67 citations · 117 across the 9 of their papers we have counts for
10 papers
Efficient estimation of weighted cumulative treatment effects by double/debiased machine learning
Shenbo Xu, Bang Zheng, Bowen Su +5
In empirical studies with time-to-event outcomes, investigators often leverage observational data to conduct causal inference on the effect of exposure when randomized controlled t…
RMExplorer: A Visual Analytics Approach to Explore the Performance and the Fairness of Disease Risk Models on Population Subgroups
Bum Chul Kwon, Uri Kartoun, Shaan Khurshid +7
Disease risk models can identify high-risk patients and help clinicians provide more personalized care. However, risk models developed on one dataset may not generalize across dive…
Bias Formulas for Violations of Proximal Identification Assumptions
Raluca Cobzaru, Roy Welsch, Stan Finkelstein +2
Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proxima…
Post-hoc loss-calibration for Bayesian neural networks
Meet P. Vadera, Soumya Ghosh, Kenney Ng +1
Bayesian decision theory provides an elegant framework for acting optimally under uncertainty when tractable posterior distributions are available. Modern Bayesian models, however,…
Blending Knowledge in Deep Recurrent Networks for Adverse Event Prediction at Hospital Discharge
Prithwish Chakraborty, James Codella, Piyush Madan +22
Deep learning architectures have an extremely high-capacity for modeling complex data in a wide variety of domains. However, these architectures have been limited in their ability…
Modeling Disease Progression Trajectories from Longitudinal Observational Data
Bum Chul Kwon, Peter Achenbach, Jessica L. Dunne +7
Analyzing disease progression patterns can provide useful insights into the disease processes of many chronic conditions. These analyses may help inform recruitment for prevention…
Dynamic Knowledge Distillation for Black-box Hypothesis Transfer Learning
Yiqin Yu, Xu Min, Shiwan Zhao +5
In real world applications like healthcare, it is usually difficult to build a machine learning prediction model that works universally well across different institutions. At the s…
DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways
Bum Chul Kwon, Vibha Anand, Kristen A Severson +5
Clinical researchers use disease progression models to understand patient status and characterize progression patterns from longitudinal health records. One approach for disease pr…
Unsupervised learning with contrastive latent variable models
Kristen Severson, Soumya Ghosh, Kenney Ng
In unsupervised learning, dimensionality reduction is an important tool for data exploration and visualization. Because these aims are typically open-ended, it can be useful to fra…
Simultaneous Modeling of Multiple Complications for Risk Profiling in Diabetes Care
Bin Liu, Ying Li, Soumya Ghosh +3
Type 2 diabetes mellitus (T2DM) is a chronic disease that often results in multiple complications. Risk prediction and profiling of T2DM complications is critical for healthcare pr…