67 citations · 100 across the 15 of their papers we have counts for
15 papers
Explainable Diabetic Retinopathy Classification Using Vision Foundation Models
Abhishek Verma, Anila Krishna, Abhishek Gajanan Bankar +1
Diabetic retinopathy (DR) is a leading cause of preventable blindness, and automated screening must be both accurate and trustworthy. We study an explainable DR classification fram…
Towards actionable hypotension prediction -- predicting catecholamine therapy initiation in the intensive care unit
Richard Koebe, Noah Saibel, Juan Miguel Lopez Alcaraz +2
Hypotension in critically ill ICU patients is common and life-threatening. Escalation to catecholamine therapy marks a key management step, with both undertreatment and overtreatme…
Predicting Chest Radiograph Findings from Electrocardiograms Using Interpretable Machine Learning
Julia Matejas, Olaf Żurawski, Nils Strodthoff +1
Purpose: Chest X-rays are essential for diagnosing pulmonary conditions, but limited access in resource-constrained settings can delay timely diagnosis. Electrocardiograms (ECGs),…
Benchmarking ECG FMs: A Reality Check Across Clinical Tasks
M A Al-Masud, Juan Miguel Lopez Alcaraz, Nils Strodthoff
The 12-lead electrocardiogram (ECG) is a long-standing diagnostic tool. Yet machine learning for ECG interpretation remains fragmented, often limited to narrow tasks or datasets. F…
Explainable machine learning for neoplasms diagnosis via electrocardiograms: an externally validated study
Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp, Nils Strodthoff
Background: Neoplasms are a major cause of mortality globally, where early diagnosis is essential for improving outcomes. Current diagnostic methods are often invasive, expensive,…
Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach
Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp, Nils Strodthoff
Background: Liver diseases present a significant global health challenge and often require costly, invasive diagnostics. Electrocardiography (ECG), a widely available and non-invas…
Abnormality Prediction and Forecasting of Laboratory Values from Electrocardiogram Signals Using Multimodal Deep Learning
Juan Miguel Lopez Alcaraz, Nils Strodthoff
This study investigates the feasibility of using electrocardiogram (ECG) data combined with basic patient metadata to estimate and monitor prompt laboratory abnormalities. We use t…
Estimation of Cardiac and Non-cardiac Diagnosis from Electrocardiogram Features
Juan Miguel Lopez Alcaraz, Nils Strodthoff
Ensuring timely and accurate diagnosis of medical conditions is paramount for effective patient care. Electrocardiogram (ECG) signals are fundamental for evaluating a patient's car…
CardioLab: Laboratory Values Estimation from Electrocardiogram Features - An Exploratory Study
Juan Miguel Lopez Alcaraz, Nils Strodthoff
Laboratory value represents a cornerstone of medical diagnostics, but suffers from slow turnaround times, and high costs and only provides information about a single point in time.…
Enhancing clinical decision support with physiological waveforms -- a multimodal benchmark in emergency care
Juan Miguel Lopez Alcaraz, Hjalmar Bouma, Nils Strodthoff
Background: AI-driven prediction algorithms have the potential to enhance emergency medicine by enabling rapid and accurate decision-making regarding patient status and potential d…
Explaining Time Series Classification Predictions via Causal Attributions
Juan Miguel Lopez Alcaraz, Nils Strodthoff
Despite the excelling performance of machine learning models, understanding their decisions remains a long-standing goal. Although commonly used attribution methods from explainabl…
Prospects for AI-Enhanced ECG as a Unified Screening Tool for Cardiac and Non-Cardiac Conditions -- An Explorative Study in Emergency Care
Nils Strodthoff, Juan Miguel Lopez Alcaraz, Wilhelm Haverkamp
Current deep learning algorithms designed for automatic ECG analysis have exhibited notable accuracy. However, akin to traditional electrocardiography, they tend to be narrowly foc…
Diffusion-based Conditional ECG Generation with Structured State Space Models
Juan Miguel Lopez Alcaraz, Nils Strodthoff
Synthetic data generation is a promising solution to address privacy issues with the distribution of sensitive health data. Recently, diffusion models have set new standards for ge…
Diffusion-based Time Series Imputation and Forecasting with Structured State Space Models
Juan Miguel Lopez Alcaraz, Nils Strodthoff
The imputation of missing values represents a significant obstacle for many real-world data analysis pipelines. Here, we focus on time series data and put forward SSSD, an imputati…
From Interpretable Filters to Predictions of Convolutional Neural Networks with Explainable Artificial Intelligence
Shagufta Henna, Juan Miguel Lopez Alcaraz
Convolutional neural networks (CNN) are known for their excellent feature extraction capabilities to enable the learning of models from data, yet are used as black boxes. An interp…