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 Plus annual

Presents the access of premium content as premium feature

Premium Content

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 Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Summarization of electronic health records (EHRs) can substantially minimize'screen time' for both patients as well as medical personnel. In recent yearssummarization of EHRs have employed machine learning pipelines using state ofthe art neural models. However, these models have produced less than adequateresults that are attributed to the difficulty of obtaining sufficient annotateddata for training. Moreover, the requirement to consider the entire content ofan EHR in summarization has resulted in poor performance due to the fact thatattention mechanisms in modern large language models (LLMs) adds a quadraticcomplexity in terms of the size of the input. We propose here a method thatmitigates these shortcomings by combining semantic search, retrieval augmentedgeneration (RAG) and question-answering using the latest LLMs. In our approachsummarization is the extraction of answers to specific questions that aredeemed important by subject-matter experts (SMEs). Our approach is quiteefficient; requires minimal to no training; does not suffer from the'hallucination' problem of LLMs; and it ensures diversity, since the summarywill not have repeated content but diverse answers to specific questions.

Question-Answering Based Summarization of Electronic Health Records using Retrieval Augmented Generation