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Found 7 papers, 0 papers with code
Distribution-free risk assessment of regression-based machine learning algorithms
We solve the risk-assessment problem using the conformal prediction approach, which provides prediction intervals that are guaranteed to contain the true label with a given probability.
Algorithmic Insurance
Specifically, we present an optimization formulation to estimate the risk exposure of a binary classification model given a pre-defined range of premiums.
Optimal Survival Trees
Tree-based models are increasingly popular due to their ability to identify complex relationships that are beyond the scope of parametric models.
From predictions to prescriptions: A data-driven response to COVID-19
Specifically, we propose a comprehensive data-driven approach to understand the clinical characteristics of COVID-19, predict its mortality, forecast its evolution, and ultimately alleviate its impact.
Personalized Treatment for Coronary Artery Disease Patients: A Machine Learning Approach
We are able to estimate with average R squared = 0. 801 the time from diagnosis to a potential adverse event (TAE) and gain accurate approximations of the counterfactual treatment effects.
Interpretable Clustering via Optimal Trees
State-of-the-art clustering algorithms use heuristics to partition the feature space and provide little insight into the rationale for cluster membership, limiting their interpretability.
Imputation of Clinical Covariates in Time Series
Missing data is a common problem in real-world settings and particularly relevant in healthcare applications where researchers use Electronic Health Records (EHR) and results of observational studies to apply analytics methods.
