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/

Zendy Plus

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

Zendy Tools

Zendy Open

Modern machine learning approaches have led to performant diagnostic modelsfor a variety of health conditions. Several machine learning approaches, suchas decision trees and deep neural networks, can, in principle, approximate anyfunction. However, this power can be considered to be both a gift and a curse,as the propensity toward overfitting is magnified when the input data areheterogeneous and high dimensional and the output class is highly nonlinear.This issue can especially plague diagnostic systems that predict behavioral andpsychiatric conditions that are diagnosed with subjective criteria. An emergingsolution to this issue is crowdsourcing, where crowd workers are paid toannotate complex behavioral features in return for monetary compensation or agamified experience. These labels can then be used to derive a diagnosis,either directly or by using the labels as inputs to a diagnostic machinelearning model. This viewpoint describes existing work in this emerging fieldand discusses ongoing challenges and opportunities with crowd-powereddiagnostic systems, a nascent field of study. With the correct considerations,the addition of crowdsourcing to human-in-the-loop machine learning workflowsfor the prediction of complex and nuanced health conditions can acceleratescreening, diagnostics, and ultimately access to care.

A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health