2 citations · 2 across the 4 of their papers we have counts for
4 papers
LLMs Understand Glass-Box Models, Discover Surprises, and Suggest Repairs
Benjamin J. Lengerich, Sebastian Bordt, Harsha Nori +4
We show that large language models (LLMs) are remarkably good at working with interpretable models that decompose complex outcomes into univariate graph-represented components. By…
Diagnosis Uncertain Models For Medical Risk Prediction
Alexander Peysakhovich, Rich Caruana, Yin Aphinyanaphongs
We consider a patient risk models which has access to patient features such as vital signs, lab values, and prior history but does not have access to a patient's diagnosis. For exa…
Observational Causal Inference in Novel Diseases: A Case Study of COVID-19
Alexander Peysakhovich, Yin Aphinyanaphongs
A key issue for all observational causal inference is that it relies on an unverifiable assumption - that observed characteristics are sufficient to proxy for treatment confounding…
Estimating Discontinuous Time-Varying Risk Factors and Treatment Benefits for COVID-19 with Interpretable ML
Benjamin Lengerich, Mark E. Nunnally, Yin Aphinyanaphongs +1
Treatment protocols, disease understanding, and viral characteristics changed over the course of the COVID-19 pandemic; as a result, the risks associated with patient comorbidities…