A recurrent neural network-based predictive model for pressure ulcers based on an intensive care unit dataset (MIMIC-III) (Preprint)

2021 ◽  
Author(s):  
Minseop Park ◽  
Hyeok Choi ◽  
Hee-Sung Ahn ◽  
Hee-Ju Kang ◽  
Saehoon Kim ◽  
...  
Keyword(s):  
Neural Network ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Dynamic Features ◽  
Braden Scale ◽  
Mlp Model ◽  
Mimic Iii

BACKGROUND A pressure ulcer (PU) is a localized cutaneous injury caused by pressure or shear, which usually occurs in the region of a bony prominence. PUs are common in hospitalized patients and cause complications including infection. OBJECTIVE This study aimed to build a recurrent neural network-based algorithm to predict PUs 24 hours before their occurrence. METHODS This study analyzed a freely accessible intensive care unit (ICU) dataset, MIMIC- III. Deep learning and machine learning algorithms including long short-term memory (LSTM), multilayer perceptron (MLP), and XGBoost were applied to 37 dynamic features (including the Braden scale, vital signs and laboratory results, and interventions to reduce the risk of PUs) and 35 static features (including the length of time spent in the ICU, demographics, and comorbidities). Their outcomes were compared in terms of the area under the receiver operating characteristic (AUROC) and the area under the precision-recall curve (AUPRC). RESULTS A total of 1,048 cases of PUs (10.0%) and 9,402 controls (90.0%) without PUs satisfied the inclusion criteria for analysis. The LSTM + MLP model (AUROC: 0.7929 ± 0.0095, AUPRC: 0.4819 ± 0.0109) outperformed the other models, namely: MLP model (AUROC: 0.7777 ± 0.0083, AUPRC: 0.4527 ± 0.0195) and XGBoost (AUROC: 0.7465 ± 0.0087, AUPRC: 0.4052 ± 0.0087). Various features, including the length of time spent in the ICU, Glasgow coma scale, and the Braden scale, contributed to the prediction model. CONCLUSIONS This study suggests that recurrent neural network-based algorithms such as LSTM can be applied to evaluate the risk of PUs in ICU patients.

scholarly journals Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study (Preprint)

2019 ◽  
Author(s):  
Longxiang Su ◽  
Chun Liu ◽  
Dongkai Li ◽  
Jie He ◽  
Fanglan Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Data Set ◽  
Machine Learning Methods ◽  
Heparin Treatment ◽  
Neural Network Algorithm ◽  
Mimic Iii ◽  
Machine Learning Models

BACKGROUND Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. OBJECTIVE The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. METHODS Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. RESULTS Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. CONCLUSIONS The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

Intracranial pressure model in intensive care unit using a simple recurrent neural network through time

2004 ◽  
Vol 57 ◽  
pp. 239-256 ◽  
Author(s):  
Jiann-Shing Shieh ◽  
Chi-Fong Chou ◽  
Sheng-Jean Huang ◽  
Ming-Chien Kao
Keyword(s):  
Neural Network ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Intracranial Pressure ◽  
Recurrent Neural Network

scholarly journals Toward Optimal Heparin Dosing by Comparing Multiple Machine Learning Methods: Retrospective Study

10.2196/17648 ◽  
2020 ◽  
Vol 8 (6) ◽  
pp. e17648
Author(s):  
Longxiang Su ◽  
Chun Liu ◽  
Dongkai Li ◽  
Jie He ◽  
Fanglan Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Intensive Care Unit ◽  
Intensive Care ◽  
Data Set ◽  
Machine Learning Methods ◽  
Heparin Treatment ◽  
Neural Network Algorithm ◽  
Mimic Iii ◽  
Machine Learning Models

