A step towards the application of an artificial intelligence model in the prediction of intradialytic complications

Elbasha, Ahmed Mustafa; Naga, Yasmine; Othman, Mai; Moussa, Nancy Diaa; El-Wakil, Hala S

doi:10.1080/20905068.2021.2024349

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…Deep learning has been applied to predict the binary occurrence of intradialytic complications, such as IDH, IDHTN, and other clinical symptoms, using a tabular dataset of hemodialysis sessions 14 , 15 , 28 , 29 . We developed the RNN model to predict IDH in real-time using a time-series dataset, and its performance was acceptable 12 .…”

Section: Discussionmentioning

confidence: 99%

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Real-time dual prediction of intradialytic hypotension and hypertension using an explainable deep learning model

Yun,

Yang,

Kim

et al. 2023

Sci Rep

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Both intradialytic hypotension (IDH) and hypertension (IDHTN) are associated with poor outcomes in hemodialysis patients, but a model predicting dual outcomes in real-time has never been developed. Herein, we developed an explainable deep learning model with a sequence-to-sequence-based attention network to predict both of these events simultaneously. We retrieved 302,774 hemodialysis sessions from the electronic health records of 11,110 patients, and these sessions were split into training (70%), validation (10%), and test (20%) datasets through patient randomization. The outcomes were defined when nadir systolic blood pressure (BP) < 90 mmHg (termed IDH-1), a decrease in systolic BP ≥ 20 mmHg and/or a decrease in mean arterial pressure ≥ 10 mmHg (termed IDH-2), or an increase in systolic BP ≥ 10 mmHg (i.e., IDHTN) occurred within 1 h. We developed a temporal fusion transformer (TFT)-based model and compared its performance in the test dataset, including receiver operating characteristic curve (AUROC) and area under the precision-recall curves (AUPRC), with those of other machine learning models, such as recurrent neural network, light gradient boosting machine, random forest, and logistic regression. Among all models, the TFT-based model achieved the highest AUROCs of 0.953 (0.952–0.954), 0.892 (0.891–0.893), and 0.889 (0.888–0.890) in predicting IDH-1, IDH-2, and IDHTN, respectively. The AUPRCs in the TFT-based model for these outcomes were higher than the other models. The factors that contributed the most to the prediction were age and previous session, which were time-invariant variables, as well as systolic BP and elapsed time, which were time-varying variables. The present TFT-based model predicts both IDH and IDHTN in real time and offers explainable variable importance.

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Section: Discussionmentioning

confidence: 99%

“…Due to the repeated occurrence of IDH and IDHTN, information on previous hemodialysis sessions may help predict outcomes in the next session 12 . However, most studies used this information as binary features only and not as a concrete structure to encode all information 14 , 15 .…”

Section: Introductionmentioning

confidence: 99%

Real-time dual prediction of intradialytic hypotension and hypertension using an explainable deep learning model

Yun,

Yang,

Kim

et al. 2023

Sci Rep

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“…MLP network (also called Artificial Neural Network ANN) consists of an input layer, one or more hidden layers, and an output layer [ 13 , 14 ]. Each node, or artificial neuron, is connected to others and has a weight and threshold that go along with it [ 8 ]. Any node whose output exceeds the defined threshold value is activated and begins providing data to the next layer.…”

Section: Related Workmentioning

confidence: 99%

“…In a previous paper [ 8 ], our team developed an artificial neural network model (which is also known as multi-layer perceptron (MLP)) to predict the occurrence of 7 intra-dialytic clinical events (see Additional file 1 : Table S2 for target outcomes) utilizing a test set collected from a regional tertiary dialysis unit in Alexandria, Egypt. The objective was to create a multiclass prediction model that can distinguish between 8 different classes efficiently (no complications or one of the seven studied complications).…”

Section: Introductionmentioning

confidence: 99%

Early prediction of hemodialysis complications employing ensemble techniques

et al. 2022

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Background and objectives Hemodialysis complications remain a critical threat among dialysis patients. They result in sudden termination of the session which impacts the efficiency of dialysis. As intra-dialytic complications are the result of the interplay of multiple factors, artificial intelligence can aid in their early prediction. This research aims to compare different machine learning tools for the early prediction of the most frequent hemodialysis complications with high performance, using the fewest predictors for easier practical implementation. Methods Fifty different variables were recorded during 6000 hemodialysis sessions performed in a regional dialysis unit in Egypt. The filter technique was used to extract the most relevant features. Then, five individual classifiers and three ensemble approaches were implemented to predict the occurrence of intra-dialytic complications. Different subsets of 25, 12 and 6 from the 50 collected features were tested. Results Random forest yielded the highest accuracy of 98% with the least training time using 12 features in a balanced dataset, while the gradient boosting allowed obtaining the highest F1-score of 94%, 92%, and 78% in the prediction of hypotension, hypertension, and dyspnea, respectively, in imbalanced datasets. Conclusion Applying different machine learning algorithms to big datasets can improve accuracy, reduce training time and model complexity allowing simple implementation in clinical practice. Our models can help nephrologists predict and possibly prevent dialysis complications.

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Artificial Intelligence Based Robust Clinical Data Classification for Intradialytic Hypotension Diagnosis

Mohammed,

Obaid,

Hadi

et al. 2023

2023 6th International Conference on Engineering Technology and Its Applications (IICETA)

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A step towards the application of an artificial intelligence model in the prediction of intradialytic complications

Cited by 3 publications

References 34 publications

Real-time dual prediction of intradialytic hypotension and hypertension using an explainable deep learning model

Real-time dual prediction of intradialytic hypotension and hypertension using an explainable deep learning model

Early prediction of hemodialysis complications employing ensemble techniques

Artificial Intelligence Based Robust Clinical Data Classification for Intradialytic Hypotension Diagnosis

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