Cited by

Salzburg Intensive Care database (SICdb), a freely accessible intensive care database (v1.0.8)

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62 publications cite any version of this project or its primary paper, newest first.

• Preceding organ trajectories guide dynamic reassessment after apparent stabilisation in sepsis
  Luomeng Wang, Jialin Song, Marina Vereshchako et al. medRxiv, 2026. Cites: paper
• Recorded Norepinephrine Restart Estimates in Intensive Care Databases: Retrospective Study of Episode Matching and Index Times (Preprint)
  Si Chen, Luan Haixia, Yan Wang et al. 2026. Cites: paper
• Blood pressure history improves near-term hypotension prediction and reassessment prioritisation in sepsis
  Luomeng Wang, Jiajun Zhu, Rongyang Zhou et al. medRxiv, 2026. Cites: dataset, paper
• Evaluating Intensive Care Unit Patient Trajectory Representations Using a Temporal Entity-Attribute-Value Schema: a Retrospective Multicenter Benchmarking Study (Preprint)
  Dimitrios Proios, Alban Bornet, Yamil Vindas et al. 2026. Cites: paper
• A generative digital twin forecasts and simulates intensive care patients without task labels
  Han Wang, Kaisheng Ye, Huaguo Yuan et al. Research Square, 2026. Cites: dataset
• Data Stream and Hospital-Level Variation in Intensive Care Unit Vital-Sign Record Density Across Medical Information Mart for Intensive Care IV and eICU Collaborative Research Database: Retrospective Data Quality Study
  Yngve Mikkelsen. JMIR Medical Informatics, 2026. Cites: paper
• Hourly Blood Urea Nitrogen Trajectories and ICU Mortality Risk in Critically Ill Patients with Gastrointestinal Bleeding: A Multicenter Retrospective Cohort Study
  Bin Liu, Xin Zheng, Zhou Xu et al. Advances in Therapy, 2026. Cites: paper
• Antibiotic trajectories after cardiopulmonary bypass and in-hospital mortality across critical care databases
  Ding Zhang, Ru-qin Yi, Wei Chen et al. Research Square, 2026. Cites: paper
• Temporal deep learning for 28-day mortality prediction in critically ill ovarian cancer patients: a multicenter development and validation study using hourly vital signs
  Chengling Wang, Lu Liu, J B Hu et al. Journal of Ovarian Research, 2026. Cites: paper
• Sedation-agitation trajectories and airway management in adult ICU patients: a retrospective SICdb study
  Chuanming Zheng, Zhan Wang. Research Square, 2026. Cites: dataset, paper
• Development and external validation of a dynamic landmark model for predicting acute kidney injury worsening in critical care: a multidatabase study using SICdb, MIMIC-IV, and eICU-CRD
  Chuanming Zheng, Zihou Xing, Weiwei Xue et al. Research Square, 2026. Cites: paper
• Toward trustworthy digital healthcare: A system-level convergence of IoMT, large language models, and explainable AI
  Maria Bashir, Shaker El–Sappagh, Simon S. Woo et al. Information Fusion, 2026. Cites: paper
• Multicenter validation and updating of the ELDER-ICU model for severity assessment in elderly critical illness
  Minjie Duan, Xiaoli Liu, Wesley Lok Kin Yeung et al. npj Digital Medicine, 2026. Cites: paper
• Regression discontinuity design - not always as good as random
  Thomas Scheier, John W. Eikelboom. Journal of Critical Care, 2026. Cites: paper
• The Impact of Evaluation Strategy on Sepsis Prediction Model Performance Metrics in Intensive Care Data: Retrospective Cohort Study
  Dang-Khoa Do, Patrick Rockenschaub, Sebastian Daniel Boie et al. Journal of Medical Internet Research, 2026. Cites: paper
• Artificial intelligence-assisted phenotyping of sepsis: Research progress, clinical challenges, and translational prospects
  Hang Ruan, Jing-Kun Lee, Jie Xiong et al. Digital Health, 2026. Cites: paper
• A single postoperative red cell distribution width measurement predicts short- and long-term mortality in surgical patients
  Daniel Dankl, Bernhard Wernly, Niklas Rodemund et al. Wiener Medizinische Wochenschrift, 2026. Cites: paper
• Rethinking Preoperative Risk Evaluation: How Well Does EuroSCORE II Predict Long-Term Mortality After Cardiac Surgery?—A Single-Centre Retrospective Analysis
  Andreas Koköfer, Lukas Simon Fischer, Bernhard Wernly et al. Journal of Clinical Medicine, 2026. Cites: paper
• Discovery of data quality issues in electronic health records: profound consequences for critical care medicine applications – a systematized review
  João Brainer Clares de Andrade, Marconny Alexandre Oliveira de Medeiros Cavalcante, Thiago Luís Marques Lopes et al. Critical Care, 2026. Cites: paper
• Artificial Intelligence in Clinical Trials: From Protocol Design to Pharmacovigilance
  Aron Schulman, Adrià Fernández‐Torras, Yojana Gadiya et al. Springer eBooks, 2026. Cites: paper
• Domain generalization and adaptation in intensive care with anchor regression
  Malte Londschien, Manuel Burger, Gunnar Rätsch et al. RSS Data Science and Artificial Intelligence, 2026. Cites: paper
• Novel Electronic Health Record–Based Data Commons for Pancreatic Cancer
  kaleem sohail Ahmed, Clayton Thomas Marcinak, Muhammad Maisam Ali et al. JCO Clinical Cancer Informatics, 2026. Cites: paper
• Development and validation of the Hypotensive Exposure Duration Index for mortality risk prediction in critically ill patients
