A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit

Xia, Jing; Su, Pan; Zhu, Min; Cai, Guolong; Yan, Ming; Su, Qun; Yan, Jing; Ning, Gangmin

doi:10.1155/2019/8152713

Cited by 28 publications

(35 citation statements)

References 39 publications

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“…It may not provide the best performance in the first 6 hours as many values may not be available during this initial period. In [63], J. Xia et al proposed an ensemble model based on the long short-term memory (LSTM) technique for mortality prediction. The idea behind this work is to use two LSTM layers based on 50 features extracted in the first 24 hours.…”

Section: B Ml-based Systemsmentioning

confidence: 99%

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

et al. 2020

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The intensive care unit (ICU) admits the most seriously ill patients requiring extensive monitoring. Early ICU mortality prediction is crucial for identifying patients who are at great risk of dying and for providing suitable interventions to save their lives. Accordingly, early prediction of patients at high mortality risk will enable their provision of appropriate and timely medical services. Although various severity scores and machine-learning models have recently been developed for early mortality prediction, such prediction remains challenging. This paper proposes a novel stacking ensemble approach to predict the mortality of ICU patients. Our approach is more accurate and medically intuitive compared to the literature work. Data were prepared and feature selection was processed under the supervision of the ICU domain expert. The data were split into six modalities based on the expert's decisions. For the prediction process, a separate classifier was selected for each modality based on the performance of the classifiers. We utilized the most popular and diverse classifiers in the literature, including linear discriminant analysis, decision tree (DT), multilayer perceptron, k-nearest neighbor, and logistic regression (LR). Then, a stacking ensemble classifier was constructed and optimized based on the fusion of these five classifier decisions. The framework was evaluated using 10,664 patients from the medical information mart for intensive care (MIMIC III) benchmark dataset. To predict patient mortality, extensive experiments were conducted using the patients' time series data of different lengths. For each patient, the first 6, 12, and 24 hours of the first stay were tested. The results indicate that our model outperformed the state-of-the-art approaches in terms of accuracy (94.4%), F1 score (93.7%), precision (96.4%), recall (91.1%), and area under the receiver operator characteristic (ROC) curve (93.3%). These results demonstrate the ability and efficiency of our approach to predict ICU mortality.

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Section: B Ml-based Systemsmentioning

confidence: 99%

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

et al. 2020

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“…LSTM networks have also shown good performances in various domains such as meteorology [22], finance [23,24], medicine [25,26], image description generation [27,28], motion prediction in video sequences [29,30] and machine translation [31,32].…”

Section: A Recurrent Neural Network and Long Short-term Memorymentioning

confidence: 99%

LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

Chiny¹,

Chihab²,

Bencharef³

et al. 2021

IJACSA

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Most sentiment analysis models that use supervised learning algorithms consume a lot of labeled data in the training phase in order to give satisfactory results. This is usually expensive and leads to high labor costs in real-world applications. This work consists in proposing a hybrid sentiment analysis model based on a Long Short-Term Memory network, a rulebased sentiment analysis lexicon and the Term Frequency-Inverse Document Frequency weighting method. These three (input) models are combined in a binary classification model. In the latter, each of these algorithms has been implemented: Logistic Regression, k-Nearest Neighbors, Random Forest, Support Vector Machine and Naive Bayes. Then, the model has been trained on a limited amount of data from the IMDB dataset. The results of the evaluation on the IMDB data show a significant improvement in the Accuracy and F1 score compared to the best scores recorded by the three input models separately. On the other hand, the proposed model was able to transfer the knowledge gained on the IMDB dataset to better handle a new data from Twitter US Airlines Sentiments dataset.

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“…We also experimented with the vanilla cross-entropy and additionally we used the Focal Loss. Xia et al [13] used an ensemble algorithm of LSTMs to deal with heterogeneous ICU data for mortality prediction. The eventual ensemble models make their predictions by merging the results of multiple parallel LSTM classifiers.…”

Section: Related Workmentioning

confidence: 99%

“…Most importantly and in contrast with the above-mentioned studies, we were extremely conservative when filtering out patients. For instance, in [13], which is a study substantially similar to ours, they only considered patients with a time window of 10 days. On the contrary, in our work, the filtering is not very restrictive and, consequently, the lengths of the dynamic data exhibit high variance.…”

Section: Related Workmentioning

confidence: 99%

Predicting the Evolution of COVID-19 Mortality Risk: a Recurrent Neural Network Approach

Villegas

González-Agirre

Gutiérrez-Fandiño

et al. 2020

Preprint

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BackgroundThe propagation of COVID-19 in Spain prompted the declaration of the state of alarm on March 14, 2020. On 2 December 2020, the infection had been confirmed in 1,665,775 patients and caused 45,784 deaths. This unprecedented health crisis challenged the ingenuity of all professionals involved. Decision support systems in clinical care and health services management were identified as crucial in the fight against the pandemic.MethodsThis study applies Deep Learning techniques for mortality prediction of COVID-19 patients. Two datasets with clinical information (medication, laboratory tests, vital signs etc.) were used. They are comprised of 2,307 and 3,870 COVID-19 infected patients admitted to two Spanish hospital chains. Firstly, we built a sequence of temporal events gathering all the clinical information for each patient. Next, we used the temporal sequences to train a Recurrent Neural Network (RNN) model with an attention mechanism exploring interpretability. We conducted extensive experiments and trained the RNNs in different settings, performing hyperparameter search and cross-validation. We ensembled resulting RNNs to reduce variability and enhance sensitivity.ResultsWe assessed the performance of our models using global metrics, by averaging the performance across all the days in the sequences. We also measured day-by-day metrics starting from the day of hospital admission and the outcome day and evaluated the daily predictions. Regarding sensitivity, when compared to more traditional models, our best two RNN ensemble models outperform a Support Vector Classifier in 6 and 16 percentage points, and Random Forest in 23 and 18 points. For the day-by-day predictions from the outcome date, the models also achieved better results than baselines showing its ability towards early predictions.ConclusionsWe have shown the feasibility of our approach to predict the clinical outcome of patients infected with SARS-CoV-2. The result is a time series model that can support decision-making in healthcare systems and aims at interpretability. The system is robust enough to deal with real world data and it is able to overcome the problems derived from the sparsity and heterogeneity of the data. In addition, the approach was validated using two datasets showing substantial differences. This not only validates the robustness of the proposal but also meets the requirements of a real scenario where the interoperability between hospitals’ datasets is difficult to achieve.

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A Long Short-Term Memory Ensemble Approach for Improving the Outcome Prediction in Intensive Care Unit

Cited by 28 publications

References 39 publications

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

Intensive Care Unit Mortality Prediction: An Improved Patient-Specific Stacking Ensemble Model

LSTM, VADER and TF-IDF based Hybrid Sentiment Analysis Model

Predicting the Evolution of COVID-19 Mortality Risk: a Recurrent Neural Network Approach

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