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Precision Health Database


Precision Health Database|Search|PHGKB

Heart, Lung, Blood and Sleep Disorders

Last data update: May 27, 2023. (Total: 59953 Documents since 2012)
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Records 1 - 13 (of 13 Records)

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Deep learning-based measurement of echocardiographic data and its application in the diagnosis of sudden cardiac death.
Lu Zhang et al. Biotechnol Genet Eng Rev 2023 1-13
A novel artificial intelligence based algorithm to reduce wearable cardioverter-defibrillator alarms.
Jeffrey Arkles et al. Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing 2023
Improved prediction of sudden cardiac death in patients with heart failure through digital processing of electrocardiography.
Shiraishi Yasuyuki et al. Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology 2023
Prediction of the Presence of Ventricular Fibrillation From a Brugada Electrocardiogram Using Artificial Intelligence.
Nakamura Tomofumi et al. Circulation journal : official journal of the Japanese Circulation Society 2022
From evidence-based medicine to digital twin technology for predicting ventricular tachycardia in ischaemic cardiomyopathy.
de Lepper Anouk G W et al. Journal of the Royal Society, Interface 2022 19(194) 20220317
Machine learning techniques for arrhythmic risk stratification: a review of the literature.
Chung Cheuk To et al. International journal of arrhythmia 2022 23
Real life experience with the wearable cardioverter-defibrillator in an international multicenter Registry.
El-Battrawy Ibrahim et al. Scientific reports 2022 12(1) 3203
Explanatory Analysis of a Machine Learning Model to Identify Hypertrophic Cardiomyopathy Patients from EHR Using Diagnostic Codes.
Farahani Nasibeh Zanjirani et al. Proceedings. IEEE International Conference on Bioinformatics and Biomedicine 2021 20201932-1937
A machine learning-based risk stratification model for ventricular tachycardia and heart failure in hypertrophic cardiomyopathy.
Smole Tim et al. Computers in biology and medicine 2021 135104648
Biventricular imaging markers to predict outcomes in non-compaction cardiomyopathy: a machine learning study.
Rocon Camila et al. ESC heart failure 2020 Jun
Improving Clinical Translation of Machine Learning Approaches Through Clinician-Tailored Visual Displays of Black Box Algorithms: Development and Validation.
Wongvibulsin Shannon et al. JMIR medical informatics 2020 Jun 8(6) e15791
UNRAVEL: big data analytics research data platform to improve care of patients with cardiomyopathies using routine electronic health records and standardised biobanking.
Sammani A et al. Netherlands heart journal : monthly journal of the Netherlands Society of Cardiology and the Netherlands Heart Foundation 2019 May
Machine learning for prediction of sudden cardiac death in heart failure patients with low left ventricular ejection fraction: study protocol for a retroprospective multicentre registry in China.
Meng Fanqi et al. BMJ open 2019 May 9(5) e023724
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