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

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. T he se tools are increasingly recognized by regulatory a g e n c ies a n d p r o f e s s ional m e d ic al s oc ie ti es, 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.

Internal Flow Choking in Cardiovascular System: A Radical Theory in the Risk Assessment of Asymptomatic Cardiovascular Diseases

“…Although the diagnostic sciences have been advanced significantly during the last eight decades [32][33][34][35][36][37][38][39][40], until the theoretical discovery of the Sanal flow choking, the real occurrence of acute-heart-failure was poorly understood, largely for the reason that it was an under diagnosis condition [2]. Now the real cause of an acute-heart-failure comes to the foreground [1,2].…”

Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

“…[ 148 ] Similarly, when employed in ambulatory monitoring of cardiovascular patients, ML‐reinforced biosensors can interpret arrhythmias, evaluate the hemodynamic consequences of heart failure, arrhythmias, or coronary syndromes. [ 149 ] Also, LSTM algorithms can be applied to predict the future blood glucose level in a personalized healthcare monitoring system for diabetic patients. [ 142 ] ML can also enable biosensors to diagnose diseases out of the clinic.…”

“…[ 156 ] This challenge should be tackled collectively by sensor users, data owners, and service providers. [ 149 ] For sensor users such as patients, more tools and instructions are needed for better data sharing and reporting without taking the risk of information leakage. For data owners such as physicians and sensor providers, establishing a safer data‐sharing platform by technological means and moral regulations are of high priority.…”

Remote monitoring and digital health tools in CVD management

“…Symptoms, body weight, activity, blood pressure, pulse rate and regularity, heart sounds, respiratory rate, an electrocardiogram, oxygen saturation and sleep quality can all be assessed by a variety of stand-alone technologies. These technologies include an increasing array of wearables, such as a watch or patch 2 . Advances in point-of-care testing further augment the ability to manage patients in out-of-hospital settings.…”