arXiv subjects

Ricardo Rodriguez

Publications and source records attributed to Ricardo Rodriguez.

11 recordsLinked to original sources

arXiv2026

Prognostic Value of Lung Ultrasound Biomarkers for Readmission Risk in Congestive Heart Failure: A Pilot Data-Driven Analysis

Hospital readmission within 30 days of discharge is a leading driver of morbidity, mortality, and avoidable healthcare expenditure in congestive heart failure (CHF). Current clinical risk stratification tools rely primarily on non-imaging data and exhibit limited predictive performance. Point-of-care lung ultrasound (LUS) offers a sensitive, noninvasive window into the pulmonary congestion that characterizes CHF decompensation, yet its prognostic utility for readmission prediction remains largely unexplored. We present a pilot feasibility study, the first systematic machine learning study using B-mode LUS acquired during hospitalization to predict 30-day CHF readmission. Quantitative spatiotemporal embeddings are extracted from a pretrained Temporal Shift Module (TSM) ResNet-18 encoder, and interpretable biomarker features are separately evaluated. Through structured ablations over lung view, temporal representation, multi-view fusion, and cross-lung augmentation, we identify the key imaging factors driving readmission risk. Our findings reveal that (1) dependent lower-lung regions (Left-3, Right-3) carry the strongest prognostic signal, consistent with their greater susceptibility to hydrostatic congestion; (2) temporal difference features between sequential examinations substantially outperform single-timepoint representations, highlighting the importance of capturing disease trajectory; and (3) multi-view feature concatenation yields the best overall performance, with our top MLP model achieving an F1 score of 0.80 (95% CI: 0.62-0.96). Biomarker analysis further reveals that pleural-line abnormalities, including breaks and indentations, are as informative as the canonical A-line and B-line markers. These results support POCUS-derived biomarkers as practical, interpretable tools for noninvasive CHF risk stratification.

eess.SP↗

arXiv2025

Label Uncertainty for Ultrasound Segmentation

In medical imaging, inter-observer variability among radiologists often introduces label uncertainty, particularly in modalities where visual interpretation is subjective. Lung ultrasound (LUS) is a prime example-it frequently presents a mixture of highly ambiguous regions and clearly discernible structures, making consistent annotation challenging even for experienced clinicians. In this work, we introduce a novel approach to both labeling and training AI models using expert-supplied, per-pixel confidence values. Rather than treating annotations as absolute ground truth, we design a data annotation protocol that captures the confidence that radiologists have in each labeled region, modeling the inherent aleatoric uncertainty present in real-world clinical data. We demonstrate that incorporating these confidence values during training leads to improved segmentation performance. More importantly, we show that this enhanced segmentation quality translates into better performance on downstream clinically-critical tasks-specifically, estimating S/F oxygenation ratio values, classifying S/F ratio change, and predicting 30-day patient readmission. While we empirically evaluate many methods for exposing the uncertainty to the learning model, we find that a simple approach that trains a model on binarized labels obtained with a (60%) confidence threshold works well. Importantly, high thresholds work far better than a naive approach of a 50% threshold, indicating that training on very confident pixels is far more effective. Our study systematically investigates the impact of training with varying confidence thresholds, comparing not only segmentation metrics but also downstream clinical outcomes. These results suggest that label confidence is a valuable signal that, when properly leveraged, can significantly enhance the reliability and clinical utility of AI in medical imaging.

eess.IV↗

arXiv2025

Existence of a robust optimal control process for efficient measurements in a two-qubit system

The verification of quantum entanglement is essential for quality control in quantum communication. In this work, we propose an efficient protocol to directly verify the two-qubit entanglement of a known target state through a single expectation value measurement. Our method provides exact entanglement quantification using the currencence measure without performing quantum state tomography. We prove the existence of a unitary transformation that drives the initial state of a two-qubit system to a designated final state, where the trace over a chosen observable directly yields the concurrence of the initial state. Furthermore, we implement an optimal control process of that transformation and demonstrate its effectiveness through numerical simulations. We also show that this process is robust to environmental noise. Our approach offers advantages in directly verifying entanglement with low circuit depth, making it suitable for industrial-scale quality control of entanglement generation. Our results, presented here, provide mathematical justification for our earlier computational experiments.

quant-ph↗

arXiv2024

LEARNER: Contrastive Pretraining for Learning Fine-Grained Patient Progression from Coarse Inter-Patient Labels

Predicting whether a treatment leads to meaningful improvement is a central challenge in personalized medicine, particularly when disease progression manifests as subtle visual changes over time. While data-driven deep learning (DL) offers a promising route to automate such predictions, acquiring large-scale longitudinal data for each individual patient remains impractical. To address this limitation, we explore whether inter-patient variability can serve as a proxy for learning intra-patient progression. We propose LEARNER, a contrastive pretraining framework that leverages coarsely labeled inter-patient data to learn fine-grained, patient-specific representations. Using lung ultrasound (LUS) and brain MRI datasets, we demonstrate that contrastive objectives trained on coarse inter-patient differences enable models to capture subtle intra-patient changes associated with treatment response. Across both modalities, our approach improves downstream classification accuracy and F1-score compared to standard MSE pretraining, highlighting the potential of inter-patient contrastive learning for individualized outcome prediction.

