Contemporary Applications of Machine Learning for Device Therapy in Heart Failure

Gautam, Nitesh; Ghanta, Sai Nikhila; Clausen, Alex; Saluja, Prachi; Sivakumar, Kalai; Dhar, Gaurav; Chen, Qi; DeMazumder, Deeptankar; Rabbat, Mark; Greene, Stephen J.; Fudim, Marat; Al’Aref, Subhi J.

doi:10.1016/j.jchf.2022.06.011

Cited by 10 publications

(3 citation statements)

References 68 publications

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“…On the other hand, heart failure is a complicated disease and involves interaction of multiple tissues and organ systems. Therefore, data integration and model interpretability are the major challenges preventing widespread assimilation into clinical practice ( 55 ), which might lead to practical application issues, particularly in clinical decision-making.…”

Section: Discussionmentioning

confidence: 99%

Bibliometric analysis of 100 top cited articles of heart failure–associated diseases in combination with machine learning

Kuang¹,

Zhong

Liang

et al. 2023

Front. Cardiovasc. Med.

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ObjectiveThe aim of this paper is to analyze the application of machine learning in heart failure-associated diseases using bibliometric methods and to provide a dynamic and longitudinal bibliometric analysis of heart failure–related machine learning publications.Materials and methodsWeb of Science was screened to gather the articles for the study. Based on bibliometric indicators, a search strategy was developed to screen the title for eligibility. Intuitive data analysis was employed to analyze the top-100 cited articles and VOSViewer was used to analyze the relevance and impact of all articles. The two analysis methods were then compared to get conclusions.ResultsThe search identified 3,312 articles. In the end, 2,392 papers were included in the study, which were published between 1985 and 2023. All articles were analyzed using VOSViewer. Key points of the analysis included the co-authorship map of authors, countries and organizations, the citation map of journal and documents and a visualization of keyword co-occurrence analysis. Among these 100 top-cited papers, with a mean of 122.9 citations, the most-cited article had 1,189, and the least cited article had 47. Harvard University and the University of California topped the list among all institutes with 10 papers each. More than one-ninth of the authors of these 100 top-cited papers wrote three or more articles. The 100 articles came from 49 journals. The articles were divided into seven areas according to the type of machine learning approach employed: Support Vector Machines, Convolutional Neural Networks, Logistic Regression, Recurrent Neural Networks, Random Forest, Naive Bayes, and Decision Tree. Support Vector Machines were the most popular method.ConclusionsThis analysis provides a comprehensive overview of the artificial intelligence (AI)-related research conducted in the field of heart failure, which helps healthcare institutions and researchers better understand the prospects of AI in heart failure and formulate more scientific and effective research plans. In addition, our bibliometric evaluation can assist healthcare institutions and researchers in determining the advantages, sustainability, risks, and potential impacts of AI technology in heart failure.

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Section: Discussionmentioning

confidence: 99%

Bibliometric analysis of 100 top cited articles of heart failure–associated diseases in combination with machine learning

Kuang¹,

Zhong

Liang

et al. 2023

Front. Cardiovasc. Med.

View full text Add to dashboard Cite

show abstract

“…Such algorithms and data may help us move from expert guidance on titration to data-driven information that incorporates patient factors (vital signs, laboratory studies, allergies, current medications, drug-drug interactions, and comorbid conditions) to generate therapy recommendations to the clinicians. However, the impact of learning algorithms on providing clinical benefit in HF care remains unproven [ 66 ].…”

Section: Strategiesmentioning

confidence: 99%

Novel Strategies to Improve Prescription of Guideline-Directed Medical Therapy in Heart Failure

Brooksbank

Faulkenberg

Tang

et al. 2023

Curr Treat Options Cardio Med

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Purpose of review To examine the emerging data for novel strategies being studied to improve use and dose titration of guideline-directed medical therapy (GDMT) for patients with heart failure (HF). Recent findings There is mounting evidence to employ novel multi-pronged strategies to address HF implementation gaps. Summary Despite high-level randomized evidence and clear national society recommendations, a large gap persists in use and dose titration of guideline-directed medical therapy (GDMT) in patients with heart failure (HF). Accelerating the safe implementation of GDMT has proven to reduce the morbidity and mortality associated with HF but remains an ongoing challenge for patients, clinicians, and health systems. In this review, we examine the emerging data for novel strategies to improve the use of GDMT including the use of multidisciplinary team-based approaches, nontraditional patient encounters, patient messaging/engagement, remote patient monitoring, and electronic health record (EHR)-based clinical alerts. While societal guidelines and implementation studies have focused on heart failure with reduced ejection fraction (HFrEF), expanding indications and evidence for the use of sodium glucose cotransporter2 (SGLT2i) will necessitate implementation efforts across the LVEF spectrum.

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“…The above-mentioned techniques have been applied in the field of HF for initial diagnosis, modeling disease prognoses, goal-directed medical therapy (GDMT) optimization, predicting outcomes for device therapy interventions, etc. ( Figure 2 ) [ 13 ].…”

Section: Introduction To Ai/ml and Time-series Forecastingmentioning

confidence: 99%

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

et al. 2022

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Substantial milestones have been attained in the field of heart failure (HF) diagnostics and therapeutics in the past several years that have translated into decreased mortality but a paradoxical increase in HF-related hospitalizations. With increasing data digitalization and access, remote monitoring via wearables and implantables have the potential to transform ambulatory care workflow, with a particular focus on reducing HF hospitalizations. Additionally, artificial intelligence and machine learning (AI/ML) have been increasingly employed at multiple stages of healthcare due to their power in assimilating and integrating multidimensional multimodal data and the creation of accurate prediction models. With the ever-increasing troves of data, the implementation of AI/ML algorithms could help improve workflow and outcomes of HF patients, especially time series data collected via remote monitoring. In this review, we sought to describe the basics of AI/ML algorithms with a focus on time series forecasting and the current state of AI/ML within the context of wearable technology in HF, followed by a discussion of the present limitations, including data integration, privacy, and challenges specific to AI/ML application within healthcare.

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Contemporary Applications of Machine Learning for Device Therapy in Heart Failure

Cited by 10 publications

References 68 publications

Bibliometric analysis of 100 top cited articles of heart failure–associated diseases in combination with machine learning

Bibliometric analysis of 100 top cited articles of heart failure–associated diseases in combination with machine learning

Novel Strategies to Improve Prescription of Guideline-Directed Medical Therapy in Heart Failure

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

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