A pattern mixture model with long short-term memory network for acute kidney injury prediction

Machine learning model for predicting acute kidney injury progression in critically ill patients

(2022) Canzheng Wei et al.   BMC Medical Informatics and Decision Making

Application of Interpretable Machine Learning for Early Prediction of Prognosis in Acute Kidney Injury

(2022) Chang Hu et al.   Computational and Structural Biotechnology Journal

Long-short-term memory machine learning of longitudinal clinical data accurately predicts acute kidney injury onset in COVID-19: a two-center study

(2022) Justin Y. Lu et al.   INTERNATIONAL JOURNAL OF INFECTIOUS DISEASES

A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients

(2021) Francesca Alfieri et al.   JOURNAL OF NEPHROLOGY

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

(2021) Nenad Tomašev et al.   Nature Protocols

A novel risk-predicted nomogram for sepsis associated-acute kidney injury among critically ill patients

(2021) Shanglin Yang et al.   BMC Nephrology

Prediction and visualization of acute kidney injury in intensive care unit using one-dimensional convolutional neural networks based on routinely collected data

(2021) Noriaki Sato et al.   COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE

Prediction of acute kidney injury in ICU with gradient boosting decision tree algorithms

(2021) Wenpeng Gao et al.   COMPUTERS IN BIOLOGY AND MEDICINE

Working Memory Connections for LSTM

(2021) Federico Landi et al.   NEURAL NETWORKS

An Efficient CNN-Based Deep Learning Model to Detect Malware Attacks (CNN-DMA) in 5G-IoT Healthcare Applications

(2021) Ankita Anand et al.   SENSORS

Identification and validation of biomarkers of persistent acute kidney injury: the RUBY study

(2020) Eric Hoste et al.   INTENSIVE CARE MEDICINE

Genetic variation implicates plasma angiopoietin-2 in the development of acute kidney injury sub-phenotypes

(2020) Pavan K. Bhatraju et al.   BMC Nephrology

Detection of pseudo brain tumors via stacked LSTM neural networks using MR spectroscopy signals

(2020) Emre Dandıl et al.   Biocybernetics and Biomedical Engineering

Time Series Classification-Based Correlational Neural Network With Bidirectional LSTM for Automated Detection of Kidney Disease

(2020) Navaneeth Bhaskar et al.   IEEE SENSORS JOURNAL

Early prediction of acute kidney injury following ICU admission using a multivariate panel of physiological measurements

(2019) Lindsay P. Zimmerman et al.   BMC Medical Informatics and Decision Making

A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures

(2019) Yong Yu et al.   NEURAL COMPUTATION

Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: A proof of concept

(2019) Nam K. Tran et al.   BURNS

A clinically applicable approach to continuous prediction of future acute kidney injury

(2019) Nenad Tomašev et al.   NATURE

Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics

(2019) Lasith Adhikari et al.   PLoS One

A prospective study of acute kidney injury in the intensive care unit: development and validation of a risk prediction model

(2019) Qi Wang et al.   Journal of Translational Medicine

Urinary Biochemistry in the Diagnosis of Acute Kidney Injury

(2018) Camila Lima et al.   DISEASE MARKERS

Paradigms of acute kidney injury in the intensive care setting

(2018) John A. Kellum et al.   Nature Reviews Nephrology

Proteomics and Metabolomics for AKI Diagnosis

(2018) David Marx et al.   SEMINARS IN NEPHROLOGY

Comparison of maximum likelihood approach, Diggle–Kenward selection model, pattern mixture model with MAR and MNAR dropout data

(2018) Nan Chen et al.   COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION