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1

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

Liang Ye, Guo Chonghui

Biocybernetics and Biomedical Engineering

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2023

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Vol. 43, no. 1

124--141

EN

Accurate early prediction of heart failure and identification of heart failure sub-phenotypes can enable in-time interventions and treatments, assist with policy decisions, and lead to a better understanding of disease pathophysiology in groups of patients. However, decision making more challenging for clinicians since the available data is complex, heterogeneous, temporal, and different in granularity. Even with much data, it is difficult for a cardiologist to pre-judge a patient’s heart condition at the next visit by relying on data from only one visit. Moreover, complicated and overloaded information bewilders clinicians, bringing obstacles to the stratification of patients and the mining of disease typical patterns in subgroups. To overcome these issues, this study proposes a novel Patient Representation model based on a temporal Bidirectional neural network with an Attention mechanism deep learning model called tBNA-PR. tBNA-PR effectively models heterogeneous and temporal Electronic Health Records (tEHRs) data from past and future directions to obtain informative patient representation to realize accurate heart failure prediction and reasonable patient stratification. Additionally, this study extracts typical diagnosis and prescriptions for disease patterns exploration and identifies significant features of sub-phenotypes for subgroup explanation in the context of complex clinical settings to provide better quality healthcare services and clinical decision support. This study leverages a real-world dataset MIMIC-III database. We carried out experiments on the prediction of heart failure to investigate tBNA-PR, which obtains prediction accuracy of 0.78, F1-Score of 0.7671, and AUC of 0.7198, showing a certain superiority compared with several state-of-the-art benchmarks. Moreover, we identified three distinct sub-phenotypes in all heart failure patients in the dataset with the clustering method and subgroup analysis. Sub-phenotype I has characteristics of more long-term anticoagulants. This sub-group has more patients who have the thrombotic disease. Sub-phenotype II has features of more patients having kidney disease, pneumonia, urinary tract infection, and coronary heart disease surgery history. Subphenotype III has characteristics of more patients having acidosis, depressive disorder, esophageal reflux, obstructive sleep apnea, and acquired hypothyroidism. Statistical tests show that the features, including age, creatinine, hemoglobin, urea nitrogen, and blood potassium, are significantly different among the three sub-phenotypes and have particular high importance. The resultant findings from this work have practical implications for clinical decision support.

2

Predicting the response to cardiac resynchronization therapy (CRT) using the deep learning approach

Nejadeh Mohammad, Bayat Peyman, Kheirkhah Jalal, Moladoust Hassan

Biocybernetics and Biomedical Engineering

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2021

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Vol. 41, no. 2

758--778

EN

Cardiac Resynchronization Therapy Defibrillator (CRT-D) is a method to improve heart rate variability and arrhythmia-related symptoms in heart failure patients. According to clinical reports, CRT is not entirely safe and risk-free like other surgery. It can reduce heart failure risks, shorten hospital stays, and enhance the patients’ quality of life. The present study aims to perform the proper selection of patients before surgery to avoid potential costs. This article focuses on the data collection of heart failure patients’ activities, the process of features effective extraction, and identifying an optimal pattern using a Deep Learning (DL) algorithm. Also, the main tasks of the proposed methods include the use of qualitative indicators for initial feature extraction, oversampling from minority class, Density-Based Spatial Clustering of Applications with Noise (DBSCAN) hierarchical clustering, selecting features from Low error clusters, selecting samples from high error clusters, and classification using customized DL configuration. The research data collection consisted of 209 patients with 60 demographic, clinical, laboratory, ECG, and echo features. In addition, features were analyzed based on their significance in predicting CRT response status. The DL algorithm, which used dense layers and convolution for its architecture, was employed to heart failure patients optimally identify the treatment status. The proposed method predicted the response to cardiac resynchronization therapy with an error rate of 91.85% and an Area Under Curve (AUC) of 0.957 and a sensitivity of 94.22%.

3

Percutaneous double lumen cannula for right ventricle assist device system: A computational fluid dynamics study

Condemi F., Wang D., Fragomeni G., Yang F., Zhao G., Jones C., Ballard-Croft C., Zwischenberger J. B.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 3

482--490

EN

Objectives: Our goal is to develop a double lumen cannula (DLC) for a percutaneous right ventricular assist device (pRVAD) in order to eliminate two open chest surgeries for RVAD installation and removal. The objective of this study was to evaluate the performance, flow pattern, blood hemolysis, and thrombosis potential of the pRVAD DLC. Methods: Computational fluid dynamics (CFD), using the finite volume method, was performed on the pRVAD DLC. For Reynolds numbers <4000, the laminar model was used to describe the blood flow behavior, while shear-stress transport k-ω model was used for Reynolds numbers >4000. Bench testing with a 27 Fr prototype was performed to validate the CFD calculations. Results: There was <1.3% difference between the CFD and experimental pressure drop results. The Lagrangian approach revealed a low index of hemolysis (0.012% in drainage lumen and 0.0073% in infusion lumen) at 5 l/min flow rate. Blood stagnancy and recirculation regions were found in the CFD analysis, indicating a potential risk for thrombosis. Conclusions: The pRVAD DLC can handle up to 5 l/min flow with limited potential hemolysis. Further modification of the pRVAD DLC is needed to address blood stagnancy and recirculation.

