=Paper=
{{Paper
|id=Vol-1663/invited-abstract-1
|storemode=property
|title=None
|pdfUrl=https://ceur-ws.org/Vol-1663/bmaw2016_invited-abstract-1.pdf
|volume=Vol-1663
}}
==None==
<pdf width="1500px">https://ceur-ws.org/Vol-1663/bmaw2016_invited-abstract-1.pdf</pdf>
<pre>
 Scalable Joint Modeling of Longitudinal and Point Process Data for
Disease Trajectory Prediction and Improving Management of Chronic
                           Kidney Disease



     Joseph Futoma                      Mark Sendak                C. Blake Cameron            Katherine Heller
Dept. of Statistical Science   Institute for Health Innovation   Division of Nephrology    Dept. of Statistical Science
Duke University Durham,              School of Medicine             Duke University             Duke University
         NC 27707                      Duke University             Durham, NC 27707           Durham, NC 27707
                                     Durham, NC 27707



                                                       Abstract


    A major goal in personalized medicine is the ability to provide individualized predictions about the future
    trajectory of a disease. Moreover, for many complex chronic diseases, patients simultaneously have additional
    comorbid conditions. Accurate determination of the risk of developing serious complications associated with a
    disease or its comorbidities may be more clinically useful than prediction of future disease trajectory in such
    cases. We propose a novel probabilistic generative model that can provide individualized pre dictions of future
    disease progression while jointly modeling the pattern of related recurrent adverse events. We fit our model
    using a scalable variational inference algorithm and apply our method to a large dataset of longitudinal
    electronic patient health records. Our model gives superior performance in terms of both prediction of future
    disease trajectories and of future serious events when compared to non-joint models. Our predictions are
    currently being utilized by our local accountable care organization during chart reviews of high risk patients.


This poster from the UAI 2016 conference was given as an invited presentation at the Bayesian Modeling Applications
Workshop




                                                 BMAW 2016 - Page 56 of 59

</pre>