9 citations · 17 across the 7 of their papers we have counts for
8 papers
SurvBench: A Standardised Preprocessing Pipeline for Multi-Modal Electronic Health Record Survival Analysis
Munib Mesinovic, Tingting Zhu
Deep-learning survival models for electronic health record (EHR) data are hard to compare across papers because the upstream preprocessing step, which includes cohort definition, t…
Causal Graph Neural Networks for Healthcare
Munib Mesinovic, Max Buhlan, Tingting Zhu
Healthcare artificial intelligence systems often degrade in performance when deployed across institutions, with documented performance drops and perpetuation of discriminatory patt…
DynaGraph: Interpretable Multi-Label Prediction from EHRs via Dynamic Graph Learning and Contrastive Augmentation
Munib Mesinovic, Soheila Molaei, Peter Watkinson +1
Learning from longitudinal electronic health records is limited if it does not capture the temporal trajectories of the patient's state in a clinical setting. Graph models allow us…
DySurv: dynamic deep learning model for survival analysis with conditional variational inference
Munib Mesinovic, Peter Watkinson, Tingting Zhu
Machine learning applications for longitudinal electronic health records often forecast the risk of events at fixed time points, whereas survival analysis achieves dynamic risk pre…
Explainable AI for clinical risk prediction: a survey of concepts, methods, and modalities
Munib Mesinovic, Peter Watkinson, Tingting Zhu
Recent advancements in AI applications to healthcare have shown incredible promise in surpassing human performance in diagnosis and disease prognosis. With the increasing complexit…
At-Admission Prediction of Mortality and Pulmonary Embolism in COVID-19 Patients Using Statistical and Machine Learning Methods: An International Cohort Study
Munib Mesinovic, Xin Ci Wong, Giri Shan Rajahram +8
By September, 2022, more than 600 million cases of SARS-CoV-2 infection have been reported globally, resulting in over 6.5 million deaths. COVID-19 mortality risk estimators are of…
XMI-ICU: Explainable Machine Learning Model for Pseudo-Dynamic Prediction of Mortality in the ICU for Heart Attack Patients
Munib Mesinovic, Peter Watkinson, Tingting Zhu
Heart attack remain one of the greatest contributors to mortality in the United States and globally. Patients admitted to the intensive care unit (ICU) with diagnosed heart attack…
Beta-VAE Reproducibility: Challenges and Extensions
Miroslav Fil, Munib Mesinovic, Matthew Morris +1
-VAE is a follow-up technique to variational autoencoders that proposes special weighting of the KL divergence term in the VAE loss to obtain disentangled representations. Unsup…