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

Objective: Machine learning algorithms are now widely used in predictingacute events for clinical applications. While most of such predictionapplications are developed to predict the risk of a particular acute event atone hospital, few efforts have been made in extending the developed solutionsto other events or to different hospitals. We provide a scalable solution toextend the process of clinical risk prediction model development of multiplediseases and their deployment in different Electronic Health Records (EHR)systems.  Materials and Methods: We defined a generic process for clinical riskprediction model development. A calibration tool has been created to automatethe model generation process. We applied the model calibration process at fourhospitals, and generated risk prediction models for delirium, sepsis and acutekidney injury (AKI) respectively at each of these hospitals.  Results: The delirium risk prediction models achieved area under thereceiver-operating characteristic curve (AUROC) ranging from 0.82 to 0.95 overdifferent stages of a hospital stay on the test datasets of the four hospitals.The sepsis models achieved AUROC ranging from 0.88 to 0.95, and the AKI modelsachieved AUROC ranging from 0.85 to 0.92.  Discussion: The scalability discussed in this paper is based on buildingcommon data representations (syntactic interoperability) between EHRs stored indifferent hospitals. Semantic interoperability, a more challenging requirementthat different EHRs share the same meaning of data, e.g. a same lab codingsystem, is not mandated with our approach.  Conclusions: Our study describes a method to develop and deploy clinical riskprediction models in a scalable way. We demonstrate its feasibility bydeveloping risk prediction models for three diseases across four hospitals.

A scalable approach for developing clinical risk prediction applications in different hospitals