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

Machine Learning-Based Predictors for ICU Admission of COVID-19 Patients
Nagham Alhawas, Serkan Kartal. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, 2022. Cites: paper

Pan-Cancer Indicators of Long-Term Survival Benefits After Immune Checkpoint Inhibitor Therapy
Jie Zhao, Yiting Dong, Hua Bai et al. SSRN Electronic Journal, 2022. Cites: paper

Prediction of intensive care unit admission (>24h) after surgery in elective noncardiac surgical patients using machine learning algorithms
Lan Lan, Fangwei Chen, Jiawei Luo et al. Digital Health, 2022. Cites: paper

Premature Birth Prediction Using Machine Learning Techniques
Kazi Rafat, Sadia Islam, Ahmed Omar Salim Adnan et al. Lecture notes in networks and systems, 2022. Cites: paper

Smart Hospital Artificial Intelligence System for Predicting Nurse Call Button Demand Using Patient Acuity Levels, Room Location, Prior Call Frequency, Time-of-Day Patterns, and Unit Staffing Ratios
Daniel Fischer, Laura Meier, Thomas Braun et al. Journal of Health Informatics and Digital Systems, 2022. Cites: paper

Intraoperative prediction of postanaesthesia care unit hypotension
Konstantina Palla, Stephanie L. Hyland, Karen L. Posner et al. British Journal of Anaesthesia, 2021. Cites: paper

The year in cardiovascular medicine 2021: digital health and innovation
Panos E. Vardas, Folkert Wouter Asselbergs, Maarten van Smeden et al. European Heart Journal, 2021. Cites: paper

A recurrent machine learning model predicts intracranial hypertension in neurointensive care patients
Nils Schweingruber, Marius Marc-Daniel Mader, Anton Wiehe et al. Brain, 2021. Cites: paper

Is artificial intelligence ready to solve mechanical ventilation? Computer says blow
Dominic Charles Marshall, Matthieu Komorowski. British Journal of Anaesthesia, 2021. Cites: paper

Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring
Yijing Li, Wenyu Ye, Kang Yang et al. Computer Methods and Programs in Biomedicine, 2021. Cites: paper

The potential for artificial intelligence to predict clinical outcomes in patients who have acquired acute kidney injury during the perioperative period
Breandan Kelly, Julio Leonel Chevarria, Barry O’Sullivan et al. Perioperative Medicine, 2021. Cites: dataset

Surgical data science – from concepts toward clinical translation
Lena Maier‐Hein, Matthias Eisenmann, Duygu Sarıkaya et al. Medical Image Analysis, 2021. Cites: paper

A wireless controlled intelligent healthcare system for diplegia patients
Muhammad Tanveer Riaz, Abeer Abdulaziz Alsanad, Saeed Ahmad et al. Mathematical Biosciences & Engineering, 2021. Cites: paper

The Framing of machine learning risk prediction models illustrated by evaluation of sepsis in general wards
Simon Meyer Lauritsen, Bo Thiesson, Marianne Johansson Jørgensen et al. npj Digital Medicine, 2021. Cites: paper

Early prediction of hemodynamic interventions in the intensive care unit using machine learning
Asif Rahman, Yale Chang, Junzi Dong et al. Critical Care, 2021. Cites: paper

Information fusion as an integrative cross-cutting enabler to achieve robust, explainable, and trustworthy medical artificial intelligence
Andreas Holzinger, Matthias Dehmer, Frank Emmert‐Streib et al. Information Fusion, 2021. Cites: paper

Artificial intelligence for mechanical ventilation: systematic review of design, reporting standards, and bias
Jack Gallifant, Joe Zhang, M.D.P. Arias Lopez et al. British Journal of Anaesthesia, 2021. Cites: paper

Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study
MinDong Sung, Sangchul Hahn, Chang Hoon Han et al. JMIR Medical Informatics, 2021. Cites: paper

Application of machine learning to predict the occurrence of arrhythmia after acute myocardial infarction
Suhuai Wang, Jingjie Li, Lin Sun et al. BMC Medical Informatics and Decision Making, 2021. Cites: paper

Intelligent patient monitoring for proactive alerting of key personnel in intensive care: A single-center study
Vikas Rana, Teddy Le Nguyen, Veera Raghava et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2021. Cites: paper

The obesity paradox and hypoglycemia in critically ill patients
Drago Plečko, Nicolas Bennett, Johan Mårtensson et al. Critical Care, 2021. Cites: paper

A Text Mining Approach in the Classification of Free-Text Cancer Pathology Reports from the South African National Health Laboratory Services
Okechinyere Achilonu, Victor Olago, Elvira Singh et al. Information, 2021. Cites: paper

Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke
Seong-Hwan Kim, Eun‐Tae Jeon, Sungwook Yu et al. Scientific Reports, 2021. Cites: paper

Stacking interventions for equitable outcomes
James Liley. arXiv (Cornell University), 2021. Cites: paper

