Publications citing HiRID, a high time-resolution ICU dataset v1.1.1

ICU Outcomes Prediction Using Optimized Extra Trees Classifier and Lasso-Based Feature Selection
Mohammad Ali Nematollahi, Mohammad Maftoun, Maryam Khademi et al. Lecture notes in computer science, 2026. Cites: paper

Machine learning-based prognostic models for hypertensive acute response for stroke patients in intensive care units and emergency departments
Mariem Abid, Philippe A. Jouvet, Elyes Oueslati et al. Elsevier eBooks, 2026. Cites: paper

Progress in the Application of Artificial Intelligence in Critical Care Nursing
李冰雁. Journal of Clinical Personalized Medicine, 2026. Cites: paper

Revolutionising sepsis management: A comprehensive review of artificial intelligence applications
Shahnawaz Ahmad, Ghizal Fatima, Ammar Mehdi Raza et al. Scripta Medica, 2026. Cites: paper

Unmeasured but Not Unbiased: The Missingness Demographic Leakage Audit (MDLA) for Calibration-Aware Fairness Evaluation in Critical Care Mortality Prediction
Krutarth Patel, Phanindra Beedala. SSRN Electronic Journal, 2026. Cites: paper

Data-driven forecasting of hydrogen production and demand via weather-clustered and day-weighted optimization for advance scheduling
Fan Liu, Ping Yang, Biao Wang et al. Renewable Energy, 2025. Cites: paper

Machine Learning for Intensive Care Unit Length-of-Stay Prediction: A Simulation-Based Approach to Bed Capacity Management
Sara Garber, Yarema Okhrin. Medical Decision Making, 2025. Cites: paper

Prediction value of respiratory permutation entropy for hemodynamic instability events in critically ill patients with sepsis
Jingjing Zhang, Xi Zhu Xu, Xuting Jin et al. BMC Infectious Diseases, 2025. Cites: paper

RMS: a ML-based system for ICU respiratory monitoring and resource planning
Matthias Hüser, Xinrui Lyu, Martin Faltys et al. npj Digital Medicine, 2025. Cites: dataset, paper

Understanding Uncertainty in Large Language Model Predictions of Early Death in Critically Ill Patients: A Conformal Prediction Approach
Fatemeh Shah-Mohammadi, Alexander Millar, Julio Facelli et al. medRxiv, 2025. Cites: paper

Machine Learning in Public Health and the Prediction-Intervention Gap
Thomas Grote, Oliver Buchholz. Synthese Library/Synthese library, 2025. Cites: paper

Magnesium Supplementation and Tachyarrhythmias
Robert Goulden, Michał Abrahamowicz, Erin Strumpf et al. JAMA Internal Medicine, 2025. Cites: paper

Machine learning-based prediction of drug response in ischemia reperfusion animal model
Asmaa Mohamed Abd ElGwad, Ibrahim Youssef, Abdelrahman Khaled et al. Scientific Reports, 2025. Cites: paper

An interpretable machine learning approach to prognosis of melioidosis pneumonia via computed tomography quantification and clinical data
Yehua Wu, Yuefu Zhan, Dehuai He et al. Scientific Reports, 2025. Cites: paper

Continuous Multimodal AI with Wearable Vital Signs Predicts Postoperative Complications in the General Ward
Robin P. van de Water, Axel Winter, Daniela Zuluaga et al. medRxiv, 2025. Cites: paper

Development and validation of deep continual learning model to sequentially learn multiple clinical prediction tasks for ICU patients
Zhixuan Zeng, Yang Liu, Shuo Yao et al. Artificial Intelligence in Medicine, 2025. Cites: paper

Machine Learning-Based Classification of ICU-Acquired Neuromuscular Weakness: A Comparative Study in Survivors of Critical Illness
David Estévez-Freire, Ivan Cangas, Andrés Tirado-Espín et al. Life, 2025. Cites: paper

