C4.5: programs for machine learning
C4.5: programs for machine learning
The Journal of Machine Learning Research
Clustering with Bregman Divergences
The Journal of Machine Learning Research
Regression-based latent factor models
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
On smoothing and inference for topic models
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
Proceedings of the 2011 workshop on Data mining for medicine and healthcare
ICALP'06 Proceedings of the 33rd international conference on Automata, Languages and Programming - Volume Part II
Proceedings of the 2nd ACM SIGHIT International Health Informatics Symposium
Our data, ourselves: privacy via distributed noise generation
EUROCRYPT'06 Proceedings of the 24th annual international conference on The Theory and Applications of Cryptographic Techniques
Calibrating noise to sensitivity in private data analysis
TCC'06 Proceedings of the Third conference on Theory of Cryptography
ACM Transactions on Management Information Systems (TMIS) - Special Issue on Informatics for Smart Health and Wellbeing
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In healthcare-related studies, individual patient or hospital data are not often publicly available due to privacy restrictions, legal issues, or reporting norms. However, such measures may be provided at a higher or more aggregated level, such as state-level, county-level summaries or averages over health zones, such as hospital referral regions (HRR) or hospital service areas (HSA). Such levels constitute partitions over the underlying individual level data, which may not match the groupings that would have been obtained if one clustered the data based on individual-level attributes. Moreover, treating aggregated values as representatives for the individuals can result in the ecological fallacy. How can one run data mining procedures on such data where different variables are available at different levels of aggregation or granularity? In this article, we seek a better utilization of variably aggregated datasets, which are possibly assembled from different sources. We propose a novel cross-level imputation technique that models the generative process of such datasets using a Bayesian directed graphical model. The imputation is based on the underlying data distribution and is shown to be unbiased. This imputation can be further utilized in a subsequent predictive modeling, yielding improved accuracies. The experimental results using a simulated dataset and the Behavioral Risk Factor Surveillance System (BRFSS) dataset are provided to illustrate the generality and capabilities of the proposed framework.