Uncovering expression signatures of synergistic drug response using an ensemble of explainable AI models
Joseph D. Janizek, Ayse B. Dincer, Safiye Çelik et al. bioRxiv (Cold Spring Harbor Laboratory), 2021. Cites: paper

Interpretable machine learning for early neurological deterioration prediction in atrial fibrillation-related stroke
Seong Hwan Kim, Eun‐Tae Jeon, Sungwook Yu et al. Journal of the Neurological Sciences, 2021. Cites: paper

Machine learning using the extreme gradient boosting (XGBoost) algorithm predicts 5-day delta of SOFA score at ICU admission in COVID-19 patients
Jonathan Montomoli, Luca Romeo, Sara Moccia et al. Journal of Intensive Medicine, 2021. Cites: paper

Artificial intelligence sepsis prediction algorithm learns to say “I don’t know”
Supreeth Prajwal Shashikumar, Gabriel Wardi, Atul Malhotra et al. npj Digital Medicine, 2021. Cites: paper

Machine learning based early mortality prediction in the emergency department
Cong Li, Zhuo Zhang, Yazhou Ren et al. International Journal of Medical Informatics, 2021. Cites: paper

Cardiogenic shock and machine learning: A systematic review on prediction through clinical decision support softwares
Rene Alemán, Sinal Patel, Jose R. Sleiman et al. Journal of Cardiac Surgery, 2021. Cites: paper

The AIMe registry for artificial intelligence in biomedical research
Julian Matschinske, Nicolás Alcaraz, Arriel Benis et al. Nature Methods, 2021. Cites: paper

The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients
Lucas M. Fleuren, Tariq A. Dam, Michele Tonutti et al. Critical Care, 2021. Cites: paper

A New Time-Window Prediction Model For Traumatic Hemorrhagic Shock Based on Interpretable Machine Learning
Yuzhuo Zhao, Lijing Jia, Ruiqi Jia et al. Shock, 2021. Cites: paper

COP-E-CAT
Aishwarya Mandyam, Elizabeth C. Yoo, Jeff Soules et al. ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB), 2021. Cites: paper

IoT-Based Patient Movement Monitoring: The Post-Operative Hip Fracture Rehabilitation Model
Akash Gupta, Adnan Al‐Anbuky. Future Internet, 2021. Cites: paper

Comparison among random forest, logistic regression, and existing clinical risk scores for predicting outcomes in patients with atrial fibrillation: A report from the J‐RHYTHM registry
Eiichi Watanabe, Shunsuke Noyama, Ken Kiyono et al. Clinical Cardiology, 2021. Cites: paper

Predicting brain function status changes in critically ill patients via Machine learning
Chao Yan, Cheng Gao, Ziqi Zhang et al. Journal of the American Medical Informatics Association, 2021. Cites: paper

Application of Machine Learning To Predict The Occurrence of Arrhythmia After Acute Myocardial Infarction
Suhuai Wang, jingjie Li, Lin Sun et al. Research Square, 2021. Cites: paper

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning
Michael Moor, Nicolas Bennett, Drago Plečko et al. arXiv (Cornell University), 2021. Cites: paper

An exploratory data quality analysis of time series physiologic signals using a large-scale intensive care unit database
Ali S Afshar, Yijun Li, Zixu Chen et al. JAMIA Open, 2021. Cites: paper

Pancreatic cancer risk predicted from disease trajectories using deep learning
Davide Placido, Bo Yuan, Jessica Xin Hjaltelin et al. bioRxiv (Cold Spring Harbor Laboratory), 2021. Cites: paper

Semi-supervised Optimal Transport with Self-paced Ensemble for Cross-hospital Sepsis Early Detection.
Ruiqing Ding, Zhou Yu, Jie Xu et al. arXiv (Cornell University), 2021. Cites: paper

Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer
Chris McIntosh, Leigh Conroy, Michael C. Tjong et al. Nature Medicine, 2021. Cites: paper

The Physiological Deep Learner: First application of multitask deep learning to predict hypotension in critically ill patients
Ményssa Cherifa, Yannet Interian, Alice Blet et al. Artificial Intelligence in Medicine, 2021. Cites: paper

Validation of a Machine Learning Model for Early Shock Detection
Yuliya Pinevich, Adam Amos‐Binks, Christie Burris et al. Military Medicine, 2021. Cites: paper

Randomised controlled trials in medical AI: ethical considerations
Thomas Grote. Journal of Medical Ethics, 2021. Cites: paper

Early prediction of respiratory failure in the intensive care unit
Matthias Hüser, Martin Faltys, Xinrui Lyu et al. arXiv (Cornell University), 2021. Cites: paper

Early Prediction of Organ Failures in Patients with Acute Pancreatitis Using Text Mining
Jiawei Luo, Lan Lan, DuJiang Yang et al. Scientific Programming, 2021. Cites: paper

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records
Nenad Tomašev, Natalie Harris, Sebastien Baur et al. Nature Protocols, 2021. Cites: paper

Classification of Relaxation and Concentration Mental States with EEG
Shingchern D. You. Information, 2021. Cites: paper

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