4

A portable system for autoregulation and wireless control of sensorized left ventricular assist devices

Fontana R., Tortora G., Silvestri M., Vatteroni M., Dario P., Trivella M. G.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 2

366--374

EN

End stage heart failure patients could benefit from left ventricular assist device (LVAD) implantation as bridge to heart transplantation or as destination therapy. However, LVAD suffers from several limitations, including the presence of a battery as power supply, the need for cabled connection from inside to outside the patient, and the lack of autonomous adaptation to the patient metabolic demand during daily activity. The authors, in this wide scenario, aim to contribute to advancement of the LVAD therapy by developing the hardware and the firmware of a portable autoregulation unit (ARU), able to fulfill the needs of sensorized VAD in terms of physic/physiological data storing, continuous monitoring, wireless control from the external environment and automatic adaptation to patient activities trough the implementation of autoregulation algorithms. Moreover, in order to answer the rules and safety requirements for implantable biomedical devices, a user control interface (UCI), was developed and associated to the ARU for an external manual safe control. The ARU and UCI functionalities and autoregulation algorithms have been successfully tested on bench and on animal, with a response time of 1 s for activating autoregulation algorithms. Animal experiments showed as the presence of the ARU do not affect the animal cardiovascular system, giving a proof of concept of its applicability in vivo.

5

Conventional and neuro-fuzzy framework for diagnosis and therapy of cardiovascular disease

Obot O. U., Uzoka F. M., Akinyokun O. C., Andy J. J.

Bio-Algorithms and Med-Systems

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2013

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Vol. 9, no. 3

115--133

EN

In this article, we present the conventional method and neuro-fuzzy model for the diagnosis and therapy of heart disease. The neuro-fuzzy system provides a basis for creating a decision support system that has a learning ability and the capacity to deal with vagueness and unstructuredness in disease management. The decision support engine carries out the cognitive and emotional filtering of the objective and subjective feelings of the medical practitioner. These filters further refine the diagnosis and therapy processes by taking care of the contextual elements.

6

Prediction of mortality rates in heart failure patients with data mining methods

Bohacik J., Kambhampati C., Davis D. N., Cleland J. G. F.

Annales Universitatis Mariae Curie-Skłodowska. Sectio AI, Informatica

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2013

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Vol. 13, no. 1

7--16

EN

Heart failure is one of the severe diseases which menace the human health and affect millions of people. Half of all patients diagnosed with heart failure die within four years. For the purpose of avoiding life-threatening situations and minimizing the costs, it is important to predict mortality rates of heart failure patients. As part of a HEIF-5 project, a data mining study was conducted aiming specifically at extracting new knowledge from a group of patients suffering from heart failure and using it for prediction of mortality rates. The methodology of knowledge discovery in databases is analyzed within the framework of home telemonitoring. Several data mining methods such as a Bayesian network method, a decision tree method, a neural network method and a nearest neighbour method are employed. The accuracy for the data mining methods from the point of view of avoiding life-threatening situations and minimizing the costs is discussed. It seems that the decision tree method achieves the best accuracy results and is also interpretable for the clinicians.

7

Heart failure ontology

Jovic A., Gamberger D., Krstacic G.

Bio-Algorithms and Med-Systems

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2011

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Vol. 7, no. 2

101--110

EN

Ontology represents explicit specification of knowledge in a specific domain of interest in the form of concepts and relations among them. This paper presents a medical ontology describing the domain of heart failure (HF). Construction of ontology for a domain like HF is recognized as an important step in systematization of existing medical knowledge. The main virtue of ontology is that the represented knowledge is both computer and human-readable. The current development of the HF ontology is one of the main results of the EU Heartfaid project. The ontology has been implemented using Ontology Web Language and Protégé editing tool. It consists of roughly 200 classes, 100 relations and 2000 instances. The ontology is a precise, voluminous, portable, and upgradable representation of the HF domain. It is also a useful framework for building knowledge based systems in the HF domain, as well as for unambiguous communication between professionals. In the process of developing the HF ontology there have been significant technical and medical dilemmas. The current result should not be treated as the ultimate solution but as a starting point that will stimulate further research and development activities that can be very relevant for both intelligent computer systems and precise communication of medical knowledge.

8

A newly-designed myocardial assist device using a sophisticated shape memory alloy fibre

Shiraishi Y., Yambe T., Sekine K., Ogawa D., Nagatoshi J., Itoh S., Park Y., Uematsu M., Sakata R., Wada Y., Saijo Y., Higa M., Hori Y., Liu H., Wang Q., Konno S., Kuwayama T., Olegario P., Tanaka A., Masumoto N., Ibuki R., Maruyama S., Okamoto E., Fujimoto T., Yoshizawa M., Umezu M., Imachi K., Nitta S.-I., Sasada H., Tabayashi K., Homma D.

Biocybernetics and Biomedical Engineering

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2007

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Vol. 27, no. 1-2

147-154

EN

Recently, the ventricular assist devices are widely applied for a surgical treatment of the final stage of severe heart failure as the bridge to heart transplantation or the destination therapy. However, it was anticipated that the artificial components in the ventricular assist devices might cause the problems concerning thrombosis and infection. As heart failure involves the decrease in myocardial contractile function, the mechanical assistance by using an artificial myocardium might be effective. In this study, the authors developed a mechano-electric artificial myocardial assist system (artificial myocardium), which is capable of supporting natural contractile function from the outside of the ventricle.