What is RAAPID?

For over 15 years, we have led the way in creating clinical NLP platforms and exploring their various applications, achieving remarkable levels of precision and accuracy in the process. Our primary expertise lies in accurately interpreting unstructured medical notes on a large scale, having tested our systems against billions of genuine clinical documents. We have developed AI that not only provides output but also elaborates with context, reasoning, and supporting evidence. Our NLP technology is enriched with over 4 million entities and more than 50 million relationships, thanks to the integration of extensive medical knowledge. By employing advanced Machine Learning (ML) and Deep Learning (DL) models, we have constructed a robust NLP framework built upon comprehensive ontologies and specialized clinician terminologies. This allows us to effectively comprehend, interpret, and extract meaningful insights from the often inconsistent and non-standard data present in medical records. Our clinical domain experts continuously enhance our NLP capabilities by integrating knowledge graphs that detail the connections between all clinical entities, ensuring our systems remain at the forefront of medical data interpretation. With this relentless pursuit of innovation, we are poised to shape the future of clinical NLP further.

Integrations

Offers API?:
Yes, RAAPID provides an API
No integrations listed.

Screenshots and Video

RAAPID Screenshot 1

Company Facts

Company Name:
RAAPID INC
Date Founded:
2022
Company Location:
United States
Company Website:
www.raapidinc.com
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Product Details

Deployment
SaaS
Training Options
Documentation Hub
Online Training
Webinars
On-Site Training
Video Library
Support
Standard Support
Web-Based Support

Product Details

Target Company Sizes
Individual
1-10
11-50
51-200
201-500
501-1000
1001-5000
5001-10000
10001+
Target Organization Types
Mid Size Business
Small Business
Enterprise
Freelance
Nonprofit
Government
Startup
Supported Languages
English

RAAPID Categories and Features

Natural Language Processing Software

Co-Reference Resolution
In-Database Text Analytics
Named Entity Recognition
Natural Language Generation (NLG)
Open Source Integrations
Parsing
Part-of-Speech Tagging
Sentence Segmentation
Stemming/Lemmatization
Tokenization