table will encode all output of NLP on clinical notes. Each row represents a single extracted term from a note.

Field | Required | Type | Description
:------------------------------- | :-------- | :------------ | :---------------------------------------------------
|note_nlp_id | Yes | integer | A unique identifier for each term extracted from a note.|
|note_id | Yes | integer | A foreign key to the Note table note the term was |extracted from.|
|section_concept_id | Yes | integer | A foreign key to the predefined Concept in the Standardized Vocabularies representing the section of the extracted term.|
|snippet | No | varchar(250) | A small window of text surrounding the term.|
|offset | No | varchar(50) | Character offset of the extracted term in the input note.|
|lexical_variant | Yes | varchar(250) | Raw text extracted from the NLP tool.|
|note_nlp_concept_id | Yes | integer | A foreign key to the predefined Concept in the Standardized Vocabularies reflecting the normalized concept for the extracted term. Domain of the term is represented as part of the Concept table.|
|note_nlp_source_concept_id | Yes | integer | A foreign key to a Concept that refers to the code in the source vocabulary used by the NLP system|
|nlp_system | No | varchar(250) | Name and version of the NLP system that extracted the term.Useful for data provenance.|
|nlp_date | Yes | date | The date of the note processing.Useful for data provenance.|
|nlp_datetime | No | datetime | The date and time of the note processing. Useful for data provenance.|
|term_exists | No | varchar(1) | A summary modifier that signifies presence or absence of the term for a given patient. Useful for quick querying.|
|term_temporal | No | varchar(50) | An optional time modifier associated with the extracted term. (for now “past” or “present” only). Standardize it later.|
|term_modifiers | No | varchar(2000) | A compact description of all the modifiers of the specific term extracted by the NLP system. (e.g. “son has rash” ? “negated=no,subject=family, certainty=undef,conditional=false,general=false”).|

### Conventions

No.|Convention Description
:--------|:------------------------------------   
| 1  | Term_exists is defined as a flag that indicates if the patient actually has or had the condition. Any of the following modifiers would make Term_exists false:<br><ul><li>Negation = true</li><li>Subject = [anything other than the patient]</li><li>Conditional = true/li><li>Rule_out = true</li><li>Uncertain = very low certainty or any lower certainties</li><li>A complete lack of modifiers would make Term_exists true.</li></ul><br>For the modifiers that are there, they would have to have these values:<br><ul><li>Negation = false</li><li>Subject = patient</li><li>Conditional = false</li><li>Rule_out = false</li><li>Uncertain = true or high or moderate or even low (could argue about low)</li></ul>|
| 2  | Term_temporal is to indicate if a condition is “present” or just in the “past”. The following would be past:<br><ul><li>History = true</li><li>Concept_date = anything before the time of the report</li></ul>|
| 3  | Term_modifiers will concatenate all modifiers for different types of entities (conditions, drugs, labs etc) into one string. Lab values will be saved as one of the modifiers. A list of allowable modifiers (e.g., signature for medications) and their possible values will be standardized later. |