scholarly journals Correction to: Predicting survival in heart failure: a risk score based on machine-learning and change point algorithm

2021 ◽  
Author(s):  
Wonse Kim ◽  
Jin Joo Park ◽  
Hae-Young Lee ◽  
Kye Hun Kim ◽  
Byung-Su Yoo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Risk Score ◽  
Change Point ◽  
Learning And Change

Predicting survival in heart failure: a risk score based on machine-learning and change point algorithm

2021 ◽  
Author(s):  
Wonse Kim ◽  
Jin Joo Park ◽  
Hae-Young Lee ◽  
Kye Hun Kim ◽  
Byung-Su Yoo ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Risk Score ◽  
Change Point ◽  
Learning And Change

scholarly journals Machine Learning to Predict the Risk of Incident Heart Failure Hospitalization Among Patients With Diabetes: The WATCH-DM Risk Score

Diabetes Care ◽  
2019 ◽  
Vol 42 (12) ◽  
pp. 2298-2306 ◽  
Author(s):  
Matthew W. Segar ◽  
Muthiah Vaduganathan ◽  
Kershaw V. Patel ◽  
Darren K. McGuire ◽  
Javed Butler ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Risk Score ◽  
Heart Failure Hospitalization ◽  
Patients With Diabetes ◽  
Incident Heart Failure

scholarly journals External assessment of the EUROMACS right-sided heart failure risk score

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hirak Shah ◽  
Thomas Murray ◽  
Jessica Schultz ◽  
Ranjit John ◽  
Cindy M. Martin ◽  
...  
Keyword(s):  
Heart Failure ◽  
Right Ventricular ◽  
Risk Score ◽  
Pressure Ratio ◽  
Area Under The Curve ◽  
Left Ventricular ◽  
Right Atrial ◽  
Risk Scores ◽  
External Assessment ◽  
Failure Risk

AbstractThe EUROMACS Right-Sided Heart Failure Risk Score was developed to predict right ventricular failure (RVF) after left ventricular assist device (LVAD) placement. The predictive ability of the EUROMACS score has not been tested in other cohorts. We performed a single center analysis of a continuous-flow (CF) LVAD cohort (n = 254) where we calculated EUROMACS risk scores and assessed for right ventricular heart failure after LVAD implantation. Thirty-nine percent of patients (100/254) had post-operative RVF, of which 9% (23/254) required prolonged inotropic support and 5% (12/254) required RVAD placement. For patients who developed RVF after LVAD implantation, there was a 45% increase in the hazards of death on LVAD support (HR 1.45, 95% CI 0.98–2.2, p = 0.066). Two variables in the EUROMACS score (Hemoglobin and Right Atrial Pressure to Pulmonary Capillary Wedge Pressure ratio) were not predictive of RVF in our cohort. Overall, the EUROMACS score had poor external discrimination in our cohort with area under the curve of 58% (95% CI 52–66%). Further work is necessary to enhance our ability to predict RVF after LVAD implantation.

Phenomapping Heart Failure with Preserved Ejection Fraction Using Machine Learning Cluster Analysis

2021 ◽  
Vol 17 (3) ◽  
pp. 499-518
Author(s):  
Elena Galli ◽  
Corentin Bourg ◽  
Wojciech Kosmala ◽  
Emmanuel Oger ◽  
Erwan Donal
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Cluster Analysis ◽  
Ejection Fraction ◽  
Preserved Ejection Fraction

Prediction of preterm birth based on machine learning using bacterial risk score in cervicovaginal fluid

2021 ◽  
Author(s):  
Sunwha Park ◽  
Daejoong Oh ◽  
Hanna Heo ◽  
Gain Lee ◽  
Soo Min Kim ◽  
...  
Keyword(s):  
Machine Learning ◽  
Preterm Birth ◽  
Risk Score ◽  
Cervicovaginal Fluid

Correlation Analysis for Determining Effective Data in Machine Learning: Detection of Heart Failure

2021 ◽  
Vol 2 (3) ◽  
Author(s):  
Tsehay Admassu Assegie ◽  
S. J. Sushma ◽  
B. G. Bhavya ◽  
S. Padmashree
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Correlation Analysis
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