AI and Heart Failure
A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Medical Research".
Deadline for manuscript submissions: 20 May 2024 | Viewed by 401
Special Issue Editors
Interests: heart failure; acute heart failure; chronic heart failure; LVAD; heart transplantation; amyloidosis; devices; pulmonary hypertension
Special Issues, Collections and Topics in MDPI journals
Interests: valvular disease; coronary artery disease; transcatheter therapies; cardiovascular imaging; artificial intelligence
2. Cardiology Department, Alexandra Hospital, Athens, Greece
Interests: cardiovascular medicine; heart failure; heart transplant
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the progress in medical technology and knowledge, large amounts of versatile medical data are now produced at high rates, constituting what is broadly called Big Data. Artificial intelligence and its algorithmic subfield termed machine learning (ML) offer patients, physicians and public health specialists advantageous methods to utilize these large amounts of medical data. Indeed, the number of indexed publications in PubMed that relate to ML and deep learning, types of machine learning algorithms that are called convolutional neural networks, has been rising exponentially since 2005. However, the application of ML algorithms is not without limitations. The collection and preprocessing of large data, overtraining and explicability of ML algorithms, risk of misinterpretation and misinformation, as well as the need for multidisciplinary teams are some of the major challenges of ML applications in the medical field.
In this context, ML algorithms are being used more and more frequently for multiple purposes within the cardiology subfield of heart failure (HF). ML algorithms have the potential to discover new knowledge, define clinical phenotypes and assist in the generation of research hypotheses. For example, they can generate research hypotheses for HF with a preserved ejection fraction, predict outcomes in different HF populations, assist physicians in the diagnosis of HF and associated clinical decision-making, and assist patients or people in avoiding HF hospitalizations by utilizing mobile devices. At the same time, great effort and expertise are warranted to address the challenges of ML applications in HF. The goal of this Special Issue is to update readers on the expanding applications and limitations of ML algorithms in HF by highlighting the key aspects and research in the field.
Dr. Andrew Xanthopoulos
Dr. Polydoros Ν. Kampaktsis
Dr. Alexandros Briasoulis
Guest Editors
Manuscript Submission Information
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Keywords
- artificial intelligence
- heart failure
- heart disease
