English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Tools annual

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The application of Artificial Intelligence (AI) in healthcare has beenrevolutionary, especially with the recent advancements in transformer-basedLarge Language Models (LLMs). However, the task of understanding unstructuredelectronic medical records remains a challenge given the nature of the records(e.g., disorganization, inconsistency, and redundancy) and the inability ofLLMs to derive reasoning paradigms that allow for comprehensive understandingof medical variables. In this work, we examine the power of coupling symbolicreasoning with language modeling toward improved understanding of unstructuredclinical texts. We show that such a combination improves the extraction ofseveral medical variables from unstructured records. In addition, we show thatthe state-of-the-art commercially-free LLMs enjoy retrieval capabilitiescomparable to those provided by their commercial counterparts. Finally, weelaborate on the need for LLM steering through the application of symbolicreasoning as the exclusive use of LLMs results in the lowest performance.

Coupling Symbolic Reasoning with Language Modeling for Efficient Longitudinal Understanding of Unstructured Electronic Medical Records