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

Confounding variables are a well known source of nuisance in biomedicalstudies. They present an even greater challenge when we combine them withblack-box machine learning techniques that operate on raw data. This workpresents two case studies. In one, we discovered biases arising from systematicerrors in the data generation process. In the other, we found a spurious sourceof signal unrelated to the prediction task at hand. In both cases, ourprediction models performed well but under careful examination hiddenconfounders and biases were revealed. These are cautionary tales on the limitsof using machine learning techniques on raw data from scientific experiments.

It's easy to fool yourself: Case studies on identifying bias and confounding in bio-medical datasets