Entity Extraction / Recognition to train NLP models

Use entity extraction to glean vital insights from unstructured medical data.

What is NER

n healthcare, Named Entity Recognition (NER) finds and sorts key details like patient names and medical terms from texts that don’t have a set structure. This tool improves how we pull data, makes finding information easier, and boosts advanced AI technologies. It’s vital for healthcare institutions.

Our NER service is designed for healthcare organizations to uncover important information in unstructured data, like medical records or insurance papers. With our strong Natural Language Processing (NLP) knowledge, we offer deep insights and handle even the most challenging annotation projects, regardless of their size.

Our Expertise

Named Entity Recognition (NER)

Clinical NER API uses advanced NLP models to spot and pull out important medical details, their context, and relationships from large amounts of unstructured clinical data. It can precisely identify and organize medically relevant terms in healthcare data.

Identifying issues, anatomical structure, medications, and treatments in medical records, like EHRs, involves dealing with unstructured data. Turning this into organized information often demands extra steps and the expertise of domain specialists to pinpoint the necessary details.

Categories Detected by Medical NER API include:

MEDICAL_CONDITION: Identifies diseases, injuries, symptoms, or any health complaints.
MEDICATION: Names of drugs, treatments, or other therapeutic substances.
ANATOMY: Terms related to body parts, organs, or anatomical structures.
TEST_RESULT: Highlights outcomes from medical tests.

PROCEDURE: Identifies medical interventions, tests, or operations.
PERSON: Identifies individuals involved in the patient’s care or personal life.
TIME: Identifies time-related references, such as durations, frequencies, or specific dates.

Examples

1. Clinical Entity Recognition

Health records contain a huge amount of medical data, mostly unorganized. Annotating medical entities helps organize this data neatly.

2. Attribution

2.1 Medicine Attributes

Most medical records include medication details and features, which are essential for clinical practice. We can identify and label these medication attributes by following set rules.

2.2 Lab Data Attributes

Medical records’ lab data comes with unique details. We identify and mark these lab data specifics while adhering to strict guidelines.

2.3 Body Measurement Attributes

Medical records document body measurements, like vital signs, with specific attributes. We highlight and label these body measurement details accurately.

3. Oncology Specific NER

Beyond basic medical NER annotations, we cover specialized fields like oncology and radiology. In oncology, we annotate specific entities such as types and stages of cancer, tumor characteristics, treatments, Cancer Medicine, Cancer Surgery, Radiation, Gene Studied, Variation Code, and Body Site.

4. Adverse Effect NER & Relationship

We go beyond identifying and annotating clinical details and their connections. Our process includes noting the side effects of the drug or procedure. Our approach includes tagging these adverse effects and their causes and mapping out their relationships.

5. Assertion Status

Our work extends to classifying the status, negation, and subjects related to clinical entities. We provide a deeper understanding of these clinical entities.