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Development and validation of a machine learned algorithm to IDENTIFY functionally significant coronary artery disease.
Frontiers in cardiovascular medicine , 2022 | Pubmed ID: 36119746
Identifying novel phenotypes of elevated left ventricular end diastolic pressure using hierarchical clustering of features derived from electromechanical waveform data.
Frontiers in cardiovascular medicine , 2022 | Pubmed ID: 36211581
Multicenter validation of a machine learning phase space electro-mechanical pulse wave analysis to predict elevated left ventricular end diastolic pressure at the point-of-care.
PloS one , 2022 | Pubmed ID: 36378672
Development of a Non-Invasive Machine-Learned Point-of-Care Rule-Out Test for Coronary Artery Disease.
Diagnostics (Basel, Switzerland) Mar, 2024 | Pubmed ID: 38611631
Pulmonary Hypertension Detection Non-Invasively at Point-of-Care Using a Machine-Learned Algorithm.
Diagnostics (Basel, Switzerland) Apr, 2024 | Pubmed ID: 38732312
Clinical Validation of a Machine-Learned, Point-of-Care System to IDENTIFY Functionally Significant Coronary Artery Disease.
Diagnostics (Basel, Switzerland) May, 2024 | Pubmed ID: 38786284
Analytics for Life, Toronto
Timothy Burton1,
Navid Nemati1,
Farhad Fathieh1,
Horace R. Gillins2,
Charles R. Bridges2,
Shyam Ramchandani1,
Ian Shadforth2
1, Analytics for Life, Toronto,
2, Analytics for Life, Bethesda
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