Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications

Gautam, Nitesh; Ghanta, Sai Nikhila; Mueller, Jenna L.; Mansour, Munthir; Chen, Zhongning; Puente, Clara; Ha, Yu Mi; Tarun, Tushar; Dhar, Gaurav; Sivakumar, Kalai; Zhang, Yiye; Halimeh, Ahmed Abu; Nakarmi, Ukash; Al‐Kindi, Sadeer G.; DeMazumder, Deeptankar; Al’Aref, Subhi J.

doi:10.3390/diagnostics12122964

Cited by 23 publications

(8 citation statements)

References 75 publications

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“…Moreover, the authors of the review in ref. [17] showed that artificial intelligence and ML (AI/ML) in heart failure diagnostics and therapy can improve workflow and outcomes, especially for time series data collected via remote monitoring. However, limitations like data integration, privacy, and challenges in healthcare apply to AI/ML in wearable technology.…”

Section: Background and Related Workmentioning

confidence: 99%

Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Christopoulou

2024

BioMedInformatics

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Background: Over the past few years, clinical studies have utilized machine learning in telehealth and smart care for disease management, self-management, and managing health issues like pulmonary diseases, heart failure, diabetes screening, and intraoperative risks. However, a systematic review of machine learning’s use in evidence-based telehealth and smart care is lacking, as evidence-based practice aims to eliminate biases and subjective opinions. Methods: The author conducted a mixed methods review to explore machine learning applications in evidence-based telehealth and smart care. A systematic search of the literature was performed during 16 June 2023–27 June 2023 in Google Scholar, PubMed, and the clinical registry platform ClinicalTrials.gov. The author included articles in the review if they were implemented by evidence-based health informatics and concerned with telehealth and smart care technologies. Results: The author identifies 18 key studies (17 clinical trials) from 175 citations found in internet databases and categorizes them using problem-specific groupings, medical/health domains, machine learning models, algorithms, and techniques. Conclusions: Machine learning combined with the application of evidence-based practices in healthcare can enhance telehealth and smart care strategies by improving quality of personalized care, early detection of health-related problems, patient quality of life, patient-physician communication, resource efficiency and cost-effectiveness. However, this requires interdisciplinary expertise and collaboration among stakeholders, including clinicians, informaticians, and policymakers. Therefore, further research using clinicall studies, systematic reviews, analyses, and meta-analyses is required to fully exploit the potential of machine learning in this area.

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Section: Background and Related Workmentioning

confidence: 99%

Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Christopoulou

2024

BioMedInformatics

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“…The incorporation of device-related charges into healthcare insurance benefit packages is necessary in order to reduce the digital gap that is established based on economic disparities. In addition, loaner digital wearables have the potential to assist in offseting the financial effects on healthcare, provided that safeguards are in place to ensure that the digital gadgets are returned without any problems [ 202 ]. A lack of digital literacy can not only hinder the implementation of digital technology but also affect the degree to which patients comply with data transmission.…”

Section: Reviewmentioning

confidence: 99%

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Medhi,

Kamidi,

Mamatha Sree

et al. 2024

Cureus

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Heart failure (HF) is prevalent globally. It is a dynamic disease with varying definitions and classifications due to multiple pathophysiologies and etiologies. The diagnosis, clinical staging, and treatment of HF become complex and subjective, impacting patient prognosis and mortality. Technological advancements, like artificial intelligence (AI), have been significant roleplays in medicine and are increasingly used in cardiovascular medicine to transform drug discovery, clinical care, risk prediction, diagnosis, and treatment. Medical and surgical interventions specific to HF patients rely significantly on early identification of HF. Hospitalization and treatment costs for HF are high, with readmissions increasing the burden. AI can help improve diagnostic accuracy by recognizing patterns and using them in multiple areas of HF management. AI has shown promise in offering early detection and precise diagnoses with the help of ECG analysis, advanced cardiac imaging, leveraging biomarkers, and cardiopulmonary stress testing. However, its challenges include data access, model interpretability, ethical concerns, and generalizability across diverse populations. Despite these ongoing efforts to refine AI models, it suggests a promising future for HF diagnosis. After applying exclusion and inclusion criteria, we searched for data available on PubMed, Google Scholar, and the Cochrane Library and found 150 relevant papers. This review focuses on AI's significant contribution to HF diagnosis in recent years, drastically altering HF treatment and outcomes.

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“…Machine learning techniques (often referred to as artificial intelligence) also enter the field of clinical prediction modeling of CHFS. The application of machine learning methods to CHFS syndrome is freshly reviewed in [115][116][117]. It has become an excellent practice to compare the efficiency of machine learning classifiers with the efficiency of established and well-researched logistic regression.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review

Odrobina

2024

Diagnostics

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This study attempts to identify and briefly describe the current directions in applied and theoretical clinical prediction research. Context-rich chronic heart failure syndrome (CHFS) telemedicine provides the medical foundation for this effort. In the chronic stage of heart failure, there are sudden exacerbations of syndromes with subsequent hospitalizations, which are called acute decompensation of heart failure (ADHF). These decompensations are the subject of diagnostic and prognostic predictions. The primary purpose of ADHF predictions is to clarify the current and future health status of patients and subsequently optimize therapeutic responses. We proposed a simplified discrete-state disease model as an attempt at a typical summarization of a medical subject before starting predictive modeling. The study tries also to structure the essential common characteristics of quantitative models in order to understand the issue in an application context. The last part provides an overview of prediction works in the field of CHFS. These three parts provide the reader with a comprehensive view of quantitative clinical predictive modeling in heart failure telemedicine with an emphasis on several key general aspects. The target community is medical researchers seeking to align their clinical studies with prognostic or diagnostic predictive modeling, as well as other predictive researchers. The study was written by a non-medical expert.

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Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications

Cited by 23 publications

References 75 publications

Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Machine Learning Models and Technologies for Evidence-Based Telehealth and Smart Care: A Review

Artificial Intelligence and Its Role in Diagnosing Heart Failure: A Narrative Review

Clinical Predictive Modeling of Heart Failure: Domain Description, Models’ Characteristics and Literature Review

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