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Development of a Real-Time Risk Prediction Model for In-Hospital Cardiac Arrest in Critically Ill Patients Using Deep Learning: Retrospective Study

https://doi.org/10.2196/16349
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1001/archinte.168.12.1300openalex-snapshot
Defining the Incidence of Cardiorespiratory Instability in Patients in Step-down Units Using an Electronic Integrated Monitoring System
inferredcites →https://doi.org/10.1001/jama.2016.17216openalex-snapshot
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
inferredcites →https://doi.org/10.1001/jama.299.7.785openalex-snapshot
Survival From In-Hospital Cardiac Arrest During Nights and Weekends
inferredcites →https://doi.org/10.1002/9781118032985openalex-snapshot
The Statistical Analysis of Failure Time Data
inferredcites →https://doi.org/10.1007/0-387-37119-2openalex-snapshot
The Statistical Analysis of Interval-censored Failure Time Data
inferredcites →https://doi.org/10.1007/s00134-008-1335-xopenalex-snapshot
A national analysis of the relationship between hospital factors and post-cardiac arrest mortality
inferredcites →https://doi.org/10.1007/springerreference_64292openalex-snapshot
Fixed Time Span
inferredcites →https://doi.org/10.1016/b978-0-12-398499-9.00015-7openalex-snapshot
Time-Frequency Diagnosis, Condition Monitoring, and Fault Detection
inferredcites →https://doi.org/10.1016/j.ajem.2017.06.012openalex-snapshot
Characteristics and outcome among 14,933 adult cases of in-hospital cardiac arrest: A nationwide study with the emphasis on gender and age
inferredcites →https://doi.org/10.1016/j.cmpb.2019.06.010openalex-snapshot
An intelligent warning model for early prediction of cardiac arrest in sepsis patients
inferredcites →https://doi.org/10.1016/j.compbiomed.2017.08.015openalex-snapshot
Learning representations for the early detection of sepsis with deep neural networks
inferredcites →https://doi.org/10.1016/j.resuscitation.2015.08.016openalex-snapshot
The prevalence and significance of abnormal vital signs prior to in-hospital cardiac arrest
inferredcites →https://doi.org/10.1016/j.resuscitation.2015.10.010openalex-snapshot
Vital signs: Vital for surviving in-hospital cardiac arrest?
inferredcites →https://doi.org/10.1016/s0300-9572(03)00215-6openalex-snapshot
Cardiopulmonary resuscitation of adults in the hospital: A report of 14 720 cardiac arrests from the National Registry of Cardiopulmonary Resuscitation
inferredcites →https://doi.org/10.1036/1097-8542.265500openalex-snapshot
Fold and fold systems
inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot
Deep learning
inferredcites →https://doi.org/10.1056/nejmoa0810245openalex-snapshot
Epidemiologic Study of In-Hospital Cardiopulmonary Resuscitation in the Elderly
inferredcites →https://doi.org/10.1093/bja/ael113openalex-snapshot
Integrated monitoring and analysis for early warning of patient deterioration
inferredcites →https://doi.org/10.1097/ccm.0000000000001571openalex-snapshot
Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards
inferredcites →https://doi.org/10.1097/ccm.0b013e3181fb7b1copenalex-snapshot
Cardiorespiratory instability before and after implementing an integrated monitoring system*
inferredcites →https://doi.org/10.1109/icassp.2017.7953252openalex-snapshot
Character-level language modeling with hierarchical recurrent neural networks
inferredcites →https://doi.org/10.1111/j.0006-341x.2000.00199.xopenalex-snapshot
A Multiple Imputation Approach to Cox Regression with Interval‐Censored Data
inferredcites →https://doi.org/10.1111/j.0006-341x.2000.00337.xopenalex-snapshot
Time‐Dependent ROC Curves for Censored Survival Data and a Diagnostic Marker
inferredcites →https://doi.org/10.1126/scitranslmed.aab3719openalex-snapshot
A targeted real-time early warning score (TREWScore) for septic shock
inferredcites →https://doi.org/10.1161/jaha.118.008678openalex-snapshot
An Algorithm Based on Deep Learning for Predicting In‐Hospital Cardiac Arrest
inferredcites →https://doi.org/10.1186/s13054-015-0807-yopenalex-snapshot
Rapid response systems: are they really effective?
