Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications
Nitesh Gautam,
Sai Nikhila Ghanta,
Joshua Mueller,
Munthir Mansour,
Zhongning Chen,
Clara Puente,
Yu Mi Ha,
Tushar Tarun,
Gaurav Dhar,
Kalai Sivakumar,
Yiye Zhang,
Ahmed Abu Halimeh,
Ukash Nakarmi,
Sadeer Al-Kindi,
Deeptankar DeMazumder,
Subhi J. Al’Aref
Affiliations
Nitesh Gautam
Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Sai Nikhila Ghanta
Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Joshua Mueller
Department of Internal Medicine, University of Arkansas for Medical Sciences Northwest Regional Campus, Fayetteville, AR 72703, USA
Munthir Mansour
Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Zhongning Chen
Department of Hematology and Oncology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Clara Puente
Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Yu Mi Ha
Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Tushar Tarun
Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Gaurav Dhar
Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Kalai Sivakumar
Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
Yiye Zhang
Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA
Ahmed Abu Halimeh
Information Science Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA
Ukash Nakarmi
Department of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA
Sadeer Al-Kindi
University Hospitals Harrington Heart & Vascular Institute, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA
Deeptankar DeMazumder
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
Subhi J. Al’Aref
Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA
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.
