Cargando…

Application of artificial intelligence ensemble learning model in early prediction of atrial fibrillation

BACKGROUND: Atrial fibrillation is a paroxysmal heart disease without any obvious symptoms for most people during the onset. The electrocardiogram (ECG) at the time other than the onset of this disease is not significantly different from that of normal people, which makes it difficult to detect and...

Descripción completa

Detalles Bibliográficos
Autores principales:	Wu, Cai, Hwang, Maxwell, Huang, Tian-Hsiang, Chen, Yen-Ming J., Chang, Yiu-Jen, Ho, Tsung-Han, Huang, Jian, Hwang, Kao-Shing, Ho, Wen-Hsien
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2021
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8576960/ https://www.ncbi.nlm.nih.gov/pubmed/34749631 http://dx.doi.org/10.1186/s12859-021-04000-2

Ejemplares similares

Prediction of vancomycin initial dosage using artificial intelligence models applying ensemble strategy
por: Ho, Wen-Hsien, et al.
Publicado: (2023)

Artificial Intelligence for the Detection and Treatment of Atrial Fibrillation
por: Harmon, David M, et al.
Publicado: (2023)

Artificial intelligence for the detection, prediction, and management of atrial fibrillation
por: Isaksen, Jonas L., et al.
Publicado: (2022)

The Use of Artificial Intelligence to Predict the Development of Atrial Fibrillation
por: Pipilas, Daniel, et al.
Publicado: (2023)

Artificial Intelligence in the Image-Guided Care of Atrial Fibrillation
por: Lyu, Yiheng, et al.
Publicado: (2023)

Artificial intelligence-guided automated measurement of pulmonary vein diameter in patients with atrial fibrillation
por: Kwon, O S, et al.
Publicado: (2023)

Genome-wide association study-based prediction of atrial fibrillation using artificial intelligence
por: Kwon, Oh-Seok, et al.
Publicado: (2022)

The Role of Artificial Intelligence in Coronary Artery Disease and Atrial Fibrillation
por: Hayıroğlu, Mert İlker, et al.
Publicado: (2023)

Explainable Paroxysmal Atrial Fibrillation Diagnosis Using Electrocardiogram with Artificial Intelligence
por: Lee, K H, et al.
Publicado: (2023)

Atrial Fibrillation Screening in Nonmetropolitan Areas Using a Telehealth Surveillance System With an Embedded Cloud-Computing Algorithm: Prospective Pilot Study
por: Chen, Ying-Hsien, et al.
Publicado: (2017)

Performance of an automated photoplethysmography-based artificial intelligence algorithm to detect atrial fibrillation
por: Mol, Daniel, et al.
Publicado: (2020)

Detection of atrial fibrillation-related electropathology by artificial intelligence: is the future already here?( )
por: de Groot, Natasja M S, et al.
Publicado: (2022)

Artificial intelligence–enabled mobile electrocardiograms for event prediction in paroxysmal atrial fibrillation
por: Raghunath, Ananditha, et al.
Publicado: (2023)

Assessment of the atrial fibrillation burden in Holter ECG recordings using artificial intelligence
por: Hennings, E, et al.
Publicado: (2023)

Factors Associated With Edoxaban Concentration Among Patients With Atrial Fibrillation
por: Lin, Shin-Yi, et al.
Publicado: (2021)

The era of artificial intelligence–based individualized telemedicine is coming
por: Jheng, Ying-Chun, et al.
Publicado: (2020)

What is next for screening for undiagnosed atrial fibrillation? Artificial intelligence may hold the key
por: Nadarajah, Ramesh, et al.
Publicado: (2021)

Can artificial intelligence prediction of successful atrial fibrillation catheter ablation therapy be interpretable?
por: Ogbomo-Harmitt, S, et al.
Publicado: (2022)

Artificial intelligence software standardizes electrogram‐based ablation outcome for persistent atrial fibrillation
por: Seitz, Julien, et al.
Publicado: (2022)

Keep your fingers on the PULsE: artificial intelligence to guide atrial fibrillation screening
por: Khurshid, Shaan, et al.
Publicado: (2022)

Wearable Devices Combined with Artificial Intelligence—A Future Technology for Atrial Fibrillation Detection?
por: Mäkynen, Marko, et al.
Publicado: (2022)

Identification of Atrial Fibrillation by Quantitative Analyses of Fingertip Photoplethysmogram
por: Tang, Sung-Chun, et al.
Publicado: (2017)

Artificial intelligence—electrocardiography to detect atrial fibrillation: trend of probability before and after the first episode
por: Christopoulos, Georgios, et al.
Publicado: (2022)

The Integration of Artificial Intelligence Into Patient Care: A Case of Atrial Fibrillation Caught by a Smartwatch
por: Bedi, Angad, et al.
Publicado: (2023)

Evaluation of Atrial Fibrillation Detection in Short-Term Photoplethysmography (PPG) Signals Using Artificial Intelligence
por: Talukdar, Debjyoti, et al.
Publicado: (2023)

Clinical applicability of an artificial intelligence prediction algorithm for early prediction of non-persistent atrial fibrillation
por: Kim, Yeji, et al.
Publicado: (2023)

Prediction of atrial fibrillation in patients with hypertension: A comprehensive comparison of office and ambulatory blood pressure measurements
por: Huang, Pin‐Hsiang, et al.
Publicado: (2022)

Prognostic role of left atrial strain and its combination index with transmitral E-wave velocity in patients with atrial fibrillation
por: Hsu, Po-Chao, et al.
Publicado: (2016)

Artificial intelligence classification model for macular degeneration images: a robust optimization framework for residual neural networks
por: Ho, Wen-Hsien, et al.
Publicado: (2021)

Anemia as an Independent Predictor of Adverse Cardiac Outcomes in Patients with Atrial Fibrillation
por: Lee, Wen-Hsien, et al.
Publicado: (2015)

Comparison of amplified P-wave analysis to artificial intelligence-derived analysis for diagnosis of atrial cardiomyopathy and outcome prediction following pvi for persistent atrial fibrillation
por: Jadidi, A, et al.
Publicado: (2023)

Heart Rate Significantly Influences the Relationship between Atrial Fibrillation and Arterial Stiffness
por: Chu, Chun-Yuan, et al.
Publicado: (2013)

External Validation of an Ensemble Model for Automated Mammography Interpretation by Artificial Intelligence
por: Hsu, William, et al.
Publicado: (2022)

Left Atrial Wall Stress and the Long-Term Outcome of Catheter Ablation of Atrial Fibrillation: An Artificial Intelligence-Based Prediction of Atrial Wall Stress
por: Lee, Jae-Hyuk, et al.
Publicado: (2021)

Identifying patients with atrial fibrillation during sinus rhythm on ECG: Significance of the labeling in the artificial intelligence algorithm
por: Suzuki, Shinya, et al.
Publicado: (2022)

Artificial Intelligence-Driven Algorithm for Drug Effect Prediction on Atrial Fibrillation: An in silico Population of Models Approach
por: Sanchez de la Nava, Ana Maria, et al.
Publicado: (2021)

Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study
por: Wang, Yu-Chiang, et al.
Publicado: (2022)

Prediction of new onset atrial fibrillation recurrence or persistence with artificial intelligence: first insights of the PRAFAI study
por: Garcia Olea, A, et al.
Publicado: (2022)

Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis
por: Manetas-Stavrakakis, Nikolaos, et al.
Publicado: (2023)

Hyperspectral Imaging Combined with Artificial Intelligence in the Early Detection of Esophageal Cancer
por: Tsai, Cho-Lun, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_rnn4rjm2igb4i3o7gmu6906mu8