Author(s)
Jiandong Zhou
Published 16 Projects
Infectious Diseases Heart Failure Cardiovascular Medicine Risk Stratification Frailty Score
Ka Hei Gabriel Wong
Published 1 Project
Key Words Cardiovascular Medicine All Cause Mortality Factorization Machine Pulmonary Hypertension
Sharen Lee
Published 16 Projects
Infectious Diseases Heart Failure Cardiovascular Medicine Risk Stratification Frailty Score
Tong Liu
Published 16 Projects
Infectious Diseases Heart Failure Cardiovascular Medicine Risk Stratification Frailty Score
Keith SK Leung
Published 3 Projects
Key Words Cardiovascular Medicine All Cause Mortality Factorization Machine Pulmonary Hypertension
Kamalan Jeevaratnam
Published 4 Projects
Key Words Cardiovascular Medicine All Cause Mortality Factorization Machine Pulmonary Hypertension
Bernard MY Cheung
Published 2 Projects
Infectious Diseases Key Words Cardiovascular Medicine All Cause Mortality Factorization Machine
Ian CK Wong
Published 2 Projects
Infectious Diseases Key Words Cardiovascular Medicine All Cause Mortality Factorization Machine
Bernard Man Yung Cheung
Published 23 Projects
Infectious Diseases Heart Failure Cardiovascular Medicine Frailty Score COVID-19
Gary Tse
Professor and Principal Investigator at Tianjin Medical University | TIJMU · Tianjin Institute of Cardiology
Published 11 Projects
COVID-19 Arrhythmia Hypokalaemia Ventricular Arrhythmogenesis Afterdepolarization
Content
Video Abstract (AI generated) (02:01) Paper PreprintBackground: Pulmonary hypertension, a progressive lung disorder with symptoms such as breathlessness and loss of exercise capacity, is highly debilitating and has a negative impact on the quality of life. In this study, we examined whether a multi-parametric approach using machine learning can improve mortality prediction. Methods: A population-based territory-wide cohort of pulmonary hypertension patients from January 1, 2000 to December 31, 2017 were retrospectively analyzed. Significant predictors of all-cause mortality were identified. Easy-to-use frailty indexes predicting primary and secondary pulmonary hypertension were derived and stratification performances of the derived scores were compared. A factorization machine model was used for the development of an accurate predictive risk model and the results were compared to multivariate logistic regression, support vector machine, random forests, and multilayer perceptron. Results: The cohorts consist of 2562 patients with either primary (n=1009) or secondary (n=1553) pulmonary hypertension. Multivariate Cox regression showed that age, prior cardiovascular, respiratory and kidney diseases, hypertension, number of emergency readmissions within 28 days of discharge were all predictors of all-cause mortality. Easy-to-use frailty scores were developed from Cox regression. A factorization machine model demonstrates superior risk prediction improvements for both primary (precision: 0.90, recall: 0.89, F1-score: 0.91, AUC: 0.91) and secondary pulmonary hypertension (precision: 0.87, recall: 0.86, F1-score: 0.89, AUC: 0.88) patients. Conclusion: We derived easy-to-use frailty scores predicting mortality in primary and secondary pulmonary hypertension. A machine learning model incorporating multi-modality clinical data significantly improves risk stratification performance.
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Gary Tse. (2021, Nov 8).Development of Predictive Risk Models for All-cause Mortality in Pulmonary Hypertension using Machine Learning[Video]. Scitok. https://scitok.com/project/p/34b3e941
Zhou Jiandong. "Development of Predictive Risk Models for All-cause Mortality in Pulmonary Hypertension using Machine Learning" Scitok, uploaded by Tse Gary, 8 Nov, 2021, https://scitok.com/project/p34b3e941
Gary Tse. "Development of Predictive Risk Models for All-cause Mortality in Pulmonary Hypertension using Machine Learning" Scitok. (Nov 8, 2021). https://scitok.com/project/p/34b3e941
Gary Tse (Nov 8, 2021). Development of Predictive Risk Models for All-cause Mortality in Pulmonary Hypertension using Machine Learning Scitok. https://scitok.com/project/p/34b3e941
Gary Tse. Development of Predictive Risk Models for All-cause Mortality in Pulmonary Hypertension using Machine Learning[video]. 2021 Nov 8. https://scitok.com/project/p/34b3e941
@online{al2006link, title={ Development of Predictive Risk Models for All-cause Mortality in Pulmonary Hypertension using Machine Learning }, author={ Tse, Gary }, organization={Scitok}, month={ Nov }, day={ 8 }, year={ 2021 }, url = {https://scitok.com/project/p/34b3e941}, }