Class: NamedEntityRecognitionService

NamedEntityRecognitionService()

Identifies named entities in text. It supports custom labels. This module is extremely versatile as the labels can be defined by the user.

Constructor

new NamedEntityRecognitionService()

Source:

Methods

add_label(label, definition)

Adds a TAG label and its description so that Soffos AI can identify the entities matching the tag
Parameters:
Name Type Description
label string The identifier of the label
definition string The definition of the label
Source:

call(user, text, labelsopt, engineopt) → {Promise.<Object>}

Parameters:
Name Type Attributes Default Description
user string The ID of the user accessing the Soffos API. Soffos assumes that the owner of the api is an application (app) and that app has users. Soffos API will accept any string.
text string Input text to be analyzed for named entities.
labels Object.<string, string> <optional>
 When providing labels, the module will extract entities that match your labels and descriptions. This gives enough flexibility to deal with any use-case.
engine string <optional>
 null The LLM engine to be used.
Source:
Returns:
named_entities - dictionary list
A list of dictionaries representing identified named entities. Each dictionary contains the following fields:
text: The text of the entity.
tag: Label of the entity.
span: A list with the start and end offset of the entity in the original text.
entity_counts - dictionary list
A list of dictionaries with entities and their counts. The dictionaries contain the following fields:
text: The name of the entity.
tag: Label of the entity.
count: Number of occurrences of the entity in the text.
Type
Promise.<Object>
Example
import { SoffosServices } from "soffosai";

const my_apiKey = "Token <put your api key here>";
const service = new SoffosServices.NamedEntityRecognitionService({apiKey:my_apiKey});
let response = await service.call(
    "client_whatever",
    "Patient Name: John Smith\nDate of Admission: June 15, 2023\n \
    Medical Record Number: 123456789\n\nChief Complaint:\n \
    The patient presents with severe abdominal pain and vomiting.\n\n \
    History of Present Illness:\n \
    Mr. Smith, a 45-year-old male, reports the onset of abdominal pain two days ago. \
    The pain is localized to the lower right quadrant and has been progressively worsening. \
    He has experienced multiple episodes of non-bloody vomiting. No significant \
    alleviating or exacerbating factors have been identified.\n\nPast Medical History:\n \
    The patient has a history of hypertension and type 2 diabetes mellitus. \
    He underwent an appendectomy in his childhood. He denies any known allergies or \
    previous surgeries.\n\n \
    Medications:\n \
    - Lisinopril 10mg once daily for hypertension\n \
    - Metformin 500mg twice daily for diabetes\n\n \
    Family History:\nThe patient's father had a history of myocardial infarction at the age of 60. \
    His mother is alive and well with no significant medical conditions. \
    There is no known family history of gastrointestinal disorders.\n\n \
    Social History:\nMr. Smith is a non-smoker and does not consume alcohol. \
    He is married and works as an accountant. He denies any illicit drug use.\n\n \
    Physical Examination:\n- Vital Signs: Blood pressure 130/80 mmHg, heart rate 82 bpm, \
    respiratory rate 16 breaths per minute, temperature 37.2°C (oral)\n \
    - General: The patient appears uncomfortable and is lying still in bed.\n \
    - Abdomen: There is tenderness in the right lower quadrant with guarding and \
    rebound tenderness. No palpable masses or organomegaly. Bowel sounds are diminished.\n\n \
    Assessment and Plan:\nThe patient's presentation is concerning for acute appendicitis. \
    He will undergo further diagnostic evaluation, including complete blood count, urinalysis, \
    and abdominal ultrasound. In the meantime, he will be kept NPO (nothing by mouth) and \
    started on intravenous fluids. Surgical consultation will be obtained for potential appendectomy.\n\n \
    Please note that this is a fictional clinical text generated for demonstration purposes only. \
    Any resemblance to actual patients or medical scenarios is purely coincidental.",
    {
        "Admission date": "date of admission"
    }
);
console.log(JSON.stringify(response, null, 2));

// returns
// {
//     "named_entities": [
//       {
//         "label": "Admission date",
//         "text": "June 15, 2023",
//         "span": [
//           44,
//           57
//         ]
//       }
//     ],
//     "entity_counts": [
//       {
//         "label": "Admission date",
//         "text": "June 15, 2023",
//         "count": 1
//       }
//     ],
//     "cost": {
//       "api_call_cost": 0.005,
//       "character_volume_cost": 0.1114,
//       "total_cost": 0.1164
//     },
//     "charged_character_count": 2228,
//     "unit_price": "0.000050"
// }

setInputConfigs(name, text, labels, engineopt)

Parameters:
Name Type Attributes Default Description
name string Reference name of this Service. It will be used by the Pipeline to reference this Service.
text string | InputConfig Input text to be analyzed for named entities.
labels Object.<string, string> | InputConfig When providing labels, the module will extract entities that match your labels and descriptions. This gives enough flexibility to deal with any use-case.
engine string <optional>
 null The LLM engine to be used.
Source: