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Found 3 papers, 1 papers with code
Interpretable (not just posthoc-explainable) heterogeneous survivor bias-corrected treatment effects for assignment of postdischarge interventions to prevent readmissions
We used survival analysis to quantify the impact of postdischarge evaluation and management (E/M) services in preventing hospital readmission or death.
Interpretable (not just posthoc-explainable) medical claims modeling for discharge placement to prevent avoidable all-cause readmissions or death
We developed an inherently interpretable multilevel Bayesian framework for representing variation in regression coefficients that mimics the piecewise linearity of ReLU-activated deep neural networks.
Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization
However, sparsity in representation decoding does not necessarily imply sparsity in the encoding of representations from the original data features.
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