Background Heparin is one of the most commonly used medications in intensive care units. In clinical practice, the use of a weight-based heparin dosing nomogram is standard practice for the treatment of thrombosis. Recently, machine learning techniques have dramatically improved the ability of computers to provide clinical decision support and have allowed for the possibility of computer generated, algorithm-based heparin dosing recommendations. Objective The objective of this study was to predict the effects of heparin treatment using machine learning methods to optimize heparin dosing in intensive care units based on the predictions. Patient state predictions were based upon activated partial thromboplastin time in 3 different ranges: subtherapeutic, normal therapeutic, and supratherapeutic, respectively. Methods Retrospective data from 2 intensive care unit research databases (Multiparameter Intelligent Monitoring in Intensive Care III, MIMIC-III; e–Intensive Care Unit Collaborative Research Database, eICU) were used for the analysis. Candidate machine learning models (random forest, support vector machine, adaptive boosting, extreme gradient boosting, and shallow neural network) were compared in 3 patient groups to evaluate the classification performance for predicting the subtherapeutic, normal therapeutic, and supratherapeutic patient states. The model results were evaluated using precision, recall, F1 score, and accuracy. Results Data from the MIMIC-III database (n=2789 patients) and from the eICU database (n=575 patients) were used. In 3-class classification, the shallow neural network algorithm performed the best (F1 scores of 87.26%, 85.98%, and 87.55% for data set 1, 2, and 3, respectively). The shallow neural network algorithm achieved the highest F1 scores within the patient therapeutic state groups: subtherapeutic (data set 1: 79.35%; data set 2: 83.67%; data set 3: 83.33%), normal therapeutic (data set 1: 93.15%; data set 2: 87.76%; data set 3: 84.62%), and supratherapeutic (data set 1: 88.00%; data set 2: 86.54%; data set 3: 95.45%) therapeutic ranges, respectively. Conclusions The most appropriate model for predicting the effects of heparin treatment was found by comparing multiple machine learning models and can be used to further guide optimal heparin dosing. Using multicenter intensive care unit data, our study demonstrates the feasibility of predicting the outcomes of heparin treatment using data-driven methods, and thus, how machine learning–based models can be used to optimize and personalize heparin dosing to improve patient safety. Manual analysis and validation suggested that the model outperformed standard practice heparin treatment dosing.

scholarly journals A Machine Learning Approach for Anaerobic Reactor Performance Prediction Using Long Short-Term Memory Recurrent Neural Network

2021 ◽  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Z Score ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Input Variables

Abstract. Predictive models are important to help manage high-value assets and to ensure optimal and safe operations. Recently, advanced machine learning algorithms have been applied to solve practical and complex problems, and are of significant interest due to their ability to adaptively ‘learn’ in response to changing environments. This paper reports on the data preparation strategies and the development and predictive capability of a Long Short-Term Memory recurrent neural network model for anaerobic reactors employed at Melbourne Water’s Western Treatment Plant for sewage treatment that includes biogas harvesting. The results show rapid training and higher accuracy in predicting biogas production when historical data, which include significant outliers, are preprocessed with z-score standardisation in comparison to those with max-min normalisation. Furthermore, a trained model with a reduced number of input variables via the feature selection technique based on Pearson’s correlation coefficient is found to yield good performance given sufficient dataset training. It is shown that the overall best performance model comprises the reduced input variables and data processed with z-score standardisation. This initial study provides a useful guide for the implementation of machine learning techniques to develop smarter structures and management towards Industry 4.0 concepts.

scholarly journals Data-Driven Stability Assessment of Multilayer Long Short-Term Memory Networks

2021 ◽  
Vol 11 (4) ◽  
pp. 1829
Author(s):  
Davide Grande ◽  
Catherine A. Harris ◽  
Giles Thomas ◽  
Enrico Anderlini
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Short Term Memory ◽  
Moving Average ◽  
Machine Learning Algorithms ◽  
Physical Models ◽  
Data Driven ◽  
The Stability ◽  
And Control ◽  
Nonlinear Autoregressive

Recurrent Neural Networks (RNNs) are increasingly being used for model identification, forecasting and control. When identifying physical models with unknown mathematical knowledge of the system, Nonlinear AutoRegressive models with eXogenous inputs (NARX) or Nonlinear AutoRegressive Moving-Average models with eXogenous inputs (NARMAX) methods are typically used. In the context of data-driven control, machine learning algorithms are proven to have comparable performances to advanced control techniques, but lack the properties of the traditional stability theory. This paper illustrates a method to prove a posteriori the stability of a generic neural network, showing its application to the state-of-the-art RNN architecture. The presented method relies on identifying the poles associated with the network designed starting from the input/output data. Providing a framework to guarantee the stability of any neural network architecture combined with the generalisability properties and applicability to different fields can significantly broaden their use in dynamic systems modelling and control.

scholarly journals Diuretic strategies in patients with resistance to loop-diuretics in the intensive care unit: A retrospective study from the MIMIC-III database

