Learning Interpretable Point-Based Clinical Risk Scores via Direct Optimization
Many clinical risk scores are deployed as additive rules with nonnegative integer points assigned to relevant binary predictive features. These integer weights not only make the score easier to use in practice but also promote sparsity in the resulting prediction model. Such risk scores are often derived by first fitting a regression model and then rounding the estimated coefficients to the nearest integer after appropriate scaling. This approach is computationally fast but does not guarantee optimality of the resulting score. Alternatively, one may search over all possible integer weights to directly optimize a value function by posing the problem as an integer programming task. However,...
| Authors | Ying Cui, Albert M Li, Vivek Charu et al. |
|---|---|
| Primary category | stat.ME |
| Published | 18 May 2026 |
| Last updated at source | 18 May 2026 |
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