Keeping up with Innovation: A Predictive Framework for Modeling Healthcare Data with Evolving Clinical Interventions. Gupta, S., Rana, S., Phung, D., & Venkatesh, S. In Proceedings of the SIAM International Conference on Data Mining, pages 235-243, Philadelphia, USA, 2014.
abstract   bibtex   
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. Efficient inference is derived. Experiments on synthetic and medical cohorts for 30-day readmission prediction demonstrate the superiority of the proposed model over clinical and data mining baselines.
@InProceedings{gupta_rana_phung_venkatesh_sdm14,
  Title                    = {Keeping up with Innovation: A Predictive Framework for Modeling Healthcare Data with Evolving Clinical Interventions},
  Author                   = {Gupta, S. and Rana, S. and Phung, D. and Venkatesh, S.},
  Booktitle                = {Proceedings of the SIAM International Conference on Data Mining},
  Year                     = {2014},

  Address                  = {Philadelphia, USA},
  Pages                    = {235-243},

  Abstract                 = {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. Efficient inference is derived. Experiments on synthetic and medical cohorts for 30-day readmission prediction demonstrate the superiority of the proposed model over clinical and data mining baselines.},
  Addendum                 = {\textbf{[h5 = 35]}},
  Owner                    = {sunilg},
  Timestamp                = {2014.11.11}
}
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