17 citations · 26 across the 11 of their papers we have counts for
11 papers
An adaptive variance estimator for relative sparsity
Samuel Julian Weisenthal
An approach to inference for relative sparsity was developed in prior work, and an adaptive lasso asymptotic normality theorem was given there, but this theorem was not fully used…
Treatment, evidence, imitation, and chat
Samuel J. Weisenthal
Large language models are thought to have the potential to aid in medical decision making. This work investigates the degree to which this might be the case. We start with the trea…
The information mismatch, and how to fix it
Samuel J. Weisenthal, Amit K. Chowdhry
We live in unprecedented times in terms of our ability to use evidence to inform medical care. For example, we can perform data-driven post-test probability calculations. However,…
Optimality-based reward learning with applications to toxicology
Samuel J. Weisenthal, Matthew Eckard, Askhan Ertefaie +2
In toxicology research, experiments are often conducted to determine the effect of toxicant exposure on the behavior of mice, where mice are randomized to receive the toxicant or n…
ChatGPT and post-test probability
Samuel J. Weisenthal
Reinforcement learning-based large language models, such as ChatGPT, are believed to have potential to aid human experts in many domains, including healthcare. There is, however, l…
Inference for relative sparsity
Samuel J. Weisenthal, Sally W. Thurston, Ashkan Ertefaie
In healthcare, there is much interest in estimating policies, or mappings from covariates to treatment decisions. Recently, there is also interest in constraining these estimated p…
Relative Sparsity for Medical Decision Problems
Samuel J. Weisenthal, Sally W. Thurston, Ashkan Ertefaie
Existing statistical methods can estimate a policy, or a mapping from covariates to decisions, which can then instruct decision makers (e.g., whether to administer hypotension trea…
Selection of inverse gamma and half-t priors for hierarchical models: sensitivity and recommendations
Zachary Brehm, Aaron Wagner, Erik VonKaenel +5
While the importance of prior selection is well understood, establishing guidelines for selecting priors in hierarchical models has remained an active, and sometimes contentious, a…
Predicting Acute Kidney Injury at Hospital Re-entry Using High-dimensional Electronic Health Record Data
Samuel J. Weisenthal, Caroline Quill, Samir Farooq +2
Acute Kidney Injury (AKI), a sudden decline in kidney function, is associated with increased mortality, morbidity, length of stay, and hospital cost. Since AKI is sometimes prevent…
Revealing patterns in HIV viral load data and classifying patients via a novel machine learning cluster summarization method
Samir Farooq, Samuel J. Weisenthal, Melissa Trayhan +4
HIV RNA viral load (VL) is an important outcome variable in studies of HIV infected persons. There exists only a handful of methods which classify patients by viral load patterns.…
Sum of previous inpatient serum creatinine measurements predicts acute kidney injury in rehospitalized patients
Sam Weisenthal, Haofu Liao, Philip Ng +1
Acute Kidney Injury (AKI), the abrupt decline in kidney function due to temporary or permanent injury, is associated with increased mortality, morbidity, length of stay, and hospit…