  The recent wide adoption of Electronic Medical Records (EMR) presents great
opportunities and challenges for data mining. The EMR data is largely temporal,
often noisy, irregular and high dimensional. This paper constructs a novel
ordinal regression framework for predicting medical risk stratification from
EMR. First, a conceptual view of EMR as a temporal image is constructed to
extract a diverse set of features. Second, ordinal modeling is applied for
predicting cumulative or progressive risk. The challenges are building a
transparent predictive model that works with a large number of weakly
predictive features, and at the same time, is stable against resampling
variations. Our solution employs sparsity methods that are stabilized through
domain-specific feature interaction networks. We introduces two indices that
measure the model stability against data resampling. Feature networks are used
to generate two multivariate Gaussian priors with sparse precision matrices
(the Laplacian and Random Walk). We apply the framework on a large short-term
suicide risk prediction problem and demonstrate that our methods outperform
clinicians to a large-margin, discover suicide risk factors that conform with
mental health knowledge, and produce models with enhanced stability.
