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Application and Potential of Artificial Intelligence in Heart Failure: Past, Present, and Future.
Yoon, Minjae; Park, Jin Joo; Hur, Taeho; Hua, Cam-Hao; Hussain, Musarrat; Lee, Sungyoung; Choi, Dong-Ju.
Afiliação
  • Yoon M; Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
  • Park JJ; Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
  • Hur T; Division of Cardiology, Department of Internal Medicine, Seoul National University Bundang Hospital, Seoul National University College of Medicine, Seongnam, Korea.
  • Hua CH; Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea.
  • Hussain M; Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea.
  • Lee S; Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea.
  • Choi DJ; Department of Computer Science and Engineering, Kyung Hee University, Yongin, Korea.
Int J Heart Fail ; 6(1): 11-19, 2024 Jan.
Article em En | MEDLINE | ID: mdl-38303917
ABSTRACT
The prevalence of heart failure (HF) is increasing, necessitating accurate diagnosis and tailored treatment. The accumulation of clinical information from patients with HF generates big data, which poses challenges for traditional analytical methods. To address this, big data approaches and artificial intelligence (AI) have been developed that can effectively predict future observations and outcomes, enabling precise diagnoses and personalized treatments of patients with HF. Machine learning (ML) is a subfield of AI that allows computers to analyze data, find patterns, and make predictions without explicit instructions. ML can be supervised, unsupervised, or semi-supervised. Deep learning is a branch of ML that uses artificial neural networks with multiple layers to find complex patterns. These AI technologies have shown significant potential in various aspects of HF research, including diagnosis, outcome prediction, classification of HF phenotypes, and optimization of treatment strategies. In addition, integrating multiple data sources, such as electrocardiography, electronic health records, and imaging data, can enhance the diagnostic accuracy of AI algorithms. Currently, wearable devices and remote monitoring aided by AI enable the earlier detection of HF and improved patient care. This review focuses on the rationale behind utilizing AI in HF and explores its various applications.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Int J Heart Fail Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Int J Heart Fail Ano de publicação: 2024 Tipo de documento: Article
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