3 citations · 5 across the 6 of their papers we have counts for
6 papers
X-FEMR: A Token-level Explainable Approach for Electronic Health Records Foundation Models using Transformer-based Models
Jie Huang, Pengfei Yin, Zihan Xu +3
Foundation Models for Electronic Health Records (FEMRs) are pretrained on large-scale structured patient data, enabling them to convert longitudinal patient trajectories into gener…
Generating synthetic electronic health record data using agent-based models to evaluate machine learning robustness under mass casualty incidents
Roben Delos Reyes, Daniel Capurro, Nicholas Geard
ML models in healthcare are typically evaluated using curated real-world EHR data. A key limitation of such evaluations is that they may fail to assess the robustness of ML models…
Improving Text-based Early Prediction by Distillation from Privileged Time-Series Text
Jinghui Liu, Daniel Capurro, Anthony Nguyen +1
Modeling text-based time-series to make prediction about a future event or outcome is an important task with a wide range of applications. The standard approach is to train and tes…
Process Mining-Driven Analysis of the COVID19 Impact on the Vaccinations of Victorian Patients
Adriano Augusto, Timothy Deitz, Noel Faux +2
Process mining is a discipline sitting between data mining and process science, whose goal is to provide theoretical methods and software tools to analyse process execution data, k…
Quantifying machine learning-induced overdiagnosis in sepsis
Anna Fedyukova, Douglas Pires, Daniel Capurro
The proliferation of early diagnostic technologies, including self-monitoring systems and wearables, coupled with the application of these technologies on large segments of healthy…
A Survey on Deep Learning and Explainability for Automatic Report Generation from Medical Images
Pablo Messina, Pablo Pino, Denis Parra +7
Every year physicians face an increasing demand of image-based diagnosis from patients, a problem that can be addressed with recent artificial intelligence methods. In this context…