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20212025
most citedMultimodal Interpretable Data-Driven Models for Early Prediction of Antimicrobial Multidrug Resistance Using Multivariate Time-Series

1 citations · 2 across the 3 of their papers we have counts for

collaborators

4 papers

cs.LG2025

Early Detection of Multidrug Resistance Using Multivariate Time Series Analysis and Interpretable Patient-Similarity Representations

Óscar Escudero-Arnanz, Antonio G. Marques, Inmaculada Mora-Jiménez +2

Background and Objectives: Multidrug Resistance (MDR) is a critical global health issue, causing increased hospital stays, healthcare costs, and mortality. This study proposes an i…

cs.LG20241 cited

Explainable Artificial Intelligence Techniques for Irregular Temporal Classification of Multidrug Resistance Acquisition in Intensive Care Unit Patients

Óscar Escudero-Arnanz, Cristina Soguero-Ruiz, Joaquín Álvarez-Rodríguez +1

Antimicrobial Resistance represents a significant challenge in the Intensive Care Unit (ICU), where patients are at heightened risk of Multidrug-Resistant (MDR) infections-pathogen…

cs.LG20241 cited

Multimodal Interpretable Data-Driven Models for Early Prediction of Antimicrobial Multidrug Resistance Using Multivariate Time-Series

Sergio Martínez-Agüero, Antonio G. Marques, Inmaculada Mora-Jiménez +2

Electronic health records (EHR) is an inherently multimodal register of the patient's health status characterized by static data and multivariate time series (MTS). While MTS are a…

cs.LG2021

Predicting Clinical Deterioration in Hospitals

Laleh Jalali, Hsiu-Khuern Tang, Richard H. Goldstein +1

Responding rapidly to a patient who is demonstrating signs of imminent clinical deterioration is a basic tenet of patient care. This gave rise to a patient safety intervention phil…