Time Series Models of the Human Heart in Patients with Heart Failure: Toward a Digital Twin Approach.

Nilmini Wickramasinghe, Nalika Ulapane, Yuxin Zhang, Paul Jansons, Gunnar Cedersund, Ralph Maddison

Abstract read

In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

–field-weighted citation impact

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

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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.

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Nilmini WickramasingheSchool of Computing, Engineering & Mathematical Sciences, La Trobe University, Melbourne, VIC 3086, Australia.ORCID 0000-0002-1314-8843

Nalika UlapaneSchool of Computing, Engineering & Mathematical Sciences, La Trobe University, Melbourne, VIC 3086, Australia.ORCID 0000-0003-3432-3943

Yuxin ZhangFaculty of Health, Deakin University, Melbourne, VIC 3125, Australia.ORCID 0000-0002-7636-4084

Paul JansonsFaculty of Health, Deakin University, Melbourne, VIC 3125, Australia.ORCID 0000-0002-8766-0516

Gunnar CedersundDepartment of Biomedical Engineering (IMT), Linköping University, SE-581 83 Linköping, Sweden.

Ralph MaddisonFaculty of Health, Deakin University, Melbourne, VIC 3125, Australia.ORCID 0000-0001-8564-5518

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Digital Twins (DTs) are digital replicas of physical entities. The use of DTs in healthcare is a growing area of research. With DTs, there is potential to revolutionize healthcare with the assistance of Artificial Intelligence. This can lead to achieving precision, personalization, and value addition in healthcare. Contributing to this field, we present one of the first attempts of uncovering time series models of decompensation of heart failure. This was performed using some of the first data collected from the pilot phase of the SmartHeart study, in which an at-home, wearable, wireless sensor-based digital self-monitoring system for people with heart failure was tested.

Indexed as

HeartHeart FailureArtificial IntelligenceDigital HealthFemaleHumansMonitoring, PhysiologicWearable Electronic Devicesartificial intelligencechronic diseasedigital twinheart failuremachine learningpersonalized careprecision medicineregressiontime serieswearable sensors

Identifiers

PMID41516517

DOI10.3390/s26010082

PMCPMC12787325

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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.