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

When a predictive model is in production, it must be monitored in real-timeto ensure that its performance does not suffer due to drift or abrupt changesto data. Ideally, this is done long before learning that the performance of themodel itself has dropped by monitoring outcome data. In this paper we considerthe problem of monitoring a predictive model that identifies the need forpalliative care currently in production at the Mayo Clinic in Rochester, MN. Weintroduce a framework, called \textit{Bayes Watch}, for detecting change-pointsin high-dimensional longitudinal data with mixed variable types and missingvalues and for determining in which variables the change-point occurred. BayesWatch fits an array of Gaussian Graphical Mixture Models to groupings ofhomogeneous data in time, called regimes, which are modeled as the observedstates of a Markov process with unknown transition probabilities. In doing so,Bayes Watch defines a posterior distribution on a vector of regime assignments,which gives meaningful expressions on the probability of every possiblechange-point. Bayes Watch also allows for an effective and efficient faultdetection system that assesses what features in the data where the mostresponsible for a given change-point.

Bayes Watch: Bayesian Change-point Detection for Process Monitoring with Fault Detection