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

Research output: Contribution to journalReview articlepeer-review

15 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number2964
JournalDiagnostics
Volume12
Issue number12
DOIs
StatePublished - Dec 2022

Keywords

  • heart failure
  • machine learning
  • pressure sensors
  • remote monitoring
  • time-series analysis

ASJC Scopus subject areas

  • Clinical Biochemistry

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