#InterSystems Natural Language Processing (NLP, iKnow)

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InterSystems IRIS Natural Language Processing (NLP), formerly known as iKnow, allows you to perform text analysis on unstructured data sources in a variety of natural languages without any prior knowledge of their content. It does this by applying language-specific rules that identify semantic entities. Because these rules are specific to the language, not the content, NLP can provide insight into the contents of texts without the use of a dictionary or ontology. Learn more.

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Presenter: Misha Bouzinier
Task: Gain an understanding of natural language processing and the current state of the art
Approach: Discuss how InterSystems iKnow technology fits into the NLP ecosystem and complements the output of other components such as Lucene and Stanford NLP tools

A 101 session on Natural Language Processing that positions Intersystems tools in the broader ecosystem Problem: we’ve been touting “unstructured data” for five years, but many people both internally and externally still don’t know what it means to “process natural language” in general and how iKnow and our upcoming UIMA capabilities fit in this NLP ecosystem. This session will describe what a number of common technologies offer and how bare-bone NLP output typically needs to be complemented with more classic analytics or inference tooling to get the value out.

Content related to this session, including slides, video and additional learning content can be found here.

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Presenter: Dirk Van Hyfte
Task: Leverage unstructured data to improve how clinicians deliver care
Approach: Give real-world examples of organizations that are benefiting from using their unstructured data

This session will feature real-world examples of how healthcare organizations can benefit from exposing unstructured data to clinicians at point-of-care as well as to clinical informatics building predictive models. Presenters are Wesley Williams, PhD, Vice President and Chief Information Officer, Mental Health Center of Denver; Augie Turano PhD. IT Director Veterans Informatics and Computer Infrastructure (VINCI); and Dirk Van Hyfte, MD, PhD, Senior Research Consultant.

Content related to this session, including slides, video and additional learning content can be found here.

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Introduction - Analyzing Textual Big Data

Big Data for Enriching Analytical Capabilities - Big data is revolutionizing the world of business intelligence and analytics. Gartner predicts that big data will drive $232 billion in spending through 2016, Wikibon claims that by 2017 big data revenue will have grown to $47.8 billion, and McKinsey Global Institute indicates that big data has the potential to increase the value of the US health care industry by $300 billion and to increase the industry value of Europe's public sector administration by Ä250 billion.

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Article
· Oct 21, 2015 1m read
Use Cases for Unstructured Data

Introduction

Experts estimate that 85% of all data exists in unstructured formats – held in e-mails, documents (contracts, memos, clinical notes, legal briefs), social media feeds, etc. Where structured data typically accounts for quantitative facts, the more interesting and potentially more valuable expert opinions and conclusions are often hidden in these unstructured formats. And with massive volumes of text being generated at unprecedented speed, there’s very little chance this information can be made useful without some process of synthesis or automation.

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As said in the previous article about the iris-fhir-generative-ai experiment, the project logs all events for analysis. Here we are going to discuss two types of analysis covered by analytics embedded in the project:

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I am trying to help another group within our organization access a SQL Table that I have created to populate Epic Department Data within our environment and came across the ability to use SQL Seach REST Interface using iKnow.

However, I am having issues trying to get it to work via POSTMAN before I hand off the solution...

the POST URL... https://<servername>/api/iKnow/latest/TESTCLIN/table/osuwmc_Epic_Clarity.DepartmentMaster/search

where osuwmc_Epic_Clarity.DepartmentMaster is the table

In the body...

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Introduction

To achieve optimized AI performance, robust explainability, adaptability, and efficiency in healthcare solutions, InterSystems IRIS serves as the core foundation for a project within the x-rAI multi-agentic framework. This article provides an in-depth look at how InterSystems IRIS empowers the development of a real-time health data analytics platform, enabling advanced analytics and actionable insights. The solution leverages the strengths of InterSystems IRIS, including dynamic SQL, native vector search capabilities, distributed caching (ECP), and FHIR interoperability. This innovative approach directly aligns with the contest themes of "Using Dynamic SQL & Embedded SQL," "GenAI, Vector Search," and "FHIR, EHR," showcasing a practical application of InterSystems IRIS in a critical healthcare context.

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