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Monitoring ICU Mortality Risk with A Long Short-Term Memory Recurrent Neural Network
2020
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
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Specifically, our model uses latent semantic analysis (LSA) to encode the patients' states into low-dimensional embeddings, which are further fed to long short-term memory networks for mortality risk prediction ...
monitoring of patients' mortality risk. ...
These include (1) average pooling, (2) selfattention mechanism, (3) hidden space of the long short-term memory networks (LSTM), (4) hidden space of bidirectional LSTM. ...
pmid:31797590
pmcid:PMC6934094
fatcat:opqxzmyxanezbg3sd2c2uykupq
Deep Learning to Attend to Risk in ICU
[article]
2017
arXiv
pre-print
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The model is based on Long Short-Term Memory, and has layered attention mechanisms. At the sensing layer, the model decides whether to observe and incorporate parts of the current measurements. ...
Here we propose a deep learning architecture that can effectively handle these challenges for predicting ICU mortality outcomes. ...
Long Short-Term Memory A Long Short-Term Memory (LSTM) [Hochreiter and Schmidhuber, 1997 ] is a recurrent neural network. Let x t denotes the input vector at time t. ...
arXiv:1707.05010v1
fatcat:mfgnofghnzblvmveu2ugdepd74
Predicting ICU mortality by supervised bidirectional LSTM networks
2018
International Joint Conference on Artificial Intelligence
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To deal with these challenges, in this paper, we propose a novel ICU mortality prediction algorithm combining bidirectional LSTM (Long Short-Term Memory) model with supervised learning. ...
It is a big challenge to model time-series variables for mortality prediction in ICU, because physiological variables such as heart rate and blood pressure are sampled with inconsistent time frequencies ...
Bidirectional LSTM In recurrent neural networks, Long Short-Term Memory (LSTM) is a relatively more efficient structure. ...
dblp:conf/ijcai/ZhuFWLSW18
fatcat:2ccpdc36lbg5jijsxd37jz3t4i
AttDMM: An Attentive Deep Markov Model for Risk Scoring in Intensive Care Units
[article]
2021
arXiv
pre-print
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To this end, prediction algorithms have been developed that estimate the risk of mortality in ICUs. ...
To the best of our knowledge, AttDMM is the first ICU prediction model that jointly learns both long-term disease dynamics (via attention) and different disease states in health trajectory (via a latent ...
Examples of neural networks that have been adapted for ICU predictions are long short-term memory (LSTM) [15, 44, 52] and gated recurrent unit (GRU) [6, 10] . ...
arXiv:2102.04702v2
fatcat:3rhtfxfocnavvkipwlse5x2gbm
Updating mortality risk estimation in intensive care units from high-dimensional electronic health records with incomplete data
[article]
2022
medRxiv
pre-print
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, we developed predictive models of short-term mortality in ICU from longitudinal data collected throughout patients' stays of at least 48 hours. ...
AbstractContextIntensive care units (ICU) are subject to a high mortality rate, currently addressed by the implementation of scores (SAPS II, SOFA, APACHE II) assessing the risk of in-hospital mortality ...
network (CNN) [16, 17] , a bidirectional long short-term memory (LSTM) recurrent neural network [18] and a CNN-LSTM network [19] , which concatenated the information from the two previous networks ...
doi:10.1101/2022.04.28.22274405
fatcat:vgflzacfazgrrn7zmmkkh5alhy
Dynamic Mortality Risk Predictions in Pediatric Critical Care Using Recurrent Neural Networks
[article]
2017
arXiv
pre-print
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Viewing the trajectory of a patient as a dynamical system, a recurrent neural network was developed to learn the course of patient encounters in the Pediatric Intensive Care Unit (PICU) of a major tertiary ...
The RNN's ICU mortality predictions offer significant improvements over those from two clinically-used scores and static machine learning algorithms. ...
Acknowledgments This work was funded by a grant from the Laura P. and Leland K. Whittier Foundation. ...
arXiv:1701.06675v1
fatcat:v6aalmhcu5dzzern3fbfeobhx4
Artificial Intelligence for Clinical Decision Support in Sepsis
2021
Frontiers in Medicine
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Artificial intelligence (AI) is an evolving field in medicine, which has been used to develop a variety of innovative Clinical Decision Support Systems. ...
Despite the continuous development of medical technology in recent years, its morbidity and mortality are still high. ...
extraction of long-term and short-term memory (LSTM) neural networks (20) . ...
doi:10.3389/fmed.2021.665464
pmid:34055839
pmcid:PMC8155362
fatcat:a2jc22nujndpdklxbibhq2e7ae
Feature Explanations in Recurrent Neural Networks for Predicting Risk of Mortality in Intensive Care Patients
2021
Journal of Personalized Medicine
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In this study, we propose a data-driven framework for predicting the risk of mortality that combines high-accuracy recurrent neural networks with interpretable explanations. ...
Our model processes time-series of vital signs and laboratory observations to predict the probability of a patient's mortality in the intensive care unit (ICU). ...
