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Found 5 papers, 1 papers with code
Predicting Antimicrobial Resistance in the Intensive Care Unit
Antimicrobial resistance (AMR) is a risk for patients and a burden for the healthcare system.
Designing metabolic division of labor in microbial communities
Specifically, we searched for communities able to survive under constraints (such as a limited number of reactions) that would not be sustainable by individual species.
Prescriptive Cluster-Dependent Support Vector Machines with an Application to Reducing Hospital Readmissions
We augment linear Support Vector Machine (SVM) classifiers by adding three important features: (i) we introduce a regularization constraint to induce a sparse classifier; (ii) we devise a method that partitions the positive class into clusters and selects a sparse SVM classifier for each cluster; and (iii) we develop a method to optimize the values of controllable variables in order to reduce the number of data points which are predicted to have an undesirable outcome, which, in our setting, coincides with being in the positive class.
Convergence of Parameter Estimates for Regularized Mixed Linear Regression Models
We establish that as the number of training samples grows large, the MIP solution converges to the true coefficient vectors in the absence of noise.
Predicting Chronic Disease Hospitalizations from Electronic Health Records: An Interpretable Classification Approach
To strike a balance between accuracy and interpretability of the prediction, which is important in a medical setting, we propose two novel methods: K-LRT, a likelihood ratio test-based method, and a Joint Clustering and Classification (JCC) method which identifies hidden patient clusters and adapts classifiers to each cluster.
