Structured data modeling involves creating schemas, tables, columns, keys, and relationships that define how data is stored and accessed in a relational database management system (RDBMS).

Bring order to unstructured data: If unstructured data has clinical or business value, it will benefit from normalization, which aims to make it look more like structured data. “Given the sheer volume ...

This is how modern AI databases work with the types of unstructured data mentioned above. Instead of looking for exact matches, these databases look for results that are similar or “close enough.” ...

Unstructured data is any data that does not follow a predefined schema, format, or structure. It can include text, images, audio, video, social media, web pages, and more. Unstructured data can ...

Natural Language Processing (NLP): AI uses NLP to process and analyze unstructured text data, such as Facebook posts and customer reviews, to extract sentiments and meaning.

Developers and data scientists use generative AI and large language models (LLMs) to query volumes of documents and unstructured data. Open source LLMs, including Dolly 2.0, EleutherAI Pythia ...

Healthcare data often exists in a variety of formats, ranging from structured data, such as lab results, to unstructured formats, such as physician notes and clinical reports. By using NLP and ...

In this article, we look at structured data, unstructured data, and how semi-structured data brings some order from potential chaos. And brings benefits to organisations that want to gain value ...

Our results clearly demonstrate that unstructured data obtained from the clinical notes by NLP are superior to the use of structured data for determining a quantitative estimate of smoking behaviors.

The unstructured data is then processed by Veradigm’s NLP models, developed by clinicians and data scientists to extract crucial clinical insights into structured data tables.