  Xiao-Yan Ding, Haiping Xu, Jingru Zhang et al. Journal of Intensive Care, 2025. Cites: paper
• Association of cumulative fluid balance trajectories with haemodynamics, inflammation, and long-term mortality in critically ill patients with circulatory failure
  Martin Ruste, Niklas Rodemund, Fabien Subtil et al. British Journal of Anaesthesia, 2025. Cites: paper
• Personalized Fluid Management in Patients With Sepsis and Acute Kidney Injury: A Causal Machine Learning Approach
  Wonsuk Oh, Kullaya Takkavatakarn, Zainab Al-Taie et al. Critical Care Explorations, 2025. Cites: paper
• Prediction of hospital mortality in sepsis-associated acute kidney injury using a machine-learning approach: a multicenter study using SHAP interpretability analysis
  Songsong Luo, Jiawei Lai, Lujiao Mo et al. Clinical Kidney Journal, 2025. Cites: paper
• Unlocking the potential of real-time ICU mortality prediction: redefining risk assessment with continuous data recovery
  Puguang Xie, Yu Hu, Jiao Li et al. npj Digital Medicine, 2025. Cites: paper
• Subphenotypes of mechanically ventilated acute respiratory distress syndrome patients based on multi-dimensional pathophysiological parameters
  lizhi Li, Xiaolong Huang, Weiyuan Luo et al. Critical Care, 2025. Cites: paper
• Opportunities, challenges and future perspectives for target trial emulation in critical care clinical research
  Carmen A. T. Reep, Evert‐Jan Wils, Leo M. A. Heunks. Critical Care, 2025. Cites: paper
• Personalized Hemodynamic Management Using Reinforcement Learning to Prevent Persistent Acute Kidney Injury After Cardiac Surgery
  Moein Sabounchi, Jacob M. Desman, Idan Shenfeld Amit et al. medRxiv, 2025. Cites: paper
• Temporal dimensions of oxygen saturation exposure and clinical outcomes in critically ill patients: a retrospective multi-database cohort study
  Xiao-Yan Ding, Hai-Ping Xu, Jingru Zhang et al. Critical Care, 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
• 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
• The association between week-one red blood cell distribution width and one-year survival in critically ill patients: propensity score-based multicenter analysis
  Shang‐Yi Lee, Li-Ting Wong, Wen‐Cheng Chao. European journal of medical research, 2025. Cites: paper
• Sex and gender considerations in randomized controlled trials in critical care nephrology: a meta-epidemiologic study
  Nuttha Lumlertgul, Anyarin Wannakittirat, Janice Yu Chen Kung et al. BMC Medicine, 2025. Cites: 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: 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
• Long-term mortality after stage 1 acute kidney injury in critically ill patients - an observational cohort study
  Carmen Andrea Pfortmueller, Markus Hahn, Angela Eggimann et al. Journal of Critical Care, 2025. Cites: paper
• ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning
  Wonsuk Oh, Marinela Veshtaj, Ashwin Shreekant Sawant et al. Critical Care, 2025. Cites: paper
• Nocturnal elective coronary artery bypass grafting (CABG) surgery is not associated with increased one-year mortality
  Andreas Koköfer, Christian Dinges, Crispiana Cozowicz et al. Patient Safety in Surgery, 2025. Cites: dataset
• Beyond perfection: why imperfect routinely collected intensive care data still hold value
  Niklas Rodemund, Bernhard Wernly, Ottokar Stundner et al. Intensive Care Medicine, 2025. Cites: paper
• A framework and analytical exploration for a data-driven update of the Sequential Organ Failure Assessment (SOFA) score in sepsis
  Drago Plečko, Nicolas Bennett, Ida-Fong Ukor et al. Critical Care and Resuscitation, 2025. Cites: paper
• Limitations of SpO2 / FiO2-ratio for classification and monitoring of acute respiratory distress syndrome—an observational cohort study
  Rolf Erlebach, Una Pale, Tilman Beck et al. Critical Care, 2025. Cites: paper
• pyAKI—An open source solution to automated acute kidney injury classification
  Christian Porschen, Jan Ernsting, Paul Brauckmann et al. PLoS ONE, 2025. Cites: paper
• ContrastLOS: A Graph-Based Deep Learning Model With Contrastive Pre-Training for Improved ICU Length-of-Stay Prediction
  G. F. Fan, Aixiang Liu, Chao Zhang. IEEE Access, 2025. Cites: paper
• Trash in/trash out? Using routinely collected clinical data for data science in the ICU: Con
  Brenda Pörteners, Christian Jung, Geert Meyfroidt. Intensive Care Medicine, 2024. Cites: paper
• Reinforcement learning model for optimizing dexmedetomidine dosing to prevent delirium in critically ill patients
  Hong Yeul Lee, Soomin Chung, Dongwoo Hyeon et al. npj Digital Medicine, 2024. Cites: paper
• The development of a C5.0 machine learning model in a limited data set to predict early mortality in patients with ARDS undergoing an initial session of prone positioning
  David M. Hannon, Jaffar David Abbas Syed, Bairbre McNicholas et al. Intensive Care Medicine Experimental, 2024. Cites: paper
• The Role of Artificial Intelligence in Personalized Intensive Insulin Therapy for Critically Ill Diabetic Patients: A Future Directive
  Md Ikhsan Mokoagow, Arief Purnama Muharram, Ahmad Zufar Manthovani et al. 2024. Cites: paper

Citation data from OpenAlex, updated weekly. Papers that use the project without citing it may be missing.