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

Background: Many efforts have been put into the use of automated approaches,such as natural language processing (NLP), to mine or extract data fromfree-text medical records to construct comprehensive patient profiles fordelivering better health-care. Reusing NLP models in new settings, however,remains cumbersome - requiring validation and/or retraining on new dataiteratively to achieve convergent results.  Objective: The aim of this work is to minimize the effort involved in reusingNLP models on free-text medical records.  Methods: We formally define and analyse the model adaptation problem inphenotype-mention identification tasks. We identify "duplicate waste" and"imbalance waste", which collectively impede efficient model reuse. We proposea phenotype embedding based approach to minimize these sources of waste withoutthe need for labelled data from new settings.  Results: We conduct experiments on data from a large mental health registryto reuse NLP models in four phenotype-mention identification tasks. Theproposed approach can choose the best model for a new task, identifying up to76% (duplicate waste), i.e. phenotype mentions without the need for validationand model retraining, and with very good performance (93-97% accuracy). It canalso provide guidance for validating and retraining the selected model fornovel language patterns in new tasks, saving around 80% (imbalance waste), i.e.the effort required in "blind" model-adaptation approaches.  Conclusions: Adapting pre-trained NLP models for new tasks can be moreefficient and effective if the language pattern landscapes of old settings andnew settings can be made explicit and comparable. Our experiments show that thephenotype-mention embedding approach is an effective way to model languagepatterns for phenotype-mention identification tasks and that its use can guideefficient NLP model reuse.

jmir medical informatics

Efficiently Reusing Natural Language Processing Models for  Phenotype-Mention Identification in Free-text Electronic Medical Records:  Methodology Study