Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

Chayakrit Krittanawong, Albert J. Rogers, Kipp W. Johnson, Zhen Wang, Mintu P. Turakhia, Jonathan L. Halperin, Sanjiv M. Narayan

Research output: Contribution to journalReview articlepeer-review

96 Scopus citations

Abstract

Ambulatory monitoring is increasingly important for cardiovascular care but is often limited by the unpredictability of cardiovascular events, the intermittent nature of ambulatory monitors and the variable clinical significance of recorded data in patients. Technological advances in computing have led to the introduction of novel physiological biosignals that can increase the frequency at which abnormalities in cardiovascular parameters can be detected, making expert-level, automated diagnosis a reality. However, use of these biosignals for diagnosis also raises numerous concerns related to accuracy and actionability within clinical guidelines, in addition to medico-legal and ethical issues. Analytical methods such as machine learning can potentially increase the accuracy and improve the actionability of device-based diagnoses. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy. These tools are increasingly recognized by regulatory agencies and professional medical societies, but several technical and ethical issues remain. In this Review, we describe the current state of cardiovascular monitoring along the continuum from biosignal acquisition to the identification of novel biosensors and the development of analytical techniques and ultimately to regulatory and ethical issues. Furthermore, we outline new paradigms for cardiovascular monitoring.

Original languageEnglish
Pages (from-to)75-91
Number of pages17
JournalNature Reviews Cardiology
Volume18
Issue number2
DOIs
StatePublished - Feb 2021

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