The Cincinnati Atrial Fibrillation Score (Cafs): Multicenter Validation of the First Polysomnography-Based Risk Score for Predicting Incident Atrial Fibrillation in Asymptomatic Ambulatory Community Adults

Thirumal, Rithika; Dey, Shobhana; Vaughan, Benjamin; Chen, Grace; Kappagantula, Sumedha; Güallar, Eliseo; Punjabi, Naresh M.; Tomaselli, Gordon F.; Blumenthal, Roger S.; Redline, Susan; Becker, Richard; Calkins, Hugh; Post, Wendy S.; Jones, Steven R.; DeMazumder, Deeptankar

doi:10.1016/s0735-1097(22)01018-x

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“…In a multicenter derivation study of 2807 community adults, the incorporation of DeepEntropy into a multivariate prediction model added major independent prognostic value to conventional risk factors and scores for predicting the incidence of new-onset AF [ 51 ]. Whereas the performance of conventional risk predictors are limited in diverse populations, a subsequent validation study in the Multi-Ethnic Study of Atherosclerosis demonstrated that DeepEntropy performs well regardless of age, sex, race/ethnicity, etc., and has potential for broad application [ 52 ]. Ultimately, randomized controlled trials are needed to definitively establish the clinical utility of AI/ML analysis of physiological time series for improving outcomes.…”

Section: Current Applications Of Ai/ml In Remote Monitoring Via Weara...mentioning

confidence: 99%

Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications

et al. 2022

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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.

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Section: Current Applications Of Ai/ml In Remote Monitoring Via Weara...mentioning

confidence: 99%

Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

The Cincinnati Atrial Fibrillation Score (Cafs): Multicenter Validation of the First Polysomnography-Based Risk Score for Predicting Incident Atrial Fibrillation in Asymptomatic Ambulatory Community Adults

Cited by 1 publication

References 0 publications

Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications

Artificial Intelligence, Wearables and Remote Monitoring for Heart Failure: Current and Future Applications

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