eess.IV↗

arXiv2022

Scalable Query Answering under Uncertainty to Neuroscientific Ontological Knowledge: The NeuroLang Approach

Researchers in neuroscience have a growing number of datasets available to study the brain, which is made possible by recent technological advances. Given the extent to which the brain has been studied, there is also available ontological knowledge encoding the current state of the art regarding its different areas, activation patterns, key words associated with studies, etc. Furthermore, there is an inherent uncertainty associated with brain scans arising from the mapping between voxels -- 3D pixels -- and actual points in different individual brains. Unfortunately, there is currently no unifying framework for accessing such collections of rich heterogeneous data under uncertainty, making it necessary for researchers to rely on ad hoc tools. In particular, one major weakness of current tools that attempt to address this kind of task is that only very limited propositional query languages have been developed. In this paper, we present NeuroLang, an ontology language with existential rules, probabilistic uncertainty, and built-in mechanisms to guarantee tractable query answering over very large datasets. After presenting the language and its general query answering architecture, we discuss real-world use cases showing how NeuroLang can be applied to practical scenarios for which current tools are inadequate.

cs.AI↗

arXiv2021

Simplified Kripke semantics for K45-like Godel modal logics and its axiomatic extensions

In this paper, we provide simplified semantics for the logic K45(G), i.e. the many-valued Godel counterpart of the classical modal logic K45. More precisely, we characterize K45(G) as the set of valid formulae of the class of possibilistic Godel Kripke Frames where W is a non-empty set of worlds and π: W \to [0, 1] is a possibility distribution on W.

math.LO↗

arXiv2020

An AI based talent acquisition and benchmarking for job

In a recruitment industry, selecting a best CV from a particular job post within a pile of thousand CV's is quite challenging. Finding a perfect candidate for an organization who can be fit to work within organizational culture is a difficult task. In order to help the recruiters to fill these gaps we leverage the help of AI. We propose a methodology to solve these problems by matching the skill graph generated from CV and Job Post. In this report our approach is to perform the business understanding in order to justify why such problems arise and how we intend to solve these problems using natural language processing and machine learning techniques. We limit our project only to solve the problem in the domain of the computer science industry.

cs.CY↗

arXiv2010

Event-by-Event Jet Quenching and Higher Fourier Moments of Hard Probes

We investigate the effect of event-by-event fluctuations of the fireball created in high energy nuclear collisions on hard probe observables. We show that spatial inhomogeneities lead to changes in the nuclear suppression factor of high momentum hadrons which can be absorbed in the quenching strength q. This can increase the theoretical uncertainty on extracted values of q by up to 50%. We also investigate effects on azimuthal asymmetries v2 and dihadron correlation functions. The latter show a promising residual signal of event-by-event quenching that might allow us to estimate the size of spatial inhomogeneities in the fireball from experimental data.

nucl-th↗

arXiv2010

Event-by-Event Jet Quenching

High momentum jets and hadrons can be used as probes for the quark gluon plasma (QGP) formed in nuclear collisions at high energies. We investigate the influence of fluctuations in the fireball on jet quenching observables by comparing propagation of light quarks and gluons through averaged, smooth QGP fireballs with event-by-event jet quenching using realistic inhomogeneous fireballs. We find that the transverse momentum and impact parameter dependence of the nuclear modification factor R_AA can be fit well in an event-by-event quenching scenario within experimental errors. However the transport coefficient qhat extracted from fits to the measured nuclear modification factor R_AA in averaged fireballs underestimates the value from event-by-event calculations by up to 50%. On the other hand, after adjusting qhat to fit R_AA in the event-by-event analysis we find residual deviations in the azimuthal asymmetry v_2 and in two-particle correlations, that provide a possible faint signature for a spatial tomography of the fireball.

nucl-th↗

arXiv2009

On the Minimum Many-Valued Modal Logic over a Finite Residuated Lattice

This paper deals with many-valued modal logics, based only on the necessity operator, over a residuated lattice. We focus on three basic classes, according to the accessibility relation, of Kripke frames: the full class of frames evaluated in the residuated lattice (and so defining the minimum modal logic), the ones evaluated in the idempotent elements and the ones evaluated in 0 and 1. We show how to expand an axiomatization, with canonical constants in the language, of a finite residuated lattice into one of the modal logic, for each one of the three basic classes of Kripke frames, over the very lattice. And we also give axiomatizations for the case of a finite MV chain but this time without canonical constants.

math.LO↗

arXiv2001

Drell-Yan Massive Lepton-Pair's Angular Distributions at Large $Q_T$

By measuring Drell-Yan massive lepton-pair's angular distributions, we can identify the polarization of the virtual photon of invariant mass $Q$ which decays immediately into the lepton-pair. In terms of a modified QCD factorization formula for Drell-Yan process, which is valid even if $Q_T\gg Q$, we calculate the massive lepton-pair's angular distributions at large $Q_T$. We find that the virtual photons produced at high $Q_T$ are more likely to be transversely polarized. We discuss the implications of this finding to the J/$ψ$ mesons' polarization measured recently at Fermilab.

hep-ph↗