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

Machine learning (ML) approaches have demonstrated promising results in awide range of healthcare applications. Data plays a crucial role in developingML-based healthcare systems that directly affect people's lives. Many of theethical issues surrounding the use of ML in healthcare stem from structuralinequalities underlying the way we collect, use, and handle data. Developingguidelines to improve documentation practices regarding the creation, use, andmaintenance of ML healthcare datasets is therefore of critical importance. Inthis work, we introduce Healthsheet, a contextualized adaptation of theoriginal datasheet questionnaire ~\cite{gebru2018datasheets} forhealth-specific applications. Through a series of semi-structured interviews,we adapt the datasheets for healthcare data documentation. As part of theHealthsheet development process and to understand the obstacles researchersface in creating datasheets, we worked with three publicly-available healthcaredatasets as our case studies, each with different types of structured data:Electronic health Records (EHR), clinical trial study data, andsmartphone-based performance outcome measures. Our findings from theinterviewee study and case studies show 1) that datasheets should becontextualized for healthcare, 2) that despite incentives to adoptaccountability practices such as datasheets, there is a lack of consistency inthe broader use of these practices 3) how the ML for health community viewsdatasheets and particularly \textit{Healthsheets} as diagnostic tool to surfacethe limitations and strength of datasets and 4) the relative importance ofdifferent fields in the datasheet to healthcare concerns.

Healthsheet: Development of a Transparency Artifact for Health Datasets