588 citations · 1.4k across the 16 of their papers we have counts for
17 papers
Critical Challenges and Guidelines in Evaluating Synthetic Tabular Data: A Systematic Review
Nazia Nafis, Inaki Esnaola, Alvaro Martinez-Perez +2
Generating synthetic tabular health data is challenging, and evaluating their quality is equally, if not more, complex. This systematic review highlights the critical importance of…
Reducing Class Bias In Data-Balanced Datasets Through Hardness-Based Resampling
Pawel Pukowski, Venet Osmani
Class-bias, that is class-wise performance disparities, is typically attributed to data imbalance and addressed through frequency-based resampling. However, we demonstrate that sub…
Multimodal Latent Fusion of ECG Leads for Early Assessment of Pulmonary Hypertension
Mohammod N. I. Suvon, Shuo Zhou, Prasun C. Tripathi +7
Recent advancements in early assessment of pulmonary hypertension (PH) primarily focus on applying machine learning methods to centralized diagnostic modalities, such as 12-lead el…
Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics Instability Detection
Mohammod N. I. Suvon, Prasun C. Tripathi, Wenrui Fan +7
Recent advancements in non-invasive detection of cardiac hemodynamic instability (CHDI) primarily focus on applying machine learning techniques to a single data modality, e.g. card…
MeDSLIP: Medical Dual-Stream Language-Image Pre-training with Pathology-Anatomy Semantic Alignment
Wenrui Fan, Mohammod N. I. Suvon, Shuo Zhou +6
Pathology and anatomy are two essential groups of semantics in medical data. Pathology describes what the diseases are, while anatomy explains where the diseases occur. They descri…
Mitigating Health Data Poverty: Generative Approaches versus Resampling for Time-series Clinical Data
Raffaele Marchesi, Nicolo Micheletti, Giuseppe Jurman +1
Several approaches have been developed to mitigate algorithmic bias stemming from health data poverty, where minority groups are underrepresented in training datasets. Augmenting t…