A Probabilistic Graphical Model for Individualizing Prognosis in Chronic, Complex Diseases

Peter Schulam, Suchi Saria

Research output: Contribution to journalArticle

Abstract

Making accurate prognoses in chronic, complex diseases is challenging due to the wide variation in expression across individuals. In many such diseases, the notion of subtypes-subpopulations that share similar symptoms and patterns of progression-have been proposed. We develop a probabilistic model that exploits the concept of subtypes to individualize prognoses of disease trajectories. These subtypes are learned automatically from data. On a new individual, our model incorporates static and time-varying markers to dynamically update predictions of subtype membership and provide individualized predictions of disease trajectory. We use our model to tackle the problem of predicting lung function trajectories in scleroderma, an autoimmune disease, and demonstrate improved predictive performance over existing approaches.

Original languageEnglish (US)
Pages (from-to)143-144
Number of pages2
JournalAMIA ... Annual Symposium proceedings. AMIA Symposium
Volume2015
StatePublished - Jan 1 2015

ASJC Scopus subject areas

  • Medicine(all)

Fingerprint Dive into the research topics of 'A Probabilistic Graphical Model for Individualizing Prognosis in Chronic, Complex Diseases'. Together they form a unique fingerprint.

Cite this