activity
20192023
most citedTargeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis

20 citations · 35 across the 12 of their papers we have counts for

collaborators

12 papers

cs.DL2023★ 2 cited

The Cardiac Analytics and Innovation (CardiacAI) Data Repository: An Australian data resource for translational cardiovascular research

Victoria Blake, Louisa Jorm, Jennifer Yu +3

In Australia, cardiovascular diseases (CVD) are managed in a complex and fragmented healthcare system across multiple providers. A data repository that links data sources, and enab…

cs.LG2022★ 1 cited

Predicting adverse outcomes following catheter ablation treatment for atrial fibrillation

Juan C. Quiroz, David Brieger, Louisa Jorm +3

Objective: To develop prognostic survival models for predicting adverse outcomes after catheter ablation treatment for non-valvular atrial fibrillation (AF). Methods: We used a lin…

stat.ME2022

TreatmentEstimatoR: a Dashboard for Estimating Treatment Effects from Observational Health Data

Collin Sakal, Hon Hwang, Juan C Quiroz +1

Observational health data can be leveraged to measure the real-world use and potential benefits or risks of existing medical interventions. However, lack of programming proficiency…

stat.ME2021★ 6 cited

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…

stat.ML2021

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…

cs.LG2021★ 2 cited

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…

cs.PF2021

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…

stat.ML2021★ 2 cited

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…

stat.ML2021★ 2 cited

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…

stat.ME2019★ 20 cited

Targeted Estimation of Heterogeneous Treatment Effect in Observational Survival Analysis

Jie Zhu, Blanca Gallego

The aim of clinical effectiveness research using repositories of electronic health records is to identify what health interventions 'work best' in real-world settings. Since there…

cs.CY2019

Evaluating the Impact of Using GRASP Framework on Clinicians and Healthcare Professionals Decisions in Selecting Clinical Predictive Tools

Mohamed Khalifa, Farah Magrabi, Blanca Gallego

Background. When selecting predictive tools, clinicians and healthcare professionals are challenged with an overwhelming number of tools, most of which have never been evaluated fo…

cs.CY2019

Developing an Evidence-Based Framework for Grading and Assessment of Predictive Tools for Clinical Decision Support

Mohamed Khalifa, Farah Magrabi, Blanca Gallego

Background: Clinical predictive tools quantify contributions of relevant patient characteristics to derive likelihood of diseases or predict clinical outcomes. When selecting a pre…