Keeping up with innovation: a predictive framework for modeling healthcare data with evolving clinical interventions
Contribuinte(s) |
Zaki,M Obradovic,Z Tan,PN Banerjee,A Kamath,C Parthasarathy,S |
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Data(s) |
01/01/2014
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Resumo |
Medical outcomes are inexorably linked to patient illness and clinical interventions. Interventions change the course of disease, crucially determining outcome. Traditional outcome prediction models build a single classifier by augmenting interventions with disease information. Interventions, however, differentially affect prognosis, thus a single prediction rule may not suffice to capture variations. Interventions also evolve over time as more advanced interventions replace older ones. To this end, we propose a Bayesian nonparametric, supervised framework that models a set of intervention groups through a mixture distribution building a separate prediction rule for each group, and allows the mixture distribution to change with time. This is achieved by using a hierarchical Dirichlet process mixture model over the interventions. The outcome is then modeled as conditional on both the latent grouping and the disease information through a Bayesian logistic regression. Experiments on synthetic and medical cohorts for 30-day readmission prediction demonstrate the superiority of the proposed model over clinical and data mining baselines. |
Identificador | |
Idioma(s) |
eng |
Publicador |
Society for Industrial and Applied Mathematics |
Relação |
http://dro.deakin.edu.au/eserv/DU:30082827/gupta-keepingup-2014.pdf http://dro.deakin.edu.au/eserv/DU:30082827/gupta-keepingup-evid--2014.pdf http://www.dx.doi.org/10.1137/1.9781611973440.27 |
Direitos |
2014, Society for Industrial and Applied Mathematics |
Tipo |
Conference Paper |