Automated detection of the epileptogenic zone in stereoelectroencephalography for drug-resistant epilepsy using multi-epileptogenic biomarker machine learning
Chuan Du, Weipeng Jin, Le Wang et al. Epilepsy Research, 2025. Cites: paper

Endotracheal intubation-related oral mucosal membrane pressure injuries: a narrative review of biomechanical insights, biomaterial optimization, and intelligent monitoring
Limei Cai, Yijing Li, Yonggang Liu et al. Frontiers in Medical Technology, 2025. Cites: paper

Enhancing post-kidney transplant prognostication: an interpretable machine learning approach for longitudinal outcome prediction
Bowen Fan, Manuel Schürch, Yuan Tian et al. npj Digital Medicine, 2025. Cites: paper

Using machine learning for early prediction of in-hospital mortality during ICU admission in liver cancer patients
Zhuo Zheng, Jinhong Xia, Jiawei Luo et al. Scientific Reports, 2025. Cites: paper

Predictive Modeling of Anesthesia Types and Their Associations with Clinical Outcomes Using the MIMIC-IV Database
Goli Asgari, Ata Amini, Pooya Vahedi et al. Journal of Cellular & Molecular Anesthesia, 2025. Cites: paper

Proposing a novel ABCDEF framework for managing critical illness in geriatrics: challenges and perspectives
Jiacheng Shen, Jing Yan, Jianhong Xie et al. Annals of Medicine, 2025. Cites: paper

Deep learning models for ICU readmission prediction: a systematic review and meta-analysis
Emanuele Koumantakis, Konstantina Remoundou, Nicoletta Colombi et al. Critical Care, 2025. Cites: paper

Autonomous artificial intelligence prescribing a drug to prevent severe acute graft-versus-host disease in HLA-haploidentical transplants
Junren Chen, Yigeng Cao, Yahui Feng et al. Nature Communications, 2025. Cites: paper

Medical Artificial Intelligence
Thomas Grote. Oxford University Press eBooks, 2025. Cites: paper

Artificial Intelligence in Non-invasive Hemodynamic Monitoring: A Systematic Review of Accuracy, Effectiveness, and Clinical Applicability in Cardiology
Anas E Ahmed, Suhail M Al-Kinani, Abdulrahman M Alshammari et al. Cureus, 2025. Cites: paper

Standardized Multicenter Critical Care Database Integrating Minute-Level Vital Signs, Laboratory Tests, Interventions, and Outcomes: Profile of the OneICU Database
Takahiro Kinoshita, Yutaka Umemura, Makoto Watanabe et al. medRxiv, 2025. Cites: paper

Machine learning can aid in the differential diagnosis of neurogenic thoracic outlet syndrome and carpal tunnel syndrome
Syeda Hoorulain Ahmed, David C. Hall, Bassam M. Smadi et al. Journal of Plastic Reconstructive & Aesthetic Surgery, 2025. Cites: paper

Advanced Hemodynamic Monitoring: Pros, Cons, and the Future
Malek Mohamed Abusannuga, Zakria Attique, Sara Abdulla et al. IntechOpen eBooks, 2025. Cites: paper

Open-access databases in anesthesiology and intensive care: a systematic review and guidelines
Mikhail Ya. Yadgarov, Берикашвили Леван Бондоевич, Alexey A. Yakovlev et al. Russian Journal of Anesthesiology and Reanimatology, 2025. Cites: paper

Sharing is caring: A systematic review of publicly available intensive care data sets
Ameet R. Jagesar, Tariq A. Dam, Tristan Struja et al. Journal of Critical Care, 2025. Cites: paper

Trends and methods in intensive care unit (ICU) research using machine learning: latent dirichlet allocation (LDA)-based thematic literature review
Duygu Topaloğlu, Olcay Polat. BMC Medical Informatics and Decision Making, 2025. Cites: paper

