Scalable joint modeling of longitudinal and point process data for disease trajectory prediction and improving management of chronic kidney disease

32nd Conference on Uncertainty in Artificial Intelligence 2016, UAI 2016

Volumn , Issue , 2016, Pages 222-231

  Futoma, Joseph ^a   Sendak, Mark ^b   Cameron, C Blake ^c   Heller, Katherine ^a  

^a Duke University   (United States)

^b DUKE UNIVERSITY SCHOOL OF MEDICINE   (United States)

^c DUKE UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; INFERENCE ENGINES; TRAJECTORIES;

CHRONIC KIDNEY DISEASE; CO-MORBID CONDITIONS; DISEASE PROGRESSION; GENERATIVE MODEL; HIGH-RISK PATIENTS; PERSONALIZED MEDICINES; TRAJECTORY PREDICTION; VARIATIONAL INFERENCE;

FORECASTING;

EID: 85002251471     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

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