AIMC Topic: Heart Failure

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A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: Application to cardiac resynchronisation therapy response prediction.

Medical image analysis
We present a framework for combining a cardiac motion atlas with non-motion data. The atlas represents cardiac cycle motion across a number of subjects in a common space based on rich motion descriptors capturing 3D displacement, velocity, strain and...

Mapping Phenotypic Information in Heterogeneous Textual Sources to a Domain-Specific Terminological Resource.

PloS one
Biomedical literature articles and narrative content from Electronic Health Records (EHRs) both constitute rich sources of disease-phenotype information. Phenotype concepts may be mentioned in text in multiple ways, using phrases with a variety of st...

A mixed-ensemble model for hospital readmission.

Artificial intelligence in medicine
OBJECTIVE: A hospital readmission is defined as an admission to a hospital within a certain time frame, typically thirty days, following a previous discharge, either to the same or to a different hospital. Because most patients are not readmitted, th...

Discrimination of systolic and diastolic dysfunctions using multi-layer perceptron in heart rate variability analysis.

Computers in biology and medicine
In this study, the heart rate variability (HRV) analysis is used to distinguish patients with systolic congestive heart failure (CHF) from patients with diastolic CHF. In the analysis performed, the best accuracy performances of short-term HRV measur...

Characterization of myocardial motion patterns by unsupervised multiple kernel learning.

Medical image analysis
We propose an independent objective method to characterize different patterns of functional responses to stress in the heart failure with preserved ejection fraction (HFPEF) syndrome by combining multiple temporally-aligned myocardial velocity traces...

Predictive value of circulating osteonectin in patients with ischemic symptomatic chronic heart failure.

Biomedical journal
BACKGROUND: Osteonectin (OSN) plays a pivotal role in cardiac remodeling, but predictive value for OSN in ischemic chronic heart failure (CHF) has not been defined. The aim of the study was to evaluate the prognostic value of OSN for cumulative survi...

Data mining framework for identification of myocardial infarction stages in ultrasound: A hybrid feature extraction paradigm (PART 2).

Computers in biology and medicine
Early expansion of infarcted zone after Acute Myocardial Infarction (AMI) has serious short and long-term consequences and contributes to increased mortality. Thus, identification of moderate and severe phases of AMI before leading to other catastrop...

Fuzzy Modeling to Predict Severely Depressed Left Ventricular Ejection Fraction following Admission to the Intensive Care Unit Using Clinical Physiology.

TheScientificWorldJournal
Left ventricular ejection fraction (LVEF) constitutes an important physiological parameter for the assessment of cardiac function, particularly in the settings of coronary artery disease and heart failure. This study explores the use of routinely and...

A Robust e-Epidemiology Tool in Phenotyping Heart Failure with Differentiation for Preserved and Reduced Ejection Fraction: the Electronic Medical Records and Genomics (eMERGE) Network.

Journal of cardiovascular translational research
Identifying populations of heart failure (HF) patients is paramount to research efforts aimed at developing strategies to effectively reduce the burden of this disease. The use of electronic medical record (EMR) data for this purpose is challenging g...