A self-correcting deep learning approach to predict acute conditions in critical care
In critical care, intensivists are required to continuously monitor high dimensional vital signs
and lab measurements to detect and diagnose acute patient conditions. This has always …
and lab measurements to detect and diagnose acute patient conditions. This has always …
[HTML][HTML] Self-correcting recurrent neural network for acute kidney injury prediction in critical care
NK Yuan - Health Data Science, 2021 - spj.science.org
Background. In critical care, intensivists are required to continuously monitor high-
dimensional vital signs and lab measurements to detect and diagnose acute patient …
dimensional vital signs and lab measurements to detect and diagnose acute patient …
[HTML][HTML] A clinically practical and interpretable deep model for ICU mortality prediction with external validation
Y Kang, X Jia, K Wang, Y Hu, J Guo… - AMIA annual …, 2020 - ncbi.nlm.nih.gov
Deep learning models are increasingly studied in the field of critical care. However, due to
the lack of external validation and interpretability, it is difficult to generalize deep learning …
the lack of external validation and interpretability, it is difficult to generalize deep learning …
[PDF][PDF] Benchmarking machine learning models on eICU critical care dataset
S Sheikhalishahi, V Balaraman… - arXiv preprint arXiv …, 2019 - researchgate.net
Progress of machine learning in critical care has been difficult to track, in part due to
absence of public benchmarks. Other fields of research (such as vision and NLP) have …
absence of public benchmarks. Other fields of research (such as vision and NLP) have …
Benchmarking machine learning models on multi-centre eICU critical care dataset
Progress of machine learning in critical care has been difficult to track, in part due to
absence of public benchmarks. Other fields of research (such as computer vision and …
absence of public benchmarks. Other fields of research (such as computer vision and …
Real-time machine learning model to predict short-term mortality in critically ill patients: development and international validation
Background A real-time model for predicting short-term mortality in critically ill patients is
needed to identify patients at imminent risk. However, the performance of the model needs …
needed to identify patients at imminent risk. However, the performance of the model needs …
Development and application of an intensive care medical data set for deep learning
S Zhao, P Liu, G Tang, Y Guo… - 2020 IEEE International …, 2020 - ieeexplore.ieee.org
A large number of patient healthcare data have been collected in the process of diagnosis
and treatment of intensive care medicine, which provides major benefits for patient safety …
and treatment of intensive care medicine, which provides major benefits for patient safety …
A deep learning model for real-time mortality prediction in critically ill children
SY Kim, S Kim, J Cho, YS Kim, IS Sol, Y Sung, I Cho… - Critical care, 2019 - Springer
Background The rapid development in big data analytics and the data-rich environment of
intensive care units together provide unprecedented opportunities for medical …
intensive care units together provide unprecedented opportunities for medical …
External validation of a deep learning prediction model for in-hospital mortality among ICU patients
S Zhao, P Liu, G Tang, Y Guo… - 2022 IEEE 2nd …, 2022 - ieeexplore.ieee.org
With increasing hospital adoption of electronic health record (EHR) systems worldwide, a
massive amount of EHR data are generated in intensive care practice, and deep learning …
massive amount of EHR data are generated in intensive care practice, and deep learning …
Deep Learning Model Utilization for Mortality Prediction in Mechanically Ventilated ICU Patients
Background: The requirement of mechanical ventilation has increased in recent years.
Patients in the intensive care unit (ICU) who undergo mechanical ventilation often …
Patients in the intensive care unit (ICU) who undergo mechanical ventilation often …