On the public citation graph
Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study
0 signed · 54 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1001/jama.1993.03510200084037openalex-snapshot
Mortality Probability Models (MPM II) Based on an International Cohort of Intensive Care Unit Patients
inferredcites →https://doi.org/10.1001/jama.1993.03510240069035openalex-snapshot
A New Simplified Acute Physiology Score (SAPS II) Based on a European/North American Multicenter Study
inferredcites →https://doi.org/10.1001/jama.2016.0287openalex-snapshot
The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3)
inferredcites →https://doi.org/10.1001/jama.2017.13836openalex-snapshot
Incidence and Trends of Sepsis in US Hospitals Using Clinical vs Claims Data, 2009-2014
inferredcites →https://doi.org/10.1007/3-540-49430-8openalex-snapshot
Neural Networks: Tricks of the Trade
inferredcites →https://doi.org/10.1007/s00134-019-05872-yopenalex-snapshot
Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy
inferredcites →https://doi.org/10.1016/j.cmpb.2018.12.027openalex-snapshot
Prediction of sepsis patients using machine learning approach: A meta-analysis
inferredcites →https://doi.org/10.1016/j.compbiomed.2019.04.027openalex-snapshot
Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs
inferredcites →https://doi.org/10.1016/j.ijmedinf.2020.104176openalex-snapshot
The development an artificial intelligence algorithm for early sepsis diagnosis in the intensive care unit
inferredcites →https://doi.org/10.1016/j.iot.2019.100125openalex-snapshot
Evaluating Internet of Medical Things (IoMT)-Based Systems from a Human-Centric Perspective
inferredcites →https://doi.org/10.1016/s2213-2600(18)30300-xopenalex-snapshot
Machine learning for real-time prediction of complications in critical care: a retrospective study
inferredcites →https://doi.org/10.1016/s2589-7500(20)30018-2openalex-snapshot
Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records
inferredcites →https://doi.org/10.1038/s41586-019-1390-1openalex-snapshot
A clinically applicable approach to continuous prediction of future acute kidney injury
inferredcites →https://doi.org/10.1038/s41591-020-0789-4openalex-snapshot
Early prediction of circulatory failure in the intensive care unit using machine learning
inferredcites →https://doi.org/10.1093/jamia/ocx080openalex-snapshot
Electronic health record adoption in US hospitals: the emergence of a digital “advanced use” divide
inferredcites →https://doi.org/10.1097/00003246-198608000-00028openalex-snapshot
APACHE II-A Severity of Disease Classification System
inferredcites →https://doi.org/10.1097/ccm.0000000000001856openalex-snapshot
ICU Admission, Discharge, and Triage Guidelines: A Framework to Enhance Clinical Operations, Development of Institutional Policies, and Further Research
inferredcites →https://doi.org/10.1097/ccm.0000000000002936openalex-snapshot
An Interpretable Machine Learning Model for Accurate Prediction of Sepsis in the ICU
inferredcites →https://doi.org/10.1136/bmjopen-2018-024778openalex-snapshot
Nurses’ 12-hour shifts and missed or delayed vital signs observations on hospital wards: retrospective observational study
inferredcites →https://doi.org/10.1159/000430770openalex-snapshot
Contrast Medium-Induced Acute Kidney Injury
inferredcites →https://doi.org/10.1159/000452702openalex-snapshot
Intravenous Fluids and Acute Kidney Injury
inferredcites →https://doi.org/10.1186/cc11454openalex-snapshot
Diagnosis, evaluation, and management of acute kidney injury: a KDIGO summary (Part 1)
inferredcites →https://doi.org/10.1186/s13054-020-2785-yopenalex-snapshot
Artificial Intelligence in the Intensive Care Unit
inferredcites →https://doi.org/10.12788/jhm.3229openalex-snapshot
Early Warning Systems: The Neglected Importance of Timing
inferredcites →https://doi.org/10.2196/16349openalex-snapshot
Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.4847060openalex-extracted
Multi-Horizon Event Detection for In-Hospital Clinical Deterioration Using Dual-Channel Graph Attention Network
inferred← citeshttps://doi.org/10.1007/978-3-031-39539-0_4openalex-snapshot
Self-supervised Predictive Coding with Multimodal Fusion for Patient Deterioration Prediction in Fine-Grained Time Resolution
inferred← citeshttps://doi.org/10.1007/978-981-95-4972-6_27openalex-snapshot
Personalized Federated Graph Learning for Heterogeneous Incomplete EHRs
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110336openalex-snapshot
Attention to early stages: predicting acute kidney injury in a post cardiosurgical ICU setting using an inclusive time-to-event model
