20 citations · 35 across the 12 of their papers we have counts for
6 papers · 1 filter
Assessing the effectiveness of empirical calibration under different bias scenarios
Hon Hwang, Juan C Quiroz, Blanca Gallego
Background: Estimations of causal effects from observational data are subject to various sources of bias. One method of adjusting for the residual biases in the estimation of a tre…
Continuous Treatment Recommendation with Deep Survival Dose Response Function
Jie Zhu, Blanca Gallego
We propose a general formulation for continuous treatment recommendation problems in settings with clinical survival data, which we call the Deep Survival Dose Response Function (D…
Incorporating Uncertainty in Learning to Defer Algorithms for Safe Computer-Aided Diagnosis
Jessie Liu, Blanca Gallego, Sebastiano Barbieri
Deep neural networks are increasingly being used for computer-aided diagnosis, but erroneous diagnoses can be extremely costly for patients. We propose a learning to defer with unc…
Comparing Broadband ISP Performance using Big Data from M-Lab
Xiaohong Deng, Yun Feng, Thanchanok Sutjarittham +3
Comparing ISPs on broadband speed is challenging, since measurements can vary due to subscriber attributes such as operation system and test conditions such as access capacity, ser…
Dynamic prediction of time to event with survival curves
Jie Zhu, Blanca Gallego
With the ever-growing complexity of primary health care system, proactive patient failure management is an effective way to enhancing the availability of health care resource. One…
Causal inference for observational longitudinal studies using deep survival models
Jie Zhu, Blanca Gallego
Causal inference for observational longitudinal studies often requires the accurate estimation of treatment effects on time-to-event outcomes in the presence of time-dependent pati…