دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications.
المؤلفون: Gautam N; Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA., Ghanta SN; Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA., Mueller J; Department of Internal Medicine, University of Arkansas for Medical Sciences Northwest Regional Campus, Fayetteville, AR 72703, USA., Mansour M; Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA., Chen Z; Department of Hematology and Oncology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA., Puente C; Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA., Ha YM; Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA., Tarun T; Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA., Dhar G; Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA., Sivakumar K; Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA., Zhang Y; Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA., Halimeh AA; Information Science Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA., Nakarmi U; Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA., Al-Kindi S; University Hospitals Harrington Heart & Vascular Institute, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA., DeMazumder D; Division of Cardiology, Department of Internal Medicine, Richard L. Roudebush Veterans' Administration Medical Center Indiana Institute for Medical Research, Indiana University School of Medicine, Indianapolis, IN 46202, USA., Al'Aref SJ; Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
المصدر: Diagnostics (Basel, Switzerland) [Diagnostics (Basel)] 2022 Nov 26; Vol. 12 (12). Date of Electronic Publication: 2022 Nov 26.
نوع المنشور: Journal Article; Review
اللغة: English
بيانات الدورية: Publisher: MDPI AG Country of Publication: Switzerland NLM ID: 101658402 Publication Model: Electronic Cited Medium: Print ISSN: 2075-4418 (Print) Linking ISSN: 20754418 NLM ISO Abbreviation: Diagnostics (Basel) Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Basel, Switzerland : MDPI AG, [2011]-
مستخلص: 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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فهرسة مساهمة: Keywords: heart failure; machine learning; pressure sensors; remote monitoring; time-series analysis
تواريخ الأحداث: Date Created: 20221223 Latest Revision: 20221225
رمز التحديث: 20240628
مُعرف محوري في PubMed: PMC9777312
DOI: 10.3390/diagnostics12122964
PMID: 36552971
قاعدة البيانات: MEDLINE
الوصف
تدمد:2075-4418
DOI:10.3390/diagnostics12122964