Previously submitted to: Journal of Medical Internet Research (no longer under consideration since Mar 21, 2024)
Date Submitted: Jul 7, 2023
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Application of Artificial Intelligence in Three-level Prevention of Heart Failure
ABSTRACT
Heart failure (HF) is a severe cardiovascular disease that poses significant challenges to global health. Recently, the application of artificial intelligence (AI) techniques in the field of heart failure has gained widespread attention and demonstrated immense potential. This study aims to provide a step-by-step guide for clinicians and researchers on the practical application of AI techniques in the domain of heart failure, with the objective of enhancing the transparency and applicability of these methods. The workflow for AI applications in heart failure starts with data selection and preprocessing. Here, we outlined the relevant preprocessing steps and machine-learning algorithm choices based on the type of data (structured, unstructured, or time series). Emphasizing the importance of model validation, we highlighted the need for an objective performance evaluation. Furthermore, by focusing on the integration of AI techniques in heart failure management, we actively addressed the concept of the ‘three levels of prevention’. The three levels of prevention involve interventions that aim to minimize complications and improve outcomes in patients with established heart failure. In this study, we presented five case studies that represent different applications of AI in the field of heart failure, illustrating how AI can assist in risk stratification, personalized treatment planning, and prognostic assessments. These case studies improve the readers' understanding of the underlying principles of AI and its potential role in implementing three levels of prevention strategies. However, it is crucial to acknowledge the limitations of the data sources, select appropriate machine learning algorithms, and conduct comprehensive evaluations and validations when utilizing AI technologies. By adhering to these methods, we can enhance the accuracy and generalizability of AI in clinical applications for heart failure. This has the potential to improve the quality of care for patients with HF and reduce the burden of disease.
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