NER Annotated & Entity Linking Data

**Overview**

Shaip offers a vast repository of real-world medical data meticulously annotated with NER tags. Our datasets encompass a broad spectrum of clinical information, including but not limited to: problems/diagnoses, procedures, medications, lab results, vital signs, anatomical structures, medical devices, and more. Each entity within these records is accurately identified and linked to corresponding knowledge base entries, providing a deep level of semantic understanding.

By leveraging our pre-annotated data, researchers and developers can expedite the development of cutting-edge clinical NLP models, enabling groundbreaking advancements in medical data analysis and machine learning applications.

Our data is sourced from a diverse range of healthcare settings, ensuring a comprehensive and representative view of patient populations. With millions of patient records at your disposal, you can confidently build robust and reliable models.

All data is rigorously de-identified to protect patient privacy while preserving data integrity.

Our data is instrumental in

- Advancing medical data analysis
- Machine learning
- AI applications

**Use cases**

**Clinical Decision Support Systems**

- **Phenotype identification**: Identifying patient populations based on specific conditions, medications, or procedures.
- **Drug-drug interaction detection**: Identifying potential interactions between prescribed medications.
- **Clinical trial matching**: Finding eligible patients for clinical trials based on inclusion/exclusion criteria.

**Healthcare Analytics and Research**

- **Disease outbreak detection**: Tracking the spread of diseases based on location and patient data.
- **Population health management**: Analyzing patient populations to identify trends and risk factors.
- **Drug efficacy and safety analysis**: Evaluating drug performance and adverse events.

**Medical Information Extraction**

- **Clinical document summarization**: Creating concise summaries of patient records.
- **Information extraction**: Extracting specific data points from clinical notes for further analysis.
- **Knowledge graph construction**: Building knowledge graphs of medical entities and their relationships.

**Natural Language Processing**

- Model training: Developing NLP models for tasks like question answering, text classification, and information retrieval.
- Entity disambiguation: Resolving ambiguities in entity references within medical text.

**Product details**

Our datasets offer comprehensive extraction and tagging of key medical entities, including

- Problems/Diagnoses
- Procedures
- Medicines
- Lab data
- Body measurements
- Modifiers
- Anatomical structures
- Body functions
- Medical Devices
- Substance abuse, and more.

These pre-annotated real-world medical records are ideal for developing Clinical NLP models, providing a robust foundation for advanced medical data analysis and machine learning applications.