Hence, only relevant information can pass through the hierarchy of the network. Thus, the LSTM has mechanisms to process both short-term and long-term memory components. ...
doi:10.3390/jpm11090934
pmid:34575711
pmcid:PMC8465577
fatcat:2rskwidj5fdkznmut6lwbp37yu
Improving Recurrent Neural Network Responsiveness to Acute Clinical Events
2021
IEEE Access
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A baseline Long Short-Term Memory (LSTM) model (k = 1), four LSTMs with increasing amounts of input data perseveration (k = 2 to k = 5), and an LSTM with an attention mechanism were trained to predict ...
Recurrent neural networks (RNN) have become popular for clinical decision support models but exhibit a delayed response to acute events. ...
RNN MODELS Many-to-many recurrent neural network models, consisting of stacked Long Short-Term Memory (LSTM) layers followed by a dense layer, were trained to predict ROM in the ICU of each patient episode ...
doi:10.1109/access.2021.3099996
fatcat:7miryacikvgnfmq6c4rf67ix2a
Improving Recurrent Neural Network Responsiveness to Acute Clinical Events
[article]
2020
arXiv
pre-print
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Recurrent neural networks (RNNs) have become common for training and deploying clinical decision support models. They frequently exhibit a delayed response to acute events. ...
When presented with data reflecting acute events, a model trained and deployed with input perseveration responds with more pronounced immediate changes in predictions and maintains globally robust performance ...
RNN MODELS Many-to-many recurrent neural network models, consisting of stacked Long Short-Term Memory (LSTM) layers followed by a dense layer, were trained to predict ICU mortality of each patient episode ...
arXiv:2007.14520v1
fatcat:im2unbnru5d4dfq2dhjsekprfe
ISeeU2: Visually Interpretable ICU mortality prediction using deep learning and free-text medical notes
[article]
2020
arXiv
pre-print
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Accurate mortality prediction allows Intensive Care Units (ICUs) to adequately benchmark clinical practice and identify patients with unexpected outcomes. ...
In this work we show a Deep Learning model trained on MIMIC-III to predict mortality using raw nursing notes, together with visual explanations for word importance. ...
Our second baseline is a recurrent neural network based on the Long Short Term Memory (LSTM). ...
arXiv:2005.09284v1
fatcat:tlti7ge7qzeqxjtme4polodzcq
A Novel and Reliable Framework of Patient Deterioration Prediction in Intensive Care Unit Based on Long Short-Term Memory-Recurrent Neural Network
2020
IEEE Access
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In this paper, a new model was proposed based on Long Short-Term Memory-Recurrent Neural Network (LSTM-RNN) to predict deterioration of ICU patients. ...
INDEX TERMS Genetic algorithm, long short-term memory, patient deterioration, prediction framework, and recurrent neural network. ...
[96] proposed a predictive model based on a Bidirectional Long Short-Term Memory (BiLSTM) network and bidirectional recurrent neural network (BiRNN) and utilizes different monitored parameter sequences ...
doi:10.1109/access.2020.3047186
fatcat:vil2w5eg4fakzjwanuyv2h7ysq
Heart Failure Diagnosis, Readmission, and Mortality Prediction Using Machine Learning and Artificial Intelligence Models
2020
Current Epidemiology Reports
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Summary Machine learning models can facilitate the identification of HF patients, as well as accurate patient-specific assessment of their risk for readmission and mortality. ...
The HF diagnosis, readmission, and mortality prediction are essential to develop personalized prevention and treatment plans. ...
Analysis and prediction of unplanned
intensive care unit readmission using
recurrent neural networks with long
short-term memory [23••]
Long short-term memory (LSTM) was
used to predict the readmission ...
doi:10.1007/s40471-020-00259-w
fatcat:5w3qaimcw5bcpha4gabp6ascum
Contextual Embeddings from Clinical Notes Improves Prediction of Sepsis
2021
AMIA Annual Symposium Proceedings
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Untreated sepsis can progress to septic shock and organ failure, making sepsis one of the leading causes of morbidity and mortality in hospitals. ...
pre-trained neural language representation model, allows for incorporation of unstructured data without an explicit need for ontology-based named-entity recognition and classification. ...
Acknowledgments This study was supported by an NIH Early Career Award (K01ES025445) to SN and a Halıcıoglu Data Science Institute doctoral fellowship to FA. ...
pmid:33936391
pmcid:PMC8075484
fatcat:5dwuwqh2nnc2zah3qupzdjfdzi
Dynamic Prediction of ICU Mortality Risk Using Domain Adaptation
2018
2018 IEEE International Conference on Big Data (Big Data)
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Gains in terms of AUC range from 4% to 8% for early predictions when compared with a recent state-of-the-art representative for ICU mortality prediction. ...
This enables dynamic predictions and creates a mortality risk space that describes the risk of a patient at a particular time. ...
signals at the higher level using a Long Short-Term Memory network (LSTM) [Hochreiter and Schmidhuber, 1997] . ...
doi:10.1109/bigdata.2018.8621927
dblp:conf/bigdataconf/MacambiraLVZ18
fatcat:q2ivp52uqjccpoit5pg7x5fuuy
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