CarDS-Plus ECG Platform: Development and Feasibility Evaluation of a Multiplatform Artificial Intelligence Toolkit for Portable and Wearable Device Electrocardiograms

Autor: Shankar, Sumukh Vasisht, Oikonomou, Evangelos K, Khera, Rohan

In the rapidly evolving landscape of modern healthcare, the integration of wearable & portable technology provides a unique opportunity for personalized health monitoring in the community. Devices like the Apple Watch, FitBit, and AliveCor KardiaMobi

Externí odkaz: http://arxiv.org/abs/2310.07000

An Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images: PRESENT SHD.

Autor: Dhingra LS; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA., Aminorroaya A; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA., Sangha V; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.; Department of Engineering Science, University of Oxford, Oxford, UK., Pedroso AF; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA., Shankar SV; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA., Coppi A; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA., Foppa M; School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, Brazil., Brant LC; Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.; Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil., Barreto SM; Department of Preventive Medicine, School of Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil., Ribeiro ALP; Department of Internal Medicine, Faculdade de Medicina, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil.; Telehealth Center and Cardiology Service, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil., Krumholz HM; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA.; Department of Health Policy and Management, Yale School of Public Health, New Haven, CT, USA., Oikonomou EK; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA., Khera R; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.; Center for Outcomes Research and Evaluation (CORE), Yale New Haven Hospital, New Haven, CT, USA.; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Publikováno v: MedRxiv : the preprint server for health sciences [medRxiv] 2024 Nov 07. Date of Electronic Publication: 2024 Nov 07.

Retrieval-Augmented Generation for Extracting CHA 2 DS 2 -VASc Risk Factors from Unstructured Clinical Notes in Patients with Atrial Fibrillation.

Autor: Adejumo P; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT., Thangaraj P; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT., Shankar SV; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT., Dhingra LS; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT., Aminorroaya A; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT., Khera R; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT.; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT.; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT.

Publikováno v: MedRxiv : the preprint server for health sciences [medRxiv] 2024 Sep 22. Date of Electronic Publication: 2024 Sep 22.

Tracking the pre-clinical progression of transthyretin amyloid cardiomyopathy using artificial intelligence-enabled electrocardiography and echocardiography.

Autor: Oikonomou EK; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA., Sangha V; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.; Department of Engineering Science, University of Oxford, Oxford, UK., Shankar SV; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA., Coppi A; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA., Krumholz HM; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA., Nasir K; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.; Department of Engineering Science, University of Oxford, Oxford, UK.; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.; Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX.; Center for Cardiovascular Computational & Precision Health, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, USA.; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA., Miller EJ; Division of Cardiovascular Prevention and Wellness, Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX.; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA., Gallegos-Kattan C; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA., Al-Kindi S; Center for Cardiovascular Computational & Precision Health, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX, USA., Khera R; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.; Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.

Publikováno v: MedRxiv : the preprint server for health sciences [medRxiv] 2024 Aug 26. Date of Electronic Publication: 2024 Aug 26.

Automated Diagnostic Reports from Images of Electrocardiograms at the Point-of-Care.

Autor: Khunte A; Department of Computer Science, Yale University, New Haven, CT., Sangha V; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.; Department of Engineering Science, Oxford University, Oxford, UK., Oikonomou EK; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT., Dhingra LS; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT., Aminorroaya A; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT., Coppi A; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.; Department of Computer Science & Engineering, Texas A&M University, College Station, TX., Shankar SV; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT., Mortazavi BJ; Department of Computer Science & Engineering, Texas A&M University, College Station, TX.; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT., Bhatt DL; Mount Sinai Fuster Heart Hospital, Icahn School of Medicine at Mount Sinai, New York, NY., Krumholz HM; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.; Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT.; Department of Health Policy and Management, Yale School of Public Health, New Haven, CT., Nadkarni GN; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; The Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Vaid A; The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.; The Division of Data Driven and Digital Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA., Khera R; Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT.; Department of Computer Science & Engineering, Texas A&M University, College Station, TX.; Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT.

Publikováno v: MedRxiv : the preprint server for health sciences [medRxiv] 2024 Feb 18. Date of Electronic Publication: 2024 Feb 18.