A Foundation Model for Intensive Care: Unlocking Generalization across Tasks and Domains at Scale
Manuel Burger, Daphné Chopard, Gregor Lichtner et al. medRxiv, 2025. Cites: dataset, paper

Machine Learning Model to Predict Pre-Term Delivery Using Electronic Health Records
Ankur Pathak, Rakesh Kumar, Meenu Gupta et al. 2025. Cites: paper

Development and validation of a dynamic early warning system with time-varying machine learning models for predicting hemodynamic instability in critical care: a multicohort study
Dung-Hung Chiang, Zeyu Jiang, Cong Tian et al. Critical Care, 2025. Cites: paper

Explainable Temporal Inference for Irregular Multivariate Time Series. A Case Study for Early Prediction of Multidrug Resistance
Óscar Escudero-Arnanz, Cristina Soguero-Ruíz, Joaquín Álvarez-Rodríguez et al. IEEE Transactions on Biomedical Engineering, 2025. Cites: paper

CoTD-VAE: Interpretable Disentanglement of Static, Trend, and Event Components in Complex Time Series for Medical Applications
Li Huang, Qingfeng Chen. Applied Sciences, 2025. Cites: paper

A comprehensive machine learning for high throughput Tuberculosis sequence analysis, functional annotation, and visualization
Md. Saddam Hossain, Md. Parvez Khandocar, Farzana Akter Riti et al. Scientific Reports, 2025. Cites: paper

Predicting the clinical evolution of septic patients from routinely collected data and vital signs variability using machine learning
Ilaria Mentasti, Marta Carrara, Manuela Ferrario. Physiological Measurement, 2025. Cites: dataset, paper

Mean arterial pressure is all you need in a machine learning model for mean arterial pressure prediction
Thomas Tschoellitsch, Sophie Kaltenleithner, Alexander Maletzky et al. European Journal of Anaesthesiology, 2025. Cites: paper

Implicit bias in ICU electronic health record data: measurement frequencies and missing data rates of clinical variables
Junming Shi, Alan Hubbard, Nicholas Fong et al. BMC Medical Informatics and Decision Making, 2025. Cites: paper

MC-MED, multimodal clinical monitoring in the emergency department
Aman Kansal, Emma Chen, Boyang Tom Jin et al. Scientific Data, 2025. Cites: dataset

SeFNet: Linking tabular datasets with semantic feature nets
Katarzyna Woźnica, Piotr Wilczyński, Przemysław Biecek. Information Fusion, 2025. Cites: paper

Case-Control Matching Erodes Feature Discriminability for AI-driven Sepsis Prediction in ICUs: A Retrospective Cohort Study
Sophia F. Ehlers, Fanny Tranchellini, Youssef Farag et al. medRxiv, 2025. Cites: dataset, paper

Data-driven trends in critical care informatics: a bibliometric analysis of global collaborations using the MIMIC database (2004–2024)
Xiyue Zhang, Weimin Kong, Rongfen Shi et al. Computers in Biology and Medicine, 2025. Cites: dataset

Predicting Cardiovascular deterioration in a paediatric intensive care unit (PicEWS): a machine learning modelling study of routinely collected health-care data
Dan F. Stein, Michael James Carter, John Booth et al. EClinicalMedicine, 2025. Cites: paper

Harnessing AI in critical care: opportunities, challenges and key steps for success
Alexandre Kalimouttou, Robert D. Stevens, Romain Pirracchio. Thorax, 2025. Cites: paper

Multi-algorithm consensus classification identifies three distinct acute liver failure subtypes with differential treatment responses: a multi-database cohort study
Ying Yang, Wei Yang, Bo Tang et al. Journal of Advanced Research, 2025. Cites: paper

Machine Learning and Artificial Intelligence in Intensive Care Medicine: Critical Recalibrations from Rule-Based Systems to Frontier Models
Pierre Hadweh, Alexandre Niset, Michele Salvagno et al. Journal of Clinical Medicine, 2025. Cites: paper

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