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

Patient subtyping based on temporal observations can lead to significantlynuanced subtyping that acknowledges the dynamic characteristics of diseases.Existing methods for subtyping trajectories treat the evolution of clinicalobservations as a homogeneous process or employ data available at regularintervals. In reality, diseases may have transient underlying states and astate-dependent observation pattern. In our paper, we present an approach tosubtype irregular patient data while acknowledging the underlying progressionof disease states. Our approach consists of two components: a probabilisticmodel to determine the likelihood of a patient's observation trajectory and amixture model to measure similarity between asynchronous patient trajectories.We demonstrate our model by discovering subtypes of progression to hemodynamicinstability (requiring cardiovascular intervention) in a patient cohort from amulti-institution ICU dataset. We find three primary patterns: two of whichshow classic signs of decompensation (rising heart rate with dropping bloodpressure), with one of these showing a faster course of decompensation than theother. The third pattern has transient period of low heart rate and bloodpressure. We also show that our model results in a 13% reduction in averagecross-entropy error compared to a model with no state progression whenforecasting vital signs.

Patient Subtyping with Disease Progression and Irregular Observation Trajectories