title: A Probabilistic Graphical Model for Individualizing Prognosis in Chronic, Complex Diseases.
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.
authors.affiliation: Dept. of Computer Science, Johns Hopkins University, Baltimore, MD.
authors.forename: Peter
authors.initials: P
authors.lastname: Schulam
authors.affiliation: Dept. of Computer Science, Johns Hopkins University, Baltimore, MD; Dept. of Health Policy and Management, Johns Hopkins University, Baltimore, MD.
authors.forename: Suchi
authors.initials: S
authors.lastname: Saria
mesh_terms: D015415:Biomarkers; D002908:Chronic Disease; D018450:Disease Progression; D006801:Humans; D008171:Lung Diseases / Q000175:diagnosis* / Q000628:therapy; D008954:Models, Biological; D015233:Models, Statistical; D057285:Precision Medicine; D011379:Prognosis; D012595:Scleroderma, Systemic / Q000175:diagnosis* / Q000628:therapy
chemical_list: D015415:Biomarkers