Heart failure disease prediction and stratification with temporal electronic health records data using patient representation

Liang, Y. T.; Guo, Chonghui

doi:10.1016/j.bbe.2022.12.008

Cited by 12 publications

(4 citation statements)

References 52 publications

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“…Liang et al [16] proposed a novel deep learning model called tBNA-PR to accurately predict heart failure and identify sub-phenotypes using temporal electronic health records (tEHRs) data. The model effectively captures the complexity and heterogeneity of the data to obtain informative patient representations.…”

Section: Related Workmentioning

confidence: 99%

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel

Mahmud,

Kabir,

Mridha

et al. 2023

Diagnostics

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Accurate prediction of heart failure can help prevent life-threatening situations. Several factors contribute to the risk of heart failure, including underlying heart diseases such as coronary artery disease or heart attack, diabetes, hypertension, obesity, certain medications, and lifestyle habits such as smoking and excessive alcohol intake. Machine learning approaches to predict and detect heart disease hold significant potential for clinical utility but face several challenges in their development and implementation. This research proposes a machine learning metamodel for predicting a patient’s heart failure based on clinical test data. The proposed metamodel was developed based on Random Forest Classifier, Gaussian Naive Bayes, Decision Tree models, and k-Nearest Neighbor as the final estimator. The metamodel is trained and tested utilizing a combined dataset comprising five well-known heart datasets (Statlog Heart, Cleveland, Hungarian, Switzerland, and Long Beach), all sharing 11 standard features. The study shows that the proposed metamodel can predict heart failure more accurately than other machine learning models, with an accuracy of 87%.

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Section: Related Workmentioning

confidence: 99%

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel

Mahmud,

Kabir,

Mridha

et al. 2023

Diagnostics

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“…Many trials have investigated new therapeutic options for HF, with most reporting equivocal results [3]. HF is a complex clinical syndrome in which the signs and symptoms are caused by any structural or functional impairment of ventricular filling or blood expulsion [4,5]. Depending on the organ or system being treated, care for HF patients may require the collaboration of many departments, including cardiologists, radiologists, and thoracic surgeons [5].…”

Section: Introductionmentioning

confidence: 99%

“…HF is a complex clinical syndrome in which the signs and symptoms are caused by any structural or functional impairment of ventricular filling or blood expulsion [4,5]. Depending on the organ or system being treated, care for HF patients may require the collaboration of many departments, including cardiologists, radiologists, and thoracic surgeons [5]. The increase in the incidence of HF among patients poses significant challenges for the patients as well as the hospital staff, including doctors and nurses [6].…”

Section: Introductionmentioning

confidence: 99%

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DU-ResNet to Predict Survival in Patient of Heart Failure

Zhang,

Tang

2023

Preprint

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Predicting survival in patients with heart disease clinically is a challenging task. Predicting the survival state is very important among patients with heart failure. In this paper, we propose a prediction model for the survival of heart failure patients based on deep learning combined with clinical data of heart failure patients. The proposed model is named DU-ResNet which is designed by combining ResUNet with ResNet50. Most clinical data of patients with heart failure are only numerical heart failure datasets. If one-dimensional clinical data can be converted into two-dimensional image data, the advantages of a deep convolutional neural network in extracting spatial features can be fully realized. For this reason, in this paper, the clinical data of all original patients with heart failure were normalized first, and then, each normalized clinical data point was placed in a certain area of the grid image. Therefore, according to the value of each clinical data point, a gray image with different brightness regions was constructed. After data enhancement was performed on the constructed image dataset of clinical data of heart failure patients to expand the number of samples, DU-ResNet is used to binary classify the expanded dataset, and ten-fold cross-validation and ablation experiments are performed on the dataset. Then, ten-fold cross-validation was used to verify the performance of the proposed DU-ResNet model. The results show that the proposed DU-ResNet model has the best result with the use of four features, with the accuracy was 96.47%, the Specificity was 97.22%, the Sensitivity was 95.71%, the Precision was 96.87%, the F1-score was 96.27% and the MCC was 92.97% after ten-fold cross-validation. In addition, the comprehensive performance of the proposed DU-ResNet model for predicting the survival of patients with heart failure is better than several typical deep learning methods.

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A review of evaluation approaches for explainable AI with applications in cardiology

Salih,

Galazzo,

Gkontra

et al. 2024

Artif Intell Rev

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Explainable artificial intelligence (XAI) elucidates the decision-making process of complex AI models and is important in building trust in model predictions. XAI explanations themselves require evaluation as to accuracy and reasonableness and in the context of use of the underlying AI model. This review details the evaluation of XAI in cardiac AI applications and has found that, of the studies examined, 37% evaluated XAI quality using literature results, 11% used clinicians as domain-experts, 11% used proxies or statistical analysis, with the remaining 43% not assessing the XAI used at all. We aim to inspire additional studies within healthcare, urging researchers not only to apply XAI methods but to systematically assess the resulting explanations, as a step towards developing trustworthy and safe models.

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Heart failure disease prediction and stratification with temporal electronic health records data using patient representation

Cited by 12 publications

References 52 publications

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel

Cardiac Failure Forecasting Based on Clinical Data Using a Lightweight Machine Learning Metamodel

DU-ResNet to Predict Survival in Patient of Heart Failure

A review of evaluation approaches for explainable AI with applications in cardiology

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