Algorithmic model for risk assessment of heart failure patients

Boháčik, Ján; Benedikovic, Miroslav; Nedeljaková, Iveta

doi:10.1109/idaacs.2015.7340724

Cited by 9 publications

(9 citation statements)

References 14 publications

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“…Besides Shah et al 2015 [28], Fonarrow et al 2005 [61] estimated mortality risk in patients hospitalized with acute decompensated heart failure (ADHF), Bohacik et al 2013 [62] applied an alternating decision tree to predict risk of mortality within six months for heart failure patients and two years later [63] they present a model based on fuzzy logic, Panahiazar et al 2015 [64] exploited data from electronic health records of the Mayo Clinic and they performed HF survival analysis using machine learning techniques. One year later, the same research team [65] applied Contrast Pattern Aided Logistic Regression (CPXR(Log)) with the probabilistic loss function to the same dataset, developing and validating prognostic risk models to predict 1, 2, and 5 year survival in HF.…”

Section: Prediction Of Adverse Eventsmentioning

confidence: 99%

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Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Tripoliti

Papadopoulos

Karanasiou

et al. 2017

Computational and Structural Biotechnology Journal

179

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Heart failure is a serious condition with high prevalence (about 2% in the adult population in developed countries, and more than 8% in patients older than 75 years). About 3–5% of hospital admissions are linked with heart failure incidents. Heart failure is the first cause of admission by healthcare professionals in their clinical practice. The costs are very high, reaching up to 2% of the total health costs in the developed countries. Building an effective disease management strategy requires analysis of large amount of data, early detection of the disease, assessment of the severity and early prediction of adverse events. This will inhibit the progression of the disease, will improve the quality of life of the patients and will reduce the associated medical costs. Toward this direction machine learning techniques have been employed. The aim of this paper is to present the state-of-the-art of the machine learning methodologies applied for the assessment of heart failure. More specifically, models predicting the presence, estimating the subtype, assessing the severity of heart failure and predicting the presence of adverse events, such as destabilizations, re-hospitalizations, and mortality are presented. According to the authors' knowledge, it is the first time that such a comprehensive review, focusing on all aspects of the management of heart failure, is presented.

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Section: Prediction Of Adverse Eventsmentioning

confidence: 99%

“…Two years later, Bochacik et al 2015 [63] presented a model for the estimation of risk mortality within 6 months employing ambiguity and notions of fuzzy logic. The model stores knowledge for the patients in the form of fuzzy rules and classifies a patient to dead or alive using those rules.…”

Section: Prediction Of Adverse Eventsmentioning

confidence: 99%

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Tripoliti

Papadopoulos

Karanasiou

et al. 2017

Computational and Structural Biotechnology Journal

179

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“…Lack of adherence is common, resulting to destabilizations, re-hospitalizations and adverse events including death [1]. Towards this direction, several studies focusing on HF management have been presented in the literature, either based on machine learning approaches which address (separately or in combination) early diagnosis of HF [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18], HF subtype recognition [19][20][21], severity estimation [22][23][24][25][26][27][28], prediction of adverse events [24,[29][30][31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47]…”

Section: Introductionmentioning

confidence: 99%

HEARTEN KMS – A knowledge management system targeting the management of patients with heart failure

Tripoliti

Karanasiou

Kalatzis

et al. 2019

Journal of Biomedical Informatics

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The aim of this work is to present the HEARTEN Knowledge Management System, one of the core modules of the HEARTEN platform. The HEARTEN platform is an mHealth collaborative environment enabling the Heart Failure patients to self-manage the disease and remain adherent, while allowing the other ecosystem actors (healthcare professionals, caregivers, nutritionists, physical activity experts, psychologists) to monitor the patient's health progress and offer personalized, predictive and preventive disease management. The HEARTEN Knowledge Management System is a tool which provides multiple functionalities to the ecosystem actors for the assessment of the patient's condition, the estimation of the patient's adherence, the prediction of potential adverse events, the calculation of Heart Failure related scores, the extraction of statistics, the association of patient clinical and non-clinical data and the provision of alerts and suggestions. The innovation of this tool lays in the analysis of multiparametric personal data coming from different sources, including for the first time breath and saliva biomarkers, and the use of machine learning techniques. The HEARTEN Knowledge Management System consists of nine modules. The accuracy of the KMS modules ranges from 78-95% depending on the module/functionality.

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“…Bohacik et al [20] applied an alternating decision tree for the prediction of heart failure. In this type of decision tree, each part can be split multiple times rather than only leaf nodes.…”

Section: Related Workmentioning

confidence: 99%

Predicting the likelihood of heart failure with a multi level risk assessment using decision tree

Aljaaf

Al‐Jumeily

Hussain

et al. 2015

2015 Third International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE)

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Abstract-Heart failure comes in the top causes of death worldwide. The number of deaths from heart failure exceeds the number of deaths resulting from any other causes. Recent studies have focused on the use of machine learning techniques to develop predictive models that are able to predict the incidence of heart failure. The majority of these studies have used a binary output class, in which the prediction would be either the presence or absence of heart failure. In this study, a multi-level risk assessment of developing heart failure has been proposed, in which a five risk levels of heart failure can be predicted using C4.5 decision tree classifier. On the other hand, we are boosting the early prediction of heart failure through involving three main risk factors with the heart failure data set. Our predictive model shows an improvement on existing studies with 86.5% sensitivity, 95.5% specificity, and 86.53% accuracy.

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Algorithmic model for risk assessment of heart failure patients

Cited by 9 publications

References 14 publications

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

Heart Failure: Diagnosis, Severity Estimation and Prediction of Adverse Events Through Machine Learning Techniques

HEARTEN KMS – A knowledge management system targeting the management of patients with heart failure

Predicting the likelihood of heart failure with a multi level risk assessment using decision tree

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