Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.28507

ECG for laboratory values:A comprehensive physiological and technical analysis

Walentin Palanchuk
View original source ↗Read source document ↗Export metadata ↓

Abstract

Laboratory measurements are essential for clinical decision making but typically require invasive blood sampling. Recent research suggests that ECG recordings may contain information beyond cardiac rhythm and provide information related to laboratory values. This thesis investigates NT-proBNP prediction from ECG recordings and patient metadata using a multimodal deep learning framework, while considering the physiological plausibility of ECG-based prediction. It also compares a 1-lead configuration using lead II with the corresponding 12-lead configuration. Additional experiments on glucose, hemoglobin, and potassium are presented in the appendix. Regression, binary classification, and three-class classification experiments were conducted using MIMIC-IV, with the three-class task based on clinically established thresholds. The 12-lead configuration consistently achieved better performance. Regression reached a mean absolute error of 534.98 pg/mL for 1-lead and 514.27 pg/mL for 12-lead. Binary classification achieved AUROCs of 0.849 and 0.876, respectively, while three-class classification achieved macro-averaged AUROCs of 0.738 and 0.752. The results indicate that precise NT-proBNP regression remains challenging, whereas broader concentration ranges can be classified more effectively. The findings are consistent with the physiological hypothesis that ECG characteristics and NT-proBNP concentrations reflect shared cardiovascular processes. ECG-based NT-proBNP prediction may therefore provide complementary information, particularly for classifying broader concentration ranges. However, laboratory measurements remain necessary, and validation on independent datasets is required.

Explore related subjects

cs.LGcs.AI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Walentin Palanchuk. 2026-10-20. ECG for laboratory values:A comprehensive physiological and technical analysis. https://arxiv.org/abs/2610.28507

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Simulating, Fast and Slow: Learning Policies for Black-Box Optimization

Simulators are vital in science and engineering, as they faithfully model the influence of design parameters on real-world observations. Optimizing design parameters to minimize a desired objective function is notoriously challenging since simulators are often non-differentiable black boxes. Moreover, each simulation incurs significant compute time, thus doing such optimizations naively may be prohibitively expensive. Furthermore, in experiment design settings, practitioners are typically required to solve the same optimization problem for various input distributions before proceeding to real-world experiments. Starting these related optimizations from scratch each time can be inefficient if the forward simulation model is expensive to evaluate. To address these challenges, this paper introduces a novel method for solving black-box optimization problems on various input distributions by learning a policy that guides the training of a differentiable surrogate. The surrogate's gradients are then used to optimize the simulation parameters with SGD. After training the policy, the cost for downstream optimization of problems involving black-box simulators is amortized, and we require up to $10\times$ fewer expensive simulator calls compared to baselines such as previous local surrogate-based approaches, numerical optimization, and Bayesian optimization.

cs.LG↗
arXiv2026

Physics Inspired Neural Networks Enable Efficient Graph Coloring

Combinatorial optimization problems near algorithmic phase transitions represent a fundamental challenge for both classical algorithms and machine learning approaches. Among them, graph coloring is a prototypical constraint satisfaction problem characterized by phase transitions that fragment the solution space into distinct clusters, making the search for valid solutions challenging. Here we introduce a physics-inspired neural framework that learns to solve large-scale graph coloring instances by combining graph neural networks with statistical-mechanics principles. Our approach integrates a planting-based supervised signal, symmetry-breaking regularization, and iterative noise-annealed neural dynamics to navigate complex solution landscapes. When the number of iterations scales quadratically with graph size, the learned solver successfully tackles increasingly challenging instances, even in regimes where finding valid solutions becomes particularly difficult, demonstrating its ability to extract meaningful information from the graph structure. The model generalizes from small training graphs to instances up to 20 times larger, demonstrating that neural architectures can learn scalable algorithmic strategies that remain effective in hard connectivity regions. These results establish a general framework for learning neural solvers that operate near fundamental phase boundaries in combinatorial optimization and inference.

cs.LG↗
arXiv2026

FiTv2: Scalable and Improved Flexible Vision Transformer for Diffusion Model

\textit{Nature is infinitely resolution-free}. In the context of this reality, existing diffusion models, such as Diffusion Transformers, often face challenges when processing image resolutions outside of their trained domain. To address this limitation, we conceptualize images as sequences of tokens with dynamic sizes, rather than traditional methods that perceive images as fixed-resolution grids. This perspective enables a flexible training strategy that seamlessly accommodates various aspect ratios during both training and inference, thus promoting resolution generalization and eliminating biases introduced by image cropping. On this basis, we present the \textbf{Flexible Vision Transformer} (FiT), a transformer architecture specifically designed for generating images with \textit{unrestricted resolutions and aspect ratios}. We further upgrade the FiT to FiTv2 with several innovative designs, includingthe Query-Key vector normalization, the AdaLN-LoRA module, a rectified flow scheduler, and a Logit-Normal sampler. Enhanced by a meticulously adjusted network structure, FiTv2 exhibits $2\times$ convergence speed of FiT. When incorporating advanced training-free extrapolation techniques, FiTv2 demonstrates remarkable adaptability in both resolution extrapolation and diverse resolution generation. Additionally, our exploration of the scalability of the FiTv2 model reveals that larger models exhibit better computational efficiency. Furthermore, we introduce an efficient post-training strategy to adapt a pre-trained model for the high-resolution generation. Comprehensive experiments demonstrate the exceptional performance of FiTv2 across a broad range of resolutions. We have released all the codes and models at https://github.com/whlzy/FiT to promote the exploration of diffusion transformer models for arbitrary-resolution image generation.

cs.LG↗