inferredcites →https://doi.org/10.1371/journal.pmed.1002709openalex-snapshot
Characterising risk of in-hospital mortality following cardiac arrest using machine learning: A retrospective international registry study
inferredcites →https://doi.org/10.1371/journal.pone.0210875openalex-snapshot
The value of vital sign trends in predicting and monitoring clinical deterioration: A systematic review
inferredcites →https://doi.org/10.2307/2988004openalex-snapshot
The Statistical Analysis of Failure Time Data.
inferredcites →https://doi.org/10.3346/jkms.2015.30.1.7openalex-snapshot
A De-identification Method for Bilingual Clinical Texts of Various Note Types
inferredcites →https://doi.org/10.3982/ecta5317openalex-snapshot
Identification of Treatment Effects Using Control Functions in Models With Continuous, Endogenous Treatment and Heterogeneous Effects
inferredcites →https://doi.org/10.4266/acc.2018.00290openalex-snapshot
Deep Learning in the Medical Domain: Predicting Cardiac Arrest Using Deep Learning

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/10.1007/978-3-031-49410-9_7openalex-snapshot
Clinical Decision Support System to Managing Beds in ICU
inferred← citeshttps://doi.org/10.1007/978-3-031-67951-3_24openalex-snapshot
Measuring Complex and Macro Research in Rapid Response Systems
inferred← citeshttps://doi.org/10.1016/j.artmed.2023.102696openalex-snapshot
GRU-D-Weibull: A novel real-time individualized endpoint prediction
inferred← citeshttps://doi.org/10.1016/j.cmpb.2021.106568openalex-snapshot
Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring
inferred← citeshttps://doi.org/10.1016/j.cpcardiol.2024.102454openalex-snapshot
Transforming clinical cardiology through neural networks and deep learning: A guide for clinicians
inferred← citeshttps://doi.org/10.1016/j.engappai.2026.115617openalex-snapshot
Federated explainable artificial intelligence framework for real time cardiovascular risk prediction using multimodal data
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.smhl.2022.100294openalex-snapshot
Attempting cardiac arrest prediction using artificial intelligence on vital signs from Electronic Health Records
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/10.1109/ccet56606.2022.10080284openalex-snapshot
Real-Time Classification of Cardiac Events in Arrhythmia Disease
inferred← citeshttps://doi.org/10.1109/embc58623.2025.11254649openalex-snapshot
Continuous Cardiac Arrest Prediction in ICU using PPG Foundation Model
inferred← citeshttps://doi.org/10.1109/icodsa58501.2023.10277367openalex-snapshot
A Deep Learning Approach for Automated Prediction of Cardiac Arrest from Vital Sign Data of Intensive Care Unit Patients
inferred← citeshttps://doi.org/10.15420/aer.2022.42openalex-snapshot
Artificial Intelligence in Ventricular Arrhythmias and Sudden Death
inferred← citeshttps://doi.org/10.17712/nsj.2021.4.20210056openalex-snapshot
Derivation and validation of the CANP scoring model for predicting the neurological outcome in post-cardiac arrest patients
inferred← citeshttps://doi.org/10.18495/comengapp.v12i3.457openalex-snapshot
Forecasting Of Intensive Care Unit Patient Heart Rate Using Long Short-Term Memory
inferred← citeshttps://doi.org/10.18517/ijaseit.13.3.17343openalex-snapshot
A Prediction of in-Hospital Cardiac Arrest Risk Scoring Based on Machine Learning
inferred← citeshttps://doi.org/10.2196/20645openalex-snapshot
Marrying Medical Domain Knowledge With Deep Learning on Electronic Health Records: A Deep Visual Analytics Approach
inferred← citeshttps://doi.org/10.2196/22183openalex-snapshot
Interpretable Conditional Recurrent Neural Network for Weight Change Prediction: Algorithm Development and Validation Study
inferred← citeshttps://doi.org/10.2196/26426openalex-snapshot
Event Prediction Model Considering Time and Input Error Using Electronic Medical Records in the Intensive Care Unit: Retrospective Study
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/10.3389/fmedt.2025.1681059openalex-snapshot
Use of artificial intelligence in predicting in-hospital cardiac and respiratory arrest in an acute care environment—implications for clinical practice
inferred← citeshttps://doi.org/10.3390/applbiosci4010006openalex-snapshot
Review of the Simulators Used in Pharmacology Education and Statistical Models When Creating the Simulators
inferred← citeshttps://doi.org/10.3390/biomedinformatics4010003openalex-snapshot
An Open-Access Dataset of Hospitalized Cardiac-Arrest Patients: Machine-Learning-Based Predictions Using Clinical Documentation
inferred← citeshttps://doi.org/10.4018/979-8-3693-2703-6.ch015openalex-snapshot
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