49 citations · 125 across the 11 of their papers we have counts for
8 papers · 1 filter
Synthetic Health-related Longitudinal Data with Mixed-type Variables Generated using Diffusion Models
Nicholas I-Hsien Kuo, Louisa Jorm, Sebastiano Barbieri
This paper presents a novel approach to simulating electronic health records (EHRs) using diffusion probabilistic models (DPMs). Specifically, we demonstrate the effectiveness of D…
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…
Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIV
Nicholas I-Hsien Kuo, Federico Garcia, Anders Sönnerborg +6
Clinical data usually cannot be freely distributed due to their highly confidential nature and this hampers the development of machine learning in the healthcare domain. One way to…
Hierarchical Label-wise Attention Transformer Model for Explainable ICD Coding
Leibo Liu, Oscar Perez-Concha, Anthony Nguyen +2
International Classification of Diseases (ICD) coding plays an important role in systematically classifying morbidity and mortality data. In this study, we propose a hierarchical l…
The Health Gym: Synthetic Health-Related Datasets for the Development of Reinforcement Learning Algorithms
Nicholas I-Hsien Kuo, Mark N. Polizzotto, Simon Finfer +6
In recent years, the machine learning research community has benefited tremendously from the availability of openly accessible benchmark datasets. Clinical data are usually not ope…
Synthetic Acute Hypotension and Sepsis Datasets Based on MIMIC-III and Published as Part of the Health Gym Project
Nicholas I-Hsien Kuo, Mark Polizzotto, Simon Finfer +2
These two synthetic datasets comprise vital signs, laboratory test results, administered fluid boluses and vasopressors for 3,910 patients with acute hypotension and for 2,164 pati…
Predicting cardiovascular risk from national administrative databases using a combined survival analysis and deep learning approach
Sebastiano Barbieri, Suneela Mehta, Billy Wu +4
AIMS. This study compared the performance of deep learning extensions of survival analysis models with traditional Cox proportional hazards (CPH) models for deriving cardiovascular…
Benchmarking Deep Learning Architectures for Predicting Readmission to the ICU and Describing Patients-at-Risk
Sebastiano Barbieri, James Kemp, Oscar Perez-Concha +4
Objective: To compare different deep learning architectures for predicting the risk of readmission within 30 days of discharge from the intensive care unit (ICU). The interpretabil…