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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

As healthcare is becoming more personalized, prediction models have become an important tool for decision support. In order to create sensible, understandable and useful prediction models, it is often necessary to include domain knowledge. This requires multi-disciplinary communication which has proven to be difficult, as the different parties involved are not always aware of each otherâs information needs. This paper presents the design process of a tool which supports the communication between clinical experts and data mining experts. Interviews and user tests were executed on four different sites and with 14 different users from both domains. The results from these user tests confirm the need for support on the communication process and provide evidence that the tool presented here indeed provides support by helping both parties to understand each otherâs information needs. The tool provides a graphical user interface which guides the users through the steps required to create a prediction model. The graphical user interface helps the clinical expert to understand the choices to be made which rely on his/her expertise, while the fact that a âquick-and-dirtyâ first version of a prediction model is generated in the process, helps the data mining expert to uncover all formal requirements for the model.

Support for the Inclusion of Domain Knowledge in Prediction Models - User Evaluations of a Tool for Generating Prediction Models for Serious Adverse Events in Oncology