Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jul 2, 2021
Date Accepted: Dec 4, 2021
Machine Learning for Cardiovascular Outcomes from Wearable Data: a Systematic Review from a Technology Readiness Level Point of View
ABSTRACT
Background:
Wearable technology has the potential to improve cardiovascular health monitoring using machine learning. It enables remote health monitoring and allows for diagnosis and prevention. In addition to detection of cardiovascular disease, it can exclude a diagnosis in symptomatic patients, preventing unnecessary hospital visits. Furthermore, early warning systems can aid the cardiologist in timely treatment and prevention.
Objective:
We systematically assessed literature on detecting and predicting outcomes of cardiovascular disease with data obtained from wearables to gain insights in the current challenges and limitations.
Methods:
We searched PubMed, Scopus and IEEE Xplore on September 26, 2020 with no restrictions on the publication date using keywords: wearables, machine learning and cardiovascular disease. Methodologies were categorized and analyzed according to machine learning based technology readiness levels (TRLs) that score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready).
Results:
After removal of duplicates, applying exclusion criteria, and full-text screening, 55 eligible studies remained for analysis, covering a variety of cardiovascular diseases. None of the studies were integrated into a health care system (TRL < 6), prospectively phase 2 and 3 trials were absent (TRL < 7 and 8) and group cross-validation was rarely used, limiting to demonstrate their effectiveness. Furthermore, there seems to be no agreement on the sample size needed to train these models, the size of the observation window used to make predictions, how long subjects should be observed and the type of machine learning model that is suitable for predicting cardiovascular outcomes.
Conclusions:
Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic and/or prognostic cardiovascular clinical tool is hampered by the lack of using a realistic dataset and a proper systematic and prospective evaluation.
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