2021 ◽  
Vol 65 ◽  
pp. 282-291
Author(s):  
Jean-Maxime Côté ◽  
Josée Bouchard ◽  
Patrick T. Murray ◽  
William Beaubien-Souligny
Keyword(s):  
Intensive Care Unit ◽  
Intensive Care ◽  
Retrospective Study ◽  
Loop Diuretics ◽  
Mimic Iii

scholarly journals Recurrent neural network-based acute concussion classifier using raw resting state EEG data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Karun Thanjavur ◽  
Arif Babul ◽  
Brandon Foran ◽  
Maya Bielecki ◽  
Adam Gilchrist ◽  
...  
Keyword(s):  
Neural Network ◽  
Recurrent Neural Network ◽  
Resting State ◽  
Short Term Memory ◽  
Characteristic Curve ◽  
Health Concern ◽  
Adolescent Athletes ◽  
Individual Level ◽  
Diffuse Brain Injury ◽  
Eeg Data

AbstractConcussion is a global health concern. Despite its high prevalence, a sound understanding of the mechanisms underlying this type of diffuse brain injury remains elusive. It is, however, well established that concussions cause significant functional deficits; that children and youths are disproportionately affected and have longer recovery time than adults; and that individuals suffering from a concussion are more prone to experience additional concussions, with each successive injury increasing the risk of long term neurological and mental health complications. Currently, the most significant challenge in concussion management is the lack of objective, clinically- accepted, brain-based approaches for determining whether an athlete has suffered a concussion. Here, we report on our efforts to address this challenge. Specifically, we introduce a deep learning long short-term memory (LSTM)-based recurrent neural network that is able to distinguish between non-concussed and acute post-concussed adolescent athletes using only short (i.e. 90 s long) samples of resting state EEG data as input. The athletes were neither required to perform a specific task nor expected to respond to a stimulus during data collection. The acquired EEG data were neither filtered, cleaned of artefacts, nor subjected to explicit feature extraction. The LSTM network was trained and validated using data from 27 male, adolescent athletes with sports related concussion, benchmarked against 35 non-concussed adolescent athletes. During rigorous testing, the classifier consistently identified concussions with an accuracy of > 90% and achieved an ensemble median Area Under the Receiver Operating Characteristic Curve (ROC/AUC) equal to 0.971. This is the first instance of a high-performing classifier that relies only on easy-to-acquire resting state, raw EEG data. Our concussion classifier represents a promising first step towards the development of an easy-to-use, objective, brain-based, automatic classification of concussion at an individual level.

Disturbance learning controller design for unmanned surface vehicle using LSTM technique of recurrent neural network

2021 ◽  
pp. 1-11
Author(s):  
Sang-Ki Jeong ◽  
Dea-Hyeong Ji ◽  
Ji-Youn Oh ◽  
Jung-Min Seo ◽  
Hyeung-Sik Choi
Keyword(s):  
Neural Network ◽  
Control System ◽  
Flow Velocity ◽  
Recurrent Neural Network ◽  
Pid Controller ◽  
Short Term Memory ◽  
Ocean Currents ◽  
Ocean Current ◽  
Movement Speed ◽  
Marine Research

In this study, to effectively control small unmanned surface vehicles (USVs) for marine research, characteristics of ocean current were learned using the long short-term memory (LSTM) model algorithm of a recurrent neural network (RNN), and ocean currents were predicted. Using the results, a study on the control of USVs was conducted. A control system model of a small USV equipped with two rear thrusters and a front thruster arranged horizontally was designed. The system was also designed to determine the output of the controller by predicting the speed of the following currents and utilizing this data as a system disturbance by learning data from ocean currents using the LSTM algorithm of a RNN. To measure ocean currents on the sea when a small USV moves, the speed and direction of the ship’s movement were measured using speed, azimuth, and location (latitude and longitude) data from GPS. In addition, the movement speed of the fluid with flow velocity is measured using the installed flow velocity measurement sensor. Additionally, a control system was designed to control the movement of the USV using an artificial neural network-PID (ANN-PID) controller [12]. The ANN-PID controller can manage disturbances by adjusting the control gain. Based on these studies, the control results were analyzed, and the control algorithm was verified through a simulation of the applied control system [8, 9].

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