inferred← citeshttps://doi.org/10.1016/j.ijmedinf.2024.105745openalex-snapshot
Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network
inferred← citeshttps://doi.org/10.1016/j.ijnurstu.2025.105133openalex-snapshot
Using nursing data for machine learning-based prediction modeling in intensive care units: A scoping review
inferred← citeshttps://doi.org/10.1016/j.inffus.2025.103283openalex-snapshot
Harnessing the potential of multimodal EHR data: A comprehensive survey of clinical predictive modeling for intelligent healthcare
inferred← citeshttps://doi.org/10.1016/j.patter.2023.100892openalex-snapshot
Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients
inferred← citeshttps://doi.org/10.1021/acs.nanolett.5c05256openalex-snapshot
HepatoAI LFA : Machine-Learning-Assisted Nano-enhanced Point-of-Care System for Personalized Precise Diagnosis of Hepatocellular Carcinoma
inferred← citeshttps://doi.org/10.1038/s41598-024-75331-2openalex-snapshot
Distilling the knowledge from large-language model for health event prediction
inferred← citeshttps://doi.org/10.1053/j.ackd.2022.06.005openalex-snapshot
Machine Learning for Acute Kidney Injury Prediction in the Intensive Care Unit
inferred← citeshttps://doi.org/10.1093/jamia/ocag124openalex-snapshot
MADE: leveraging informative missingness and temporal evaluation for electronic health record-based deterioration prediction in general wards
inferred← citeshttps://doi.org/10.1101/2023.10.11.23296733openalex-snapshot
Generalisability of AI-based scoring systems in the ICU: a systematic review and meta-analysis
inferred← citeshttps://doi.org/10.1109/access.2023.3257406openalex-snapshot
Rapid Response System Based on Graph Attention Network for Predicting In-Hospital Clinical Deterioration
inferred← citeshttps://doi.org/10.1136/bmjhci-2024-101353openalex-snapshot
Validation of an artificial intelligence-based algorithm for predictive performance and risk stratification of sepsis using real-world data from hospitalised patients: a prospective observational study
inferred← citeshttps://doi.org/10.1145/3651671.3651708openalex-snapshot
Predicting wildfire events with calibrated probabilities
inferred← citeshttps://doi.org/10.12693/aphyspola.146.388openalex-snapshot
Biosignal-Based Machine Learning Predictors of Sepsis: A Mini-Review
inferred← citeshttps://doi.org/10.2196/38053openalex-snapshot
A Transfer Learning Approach to Correct the Temporal Performance Drift of Clinical Prediction Models: Retrospective Cohort Study
inferred← citeshttps://doi.org/10.2196/72232openalex-snapshot
Deep Learning–Based Early Warning Systems in Hospitalized Patients at Risk of Code Blue Events and Length of Stay: Retrospective Real-World Implementation Study
inferred← citeshttps://doi.org/10.2196/93466openalex-snapshot
Value of AI in Critical Care Using Real-World Evidence on Intensive Care Unit Mortality Prediction: Cost-Utility Analysis
inferred← citeshttps://doi.org/10.22514/sv.2024.045openalex-snapshot
The development and validation of a novel deep-learning algorithm to predict in-hospital cardiac arrest in ED-ICU (emergency department-based intensive care units): a single center retrospective cohort study
inferred← citeshttps://doi.org/10.3390/jcm14072213openalex-snapshot
Preserving Informative Presence: How Missing Data and Imputation Strategies Affect the Performance of an AI-Based Early Warning Score
inferred← citeshttps://doi.org/10.3390/jcm14134444openalex-snapshot
Clinical Context Is More Important than Data Quantity to the Performance of an Artificial Intelligence-Based Early Warning System
inferred← citeshttps://doi.org/10.3390/jcm15062125openalex-snapshot
Evaluating the Predictive Potential of an AI-Driven Deep Learning Model for Pneumonia-Associated Sepsis
inferred← citeshttps://doi.org/10.4266/acc.000525openalex-snapshot
Prospective external validation of a deep-learning-based early-warning system for major adverse events in general wards in South Korea
inferred← citeshttps://doi.org/10.47606/acven/mv0270openalex-snapshot
Impacto de la inteligencia artificial en la predicción de eventos críticos en las unidades de cuidados intensivos: Implicaciones para la práctica y la toma de decisiones en enfermería
inferred← citeshttps://doi.org/10.47606/acven/ph0343openalex-snapshot
Impacto de la inteligencia artificial en la predicción de eventos críticos en las unidades de cuidados intensivos: implicaciones para la práctica y la toma de decisiones en enfermería
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.