activity
20172023
most citedAdversarial Attacks Against Medical Deep Learning Systems

110 citations · 138 across the 9 of their papers we have counts for

collaborators

9 papers

cs.AI2023

Construction of extra-large scale screening tools for risks of severe mental illnesses using real world healthcare data

Dianbo Liu, Karmel W. Choi, Paulo Lizano +4

Importance: The prevalence of severe mental illnesses (SMIs) in the United States is approximately 3% of the whole population. The ability to conduct risk screening of SMIs at larg…

cs.LG2022

Identifying Heterogeneous Treatment Effects in Multiple Outcomes using Joint Confidence Intervals

Peniel N. Argaw, Elizabeth Healey, Isaac S. Kohane

Heterogeneous treatment effects (HTEs) are commonly identified during randomized controlled trials (RCTs). Identifying subgroups of patients with similar treatment effects is of hi…

q-bio.QM2019

Cross-modal representation alignment of molecular structure and perturbation-induced transcriptional profiles

Samuel G. Finlayson, Matthew B. A. McDermott, Alex V. Pickering +2

Modeling the relationship between chemical structure and molecular activity is a key goal in drug development. Many benchmark tasks have been proposed for molecular property predic…

cs.CV2018★ 22 cited

Towards generative adversarial networks as a new paradigm for radiology education

Samuel G. Finlayson, Hyunkwang Lee, Isaac S. Kohane +1

Medical students and radiology trainees typically view thousands of images in order to "train their eye" to detect the subtle visual patterns necessary for diagnosis. Nevertheless,…

q-bio.QM2018★ 3 cited

Learning Contextual Hierarchical Structure of Medical Concepts with Poincairé Embeddings to Clarify Phenotypes

Brett K. Beaulieu-Jones, Isaac S. Kohane, Andrew L. Beam

Biomedical association studies are increasingly done using clinical concepts, and in particular diagnostic codes from clinical data repositories as phenotypes. Clinical concepts ca…

stat.CO2018★ 1 cited

A Fast Divide-and-Conquer Sparse Cox Regression

Yan Wang, Nathan Palmer, Qian Di +3

We propose a computationally and statistically efficient divide-and-conquer (DAC) algorithm to fit sparse Cox regression to massive datasets where the sample size is exceedin…