3 citations · 8 across the 5 of their papers we have counts for
6 papers
Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research
Krishna Padmanabhan, Minxin Lu, Dai Feng +4
We present a structured review of commonly used Explainable machine learning (XML) methodologies, including global and local interpretability tools such as SHapley Additive exPlana…
General OOD Detection via Model-aware and Subspace-aware Variable Priority
Min Lu, Hemant Ishwaran
Out-of-distribution (OOD) detection is essential for determining when a supervised model encounters inputs that differ meaningfully from its training distribution. While widely stu…
Model-independent variable selection via the rule-based variable priority
Min Lu, Hemant Ishwaran
While achieving high prediction accuracy is a fundamental goal in machine learning, an equally important task is finding a small number of features with high explanatory power. One…
Dynamic Competing Risk Modeling COVID-19 in a Pandemic Scenario
Min Lu, Hemant Ishwaran
The emergence of coronavirus disease 2019 (COVID-19) in the United States has forced federal and local governments to implement containment measures. Moreover, the severity of the…
A Machine Learning Alternative to P-values
Min Lu, Hemant Ishwaran
This paper presents an alternative approach to p-values in regression settings. This approach, whose origins can be traced to machine learning, is based on the leave-one-out bootst…
Estimating Individual Treatment Effect in Observational Data Using Random Forest Methods
Min Lu, Saad Sadiq, Daniel J. Feaster +1
Estimation of individual treatment effect in observational data is complicated due to the challenges of confounding and selection bias. A useful inferential framework to address th…