Early Diagnosis and Prediction of Sepsis Shock by Combining Static and Dynamic Information Using Convolutional-LSTM

Chen, Lin; Zhangy, Yuan; Ivy, Julie S.; Capan, Müge; Arnold, Ryan; Huddleston, Jeanne M.; Chi, Min

doi:10.1109/ichi.2018.00032

Cited by 67 publications

(36 citation statements)

References 21 publications

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“…In this experiment, we use six methods as our baselines: logistic regression (LR) with L2 regularization, support vector machine (SVM), decision tree (DT), random forest (RF), GRU, and the state-of-the-art LSTM-based method [6] for both the diagnosis task and mortality prediction task. Because the results are similar, we only listed the best of the top two in our paper for each of the tasks.…”

Section: Compared Methodsmentioning

confidence: 99%

IDDSAM: An Integrated Disease Diagnosis and Severity Assessment Model for Intensive Care Units

et al. 2020

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People are admitted to intensive care units (ICUs) because they need complete support for failing organ systems, constant monitoring, routine nursing care, and treatment. A critical or intensive illness is different from conventional or chronic diseases that most people are likely to have previously encountered. Such an illness is often unexpected and without warnings and can suddenly strike the previously fit. High levels of treatment and support are generally required to prevent life-threatening complications for the patents. Two of the most noticeable actions during an ICU stay are disease diagnosis and severity assessment of the patients. Unlike the majority of previous approaches where diagnosis and severity assessment are studied separately, we treat these actions as two tasks in an integrated procedure that clinicians must be able to quickly and accurately conduct such that patients are given the best possible chance for therapeutic success. In this paper, we propose an integrated disease diagnosis and severity assessment model (IDDSAM) to diagnose and assess diseases. Moreover, accompanying the prediction, we also provide an evidence-based explanation. IDDSAM is a multisource multitask model that is based on an attention mechanism and utilizes shareable information from laboratory tests, bedside monitoring, and complications to support patients' severity assessment and in-hospital disease diagnoses. We use 50,430 ICU cases consisting of 46,520 patients from 50 kinds of diseases over nine classifications to evaluate our proposed model. The experimental results demonstrated that our model outperforms the existing state-of-the-art mortality and diagnosis prediction framework by 3.79% on average in terms of accuracy for the mortality prediction tasks and by 14.51% on average for the diagnosis tasks.

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Section: Compared Methodsmentioning

confidence: 99%

IDDSAM: An Integrated Disease Diagnosis and Severity Assessment Model for Intensive Care Units

et al. 2020

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“…The major detriment of the RNN model was vanishing gradient problem, LSTM increased the input and output capability of RNN to solve these issues and it uses logical memory to learn sequence vector. To deal with CVDs data temporal features could be learn by Intelligent Healthcare Platform (IHP) established on attention module based LSTM framework [23]. Moreover, to predict CVDs 4. distinct repositories in conjunction with Cleveland dataset is used [24].…”

Section: Review Of Relevant Workmentioning

confidence: 99%

Enhanced Accuracy of Heart Disease Prediction using Machine Learning and Recurrent Neural Networks Ensemble Majority Voting Method

Javid¹,

Alsaedi²,

Ghazali³

2020

IJACSA

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To solve many problems in data science, Machine Learning (ML) techniques implicates artificial intelligence which are commonly used. The major utilization of ML is to predict the conclusion established on the extant data. Using an established dataset machine determine emulate and spread them to an unfamiliar data sets to anticipate the conclusion. A few classification algorithm's accuracy prediction is satisfactory, although other perform limited accuracy. Different ML and Deep Learning (DL) networks established on ANN have been extensively recommended for the disclosure of heart disease in antecedent researches. In this paper, we used UCI Heart Disease dataset to test ML techniques along with conventional methods (i.e. random forest, support vector machine, K-nearest neighbor), as well as deep learning models (i.e. long short-term-memory and gated-recurrent unit neural networks). To improve the accuracy of weak algorithms we explore voting based model by combining multiple classifiers. A provisional cogent approach was used to regulate how the ensemble technique can be enforced to improve an accuracy in the heart disease prediction. The strength of the proposed ensemble approach such as voting based model is compelling in improving the prognosis accuracy of anemic classifiers and established adequate achievement in analyze risk of heart disease. A superlative increase of 2.1% accuracy for anemic classifiers was attained with the help of an ensemble voting based model.

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“…In addition, recurrent neural networks (RNNs) and long short-term networks (LSTMs) [23,24] were validated in terms of their performance on one-dimensional (1D) signals. In Reference [25], a CNN and a fully connected neural network were both incorporated into a deep neural network framework to improve LSTM. The framework outperformed the original LSTM for the early diagnosis and prediction of sepsis shock.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to the state-of-the-art models evaluated on standard benchmark electrocardiogram datasets, the proposed model produced better performance in detecting atrial fibrillation. The ideas in References [25,26] are very good references for multi-factor operating condition recognition based on vibration signals.…”

Section: Introductionmentioning

confidence: 99%

Multi-Factor Operating Condition Recognition Using 1D Convolutional Long Short-Term Network

Jiang

Lai

Zhang

et al. 2019

Sensors

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For a diesel engine, operating conditions have extreme importance in fault detection and diagnosis. Limited to various special circumstances, the multi-factor operating conditions of a diesel engine are difficult to measure, and the demand of automatic condition recognition based on vibration signals is urgent. In this paper, multi-factor operating condition recognition using a one-dimensional (1D) convolutional long short-term network (1D-CLSTM) is proposed. Firstly, a deep neural network framework is proposed based on a 1D convolutional neural network (CNN) and long short-Term network (LSTM). According to the characteristics of vibration signals of a diesel engine, batch normalization is introduced to regulate the input of each convolutional layer by fixing the mean value and variance. Subsequently, adaptive dropout is proposed to improve the model sparsity and prevent overfitting in model training. Moreover, the vibration signals measured under 12 operating conditions were used to verify the performance of the trained 1D-CLSTM classifier. Lastly, the vibration signals measured from another kind of diesel engine were applied to verify the generalizability of the proposed approach. Experimental results show that the proposed method is an effective approach for multi-factor operating condition recognition. In addition, the adaptive dropout can achieve better training performance than the constant dropout ratio. Compared with some state-of-the-art methods, the trained 1D-CLSTM classifier can predict new data with higher generalization accuracy.

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Early Diagnosis and Prediction of Sepsis Shock by Combining Static and Dynamic Information Using Convolutional-LSTM

Cited by 67 publications

References 21 publications

IDDSAM: An Integrated Disease Diagnosis and Severity Assessment Model for Intensive Care Units

IDDSAM: An Integrated Disease Diagnosis and Severity Assessment Model for Intensive Care Units

Enhanced Accuracy of Heart Disease Prediction using Machine Learning and Recurrent Neural Networks Ensemble Majority Voting Method

Multi-Factor Operating Condition Recognition Using 1D Convolutional Long Short-Term Network

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