Evidence map›Paper›PMID 36090536›Full record

ReviewAmerican journal of preventive cardiology2022

Medicine 2032: The future of cardiovascular disease prevention with machine learning and digital health technology.

Aamir Javaid, Fawzi Zghyer, Chang Kim, Erin M Spaulding, Nino Isakadze, Jie Ding, Daniel Kargillis, Yumin Gao, Faisal Rahman, Donald E Brown and 5 more

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

Review in American journal of preventive cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.

0numbers the graph read from it
0cells of the map it votes in
43citing papers in PubMed
23.6field-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

43 citing papers in PubMed, 91 citations in OpenAlex.

  1. Review
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  8. Machine Learning Insights into Age-Related Sex Differences in Blood Pressure: Influence of Diabetes and Body Mass Index.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 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

15 authors at 3 institutions in 1 country.

Aamir JavaidJohns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA.
Fawzi ZghyerJohns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA.
Chang KimJohns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA.
Erin M SpauldingJohns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA.
Nino IsakadzeJohns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA.
Jie DingJohns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA.
Daniel KargillisJohns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA.
Yumin GaoJohns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA.
Faisal RahmanDivision of Cardiology, Department of Medicine, Baylor College of Medicine, Houston, TX, USA.
Donald E BrownSchool of Data Science, University of Virginia, Charlottesville, VA, USA.
Suchi SariaMachine Learning and Healthcare Laboratory, Departments of Computer Science, Statistics, and Health Policy, Malone Center for Engineering in Healthcare, and Armstrong Institute for Patient Safety and Quality, Johns Hopkins University, Baltimore, MD, USA.
Seth S MartinJohns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA.
Christopher M KramerCardiovascular Division, Department of Medicine, University of Virginia Health, Charlottesville, VA, USA.
Roger S BlumenthalJohns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA.
Francoise A MarvelJohns Hopkins Ciccarone Center for the Prevention of Cardiovascular Disease, Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, 600 N. Wolfe St, Carnegie 591, Baltimore, MD 21287, USA.
Johns Hopkins University · USUniversity of Virginia · USBaylor College of Medicine · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Machine learning (ML) refers to computational algorithms that iteratively improve their ability to recognize patterns in data. The digitization of our healthcare infrastructure is generating an abundance of data from electronic health records, imaging, wearables, and sensors that can be analyzed by ML algorithms to generate personalized risk assessments and promote guideline-directed medical management. ML's strength in generating insights from complex medical data to guide clinical decisions must be balanced with the potential to adversely affect patient privacy, safety, health equity, and clinical interpretability. This review provides a primer on key advances in ML for cardiovascular disease prevention and how they may impact clinical practice.

Indexed as

Artificial intelligenceCardiologyCardiovascular diseaseDigital healthMachine learningPreventionSmartphonesSmartwatchesWearables

Identifiers

PMID36090536
PMCPMC9460561
OpenAlexW4293469511

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.