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. Experiments on synthetic and medical cohorts for 30-day readmission prediction demonstrate the superiority of the proposed model over clinical and data mining baselines.
Original language | English |
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Title of host publication | Proceedings of the 2014 SIAM International Conference on Data Mining |
Editors | Zoran Obradovic, Mohammed J. Zaki, Pang Ning Tan, Arindam Banerjee, Chandrika Kamath |
Place of Publication | Philadelphia PA USA |
Publisher | Society for Industrial & Applied Mathematics (SIAM) |
Pages | 235-243 |
Number of pages | 9 |
Volume | 1 |
ISBN (Electronic) | 9781510811515, 9781611973440 |
DOIs | |
Publication status | Published - 2014 |
Externally published | Yes |
Event | SIAM International Conference on Data Mining 2014 - Philadelphia, United States of America Duration: 24 Apr 2014 → 26 Apr 2014 Conference number: 14th https://archive.siam.org/meetings/sdm14/ |
Conference
Conference | SIAM International Conference on Data Mining 2014 |
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Abbreviated title | SDM 2014 |
Country/Territory | United States of America |
City | Philadelphia |
Period | 24/04/14 → 26/04/14 |
Internet address |