Evidence map›Paper›PMID 36552971›Full record

ReviewDiagnostics (Basel, Switzerland)2022

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 and 6 more

Open access · goldAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Diagnostics (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
33citing papers in PubMed, 3 pooled it
11.7field-weighted citation impact, top 1% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

33 citing papers in PubMed, 3 syntheses or guidelines pooled it, 75 citations in OpenAlex.

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  9. Who will be our adult congenital heart disease patient tomorrow?European heart journal supplements : journal of the European Society of Cardiology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

16 authors at 6 institutions in 1 country.

Nitesh GautamDepartment of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.ORCID 0000-0003-3407-3745
Sai Nikhila GhantaDepartment of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Joshua MuellerDepartment of Internal Medicine, University of Arkansas for Medical Sciences Northwest Regional Campus, Fayetteville, AR 72703, USA.
Munthir MansourDepartment of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.ORCID 0000-0002-5916-4291
Zhongning ChenDepartment of Hematology and Oncology, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Clara PuenteDepartment of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Yu Mi HaDepartment of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Tushar TarunDivision of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Gaurav DharDivision of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Kalai SivakumarDivision of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Yiye ZhangDepartment of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, USA.
Ahmed Abu HalimehInformation Science Department, University of Arkansas at Little Rock, Little Rock, AR 72204, USA.
Ukash NakarmiDepartment of Computer Science and Computer Engineering, University of Arkansas, Fayetteville, AR 72701, USA.
Sadeer Al-KindiUniversity Hospitals Harrington Heart & Vascular Institute, Case Western Reserve University School of Medicine, Cleveland, OH 44106, USA.
Deeptankar DeMazumderDivision of Cardiology, Department of Internal Medicine, Richard L. Roudebush Veterans' Administration Medical Center Indiana Institute for Medical Research, Indiana University School of Medicine, Indianapolis, IN 46202, USA.
Subhi J Al'ArefDivision of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.ORCID 0000-0002-2258-2035
University of Arkansas for Medical Sciences · USCornell University · USRichard L. Roudebush VA Medical Center · USUniversity Hospitals of Cleveland · USUniversity of Arkansas at Fayetteville · USUniversity of Arkansas at Little Rock · US

Funding

Eavesdropping on heart-brain conversations during sleep for early detection and prevention of fatal cardiovascular diseaseDP2HL157941 · NHLBI · VETERANS HEALTH FOUNDATION · PI DEMAZUMDER, DEEPTANKAR · 2020 to 2020
$2.4M
Autonomic remodeling and modulation therapy in heart failure and sudden deathR00HL130662 · NHLBI · UNIVERSITY OF CINCINNATI · PI DEMAZUMDER, DEEPTANKAR · 2017 to 2019
$747k
Autonomic remodeling and modulation therapy in heart failure and sudden deathK99HL130662 · NHLBI · JOHNS HOPKINS UNIVERSITY · PI DEMAZUMDER, DEEPTANKAR · 2016 to 2016
$172k
NHLBI NIH HHS DP2 HL157941NHLBI NIH HHS K99 HL130662NHLBI NIH HHS L30 HL115782NHLBI NIH HHS R00 HL130662
6 · The paper itself

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.

Indexed as

heart failuremachine learningpressure sensorsremote monitoringtime-series analysis

Identifiers

PMID36552971
PMCPMC9777312
OpenAlexW4310202133

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.