Evidence map›Paper›PMID 38813389›Full record

ArticleFrontiers in medicine2024

Artificial intelligence based data curation: enabling a patient-centric European health data space.

Isabelle de Zegher, Kerli Norak, Dominik Steiger, Heimo Müller, Dipak Kalra, Bart Scheenstra, Isabella Cina, Stefan Shulz, Kanimozhi Uma, Petros Kalendralis and 4 more

Erratum issuedAbstract read
In one paragraph

Article in Frontiers in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
–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

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

9 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. [Medical education and artificial intelligence: perspectives and ethical challenges].Revista medica del Instituto Mexicano del Seguro Social · 2025
    Pooled it
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

14 authors.

Isabelle de ZegherB!loba, Tervuren, Belgium.
Kerli NorakNorth Estonia Medical Centre, Tallinn, Estonia.
Dominik SteigerMIDATA Genossenschaft, Zürich, Switzerland.
Heimo MüllerDiagnostics and Research Institute of Pathology, Medical University Graz, Graz, Austria.
Dipak KalraThe European Institute for Innovation Through Health Data, Ghent, Belgium.
Bart ScheenstraDepartment of Cardiothoracic Surgery, Cardiovascular Research Institute Maastricht, Maastricht University Medical Centre, Maastricht, Netherlands.
Isabella CinaEuropean Heart Network, Bruxelles, Belgium.
Stefan ShulzAverbis GmbH, Freiburg, Germany.
Kanimozhi UmaFaculty of Engineering Science, Department of Computer Science (HCI), Leuven, Belgium.
Petros KalendralisDepartment of Radiation Oncology (Maastro), GROW School for Oncology and Reproduction, Maastricht University Medical Centre, Maastricht, Netherlands.
Eno-Martin LotmamNorth Estonia Medical Centre, Tallinn, Estonia.
Martin BenediktDepartment of Internal Medicine, Division of Cardiology, Medical University of Graz, Graz, Austria.
Michel DumontierDepartment of Advanced Computing Sciences, Institute of Data Science, Maastricht University, Maastricht, Netherlands.
Remzi CelebiDepartment of Advanced Computing Sciences, Institute of Data Science, Maastricht University, Maastricht, Netherlands.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The emerging European Health Data Space (EHDS) Regulation opens new prospects for large-scale sharing and re-use of health data. Yet, the proposed regulation suffers from two important limitations: it is designed to benefit the whole population with limited consideration for individuals, and the generation of secondary datasets from heterogeneous, unlinked patient data will remain burdensome. AIDAVA, a Horizon Europe project that started in September 2022, proposes to address both shortcomings by providing patients with an AI-based virtual assistant that maximises automation in the integration and transformation of their health data into an interoperable, longitudinal health record. This personal record can then be used to inform patient-related decisions at the point of care, whether this is the usual point of care or a possible cross-border point of care. The personal record can also be used to generate population datasets for research and policymaking. The proposed solution will enable a much-needed paradigm shift in health data management, implementing a 'curate once at patient level, use many times' approach, primarily for the benefit of patients and their care providers, but also for more efficient generation of high-quality secondary datasets. After 15 months, the project shows promising preliminary results in achieving automation in the integration and transformation of heterogeneous data of each individual patient, once the content of the data sources managed by the data holders has been formally described. Additionally, the conceptualization phase of the project identified a set of recommendations for the development of a patient-centric EHDS, significantly facilitating the generation of data for secondary use.

Indexed as

AI-based data curationcatalogue of data sourcesdata intermediaryEHDSontologypatient-centricitypersonal health knowledge graph

Identifiers

PMID38813389
PMCPMC11133575

What OpenQuestion holds

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Registered trials

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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.