1,720,959 research outputs found
Detecting Heart Failure Relations: A Preliminary Study Integrating HRV, LVEF, and GLS in Patients with Ischemic Heart Disease and Dilated Cardiomyopathy
No full textCardiovascular diseases, such as Ischemic Heart Disease (IHD) and Dilated Cardiomyopathy (DCM), collectively represent the leading cause of mortality worldwide. In both pathological conditions, patients displaying heart failure symptoms emphasize the critical need for early detection, facilitating timely and appropriate care, enhancing patient outcomes, and optimizing healthcare resources. Heart rate variability (HRV), Left ventricular ejection fraction (LVEF) and Global longitudinal strain (GLS) are prominent parameters that could allow the identification of heart failure event. Therefore, the aim of our study was to develop an interpretable model that identify the relation between the occurrence of heart failure and HRV features, as well as LVEF, GLS, sex and age in patients with IHD and DCM. The study encompassed two groups: 126 patients with heart failure (HF group) and 126 patients without it (noHF group). GLS, LVEF, and linear and non-linear HRV features were extracted for each subject. Then, the interpretable model was produced by a logistic regression algorithm considering a set of features chosen with the univariate logistic regression method. The univariate logistic regression results indicate a significative correlation between the occurrence of heart failure events and the following parameters: LVEF, age, expBeta, HFn, and LF/HF. The obtained classification accuracy of produced model was 73% and the area under the ROC curve was 0.77. These preliminary findings showed that the identified parameters may be useful for stratification of IHD and DCM subjects with a risk of a heart failure event
Discriminatory power of Global Longitudinal Strain and Left Ventricular Ejection Fraction for Identification of Dilated Cardiomyopathy
No full textDilated cardiomyopathy (DCM) is one of the leading causes of heart failure. Left ventricular ejection fraction (LVEF) is one of the most used features for assessing heart health and predicting outcomes in DCM patients. However, it does have numerous pitfalls. Recent studies suggest that global longitudinal strain (GLS) and heart rate variability (HRV) can be used to predict DCM. Furthermore, numerous studies have demonstrated how essential it is to deploy interpretable machine learning models to aid clinicians in the cases when the diagnosis is challenging. Therefore, we aimed to investigate discriminatory power of GLS and LVEF as a feature of logistic regression models for identification of DCM. The study encompassed 138 DCM and 138 healthy controls (HC). The models were produced by logistic regression algorithms considering the set of selected HRV features and GLS (LogRegGLS) or LVEF (LogRegLVEF). The results showed that the accuracy of produced LogRegGLS model was 86%, higher than the one observed in case of LogRegLVEF model (83%). The produced nomograms also supported the hypothesis of the relevance of the GLS and LVEF features, indicating that these two measurements are the most useful in identification of DCM. In conclusion, our findings highlight the value and efficacy of interpretable machine learning models and suggest that GLS may have more discriminatory power in differentiating between DCM and healthy participants than LVEF
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Interpretable Model to Support Differential Diagnosis Between Ischemic Heart Disease, Dilated Cardiomyopathy and Healthy Subjects
No full textThe differential diagnosis between Ischemic Heart Disease (IHD) and Dilated Cardiomyopathy (DCM) can often be challenging, because only invasive, and not largely available exams can provide a definite diagnosis. The echocardiographic left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) as well as ECGheart rate variability (HRV) analysis are shown to be helpful tools for diagnosing several cardiac diseases. There is also a growing interest in application of interpretable machine learning techniques to guide the diagnosis.
We aimed to produce an interpretable model applied for differential diagnosis between DCM, IHD and healthy subjects (HC) based on LVEF, GLS and HRV features. The study encompassed three groups: 130 DCM, 164 IHD, and 152 HC subjects. The novel GLS, LVEF, and linear and non-linear HRV features were extracted for each subject. Then, the interpretable models were produced by a logistic regression algorithm considering a set of features chosen with the ReliefF method. The results showed that the most informative features for classification between IHD, DCM e HC were: GLS, LVEF, age, FD, SD1/SD2 and sex, listed in order of importance. The obtained classification accuracy was 70% and the area under the ROC curvewas 83.4%. The study demonstrates that a logistic regression model and its nomograms allow detailed clinical interpretation of the model and may be a powerful tools support differential diagnosis between IHD, DCM and HC
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Development of an Interpretable Model for Improving Differential Diagnosis in Subjects with a Left Ventricular Ejection Fraction Ranging from 40 to 55%
No full textDistinguishing between Ischemic Heart Disease (IHD) and Non- Ischemic Dilated Cardiomyopathy (DCM) can often be difficult without invasive coronary angiography, especially in patients with Left Ventricular Ejection Frac- tion (LVEF) ranging from 40 to 50%. Moreover, although it is rare, some healthy subjects (HC) can have an LVEF of about 50% and must be differentiated from IHD and DCM patients. Global longitudinal strain (GLS) and heart rate variabil- ity (HRV) analysis are efficient diagnostic tools for different cardiac conditions. The use of interpretable machine-learning methods to direct the diagnosis is also gaining popularity. Therefore, this study aimed to produce a multinomial logistic regression model based on HRV, GLS and clinical features for differential diag- nosis between DCM, IHD, and HC in cases with LVEF in a range of 40–55%. The study encompassed 73 DCM, 71 IHD, and 70 HC. The model was produced by logistic regression algorithms considering the set of selected features chosen with the information gain ratio method. The results showed that the most informative features for classification between HC, DCM, and IHD were GLS, meanRR, sex, age, and LFn. The model has a moderately high classification accuracy of 73%. Finally, the developed model with its nomograms enables probabilistic interpre- tation of classification output between HC, DCM, and IHD, and may support the differential diagnosis in this population
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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