Evidence map›Paper›PMID 40537776›Full record

ArticleRespiratory research2025

Innovating care for people with sarcoidosis using a machine learning-driven approach.

Vivienne Kahlmann, Astrid Dunweg, Heleen Kicken, Nick Jelicic, Johanna M Hendriks, Richard Goossens, Marlies S Wijsenbeek, Jiwon Jung

Abstract read
In one paragraph

Article in Respiratory research, 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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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Vivienne KahlmannCentre of Excellence for Interstitial Lung Diseases and Sarcoidosis, Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands.
Astrid DunwegCentre of Excellence for Interstitial Lung Diseases and Sarcoidosis, Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands.
Heleen KickenFaculty of Industrial Design Engineering, Delft University of Technology (TU Delft), Delft, the Netherlands.
Nick JelicicUniversity Library, Erasmus University of Rotterdam, Rotterdam, the Netherlands.
Johanna M HendriksSurgery Department, Erasmus University Medical Center, Rotterdam, the Netherlands.
Richard GoossensFaculty of Industrial Design Engineering, Delft University of Technology (TU Delft), Delft, the Netherlands.
Marlies S Wijsenbeek *Centre of Excellence for Interstitial Lung Diseases and Sarcoidosis, Department of Respiratory Medicine, Erasmus University Medical Center, Rotterdam, the Netherlands.
Jiwon Jung *Faculty of Industrial Design Engineering, Delft University of Technology (TU Delft), Delft, the Netherlands. j.jung@erasmusmc.nl.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionUnderstanding patients' everyday experience is essential to improve patient centered care in sarcoidosis. So far, patient perspectives are based on survey- and qualitative research.

aimWe aimed to assess patient-driven perspectives on their care trajectories using a novel machine learning-driven approach (MLD).

methodsWe used the largest Dutch sarcoidosis patient platform as the data source of patient stories. The patients' stories were extracted with permission. We applied topic modelling (to generate topics among the posts), and sentiment analysis (to find tone of voice in the topics). To validate the findings, we read the top 50 most relevant posts of each topic. An in-depth patients' disease trajectory map was made.

resultsBased on 4969 forum posts, 30 final topics and 10 upper themes were generated, which formed the basis for the "patient journey-map" which shows patients' perspective across the care pathway. Important decision moments could be identified, as well as care "tracks" at home and hospital and topics associated with positive or negative emotions. Most patients' perspectives were about symptoms (mainly negative sentiment), disease-modifying medication (mainly neutral sentiment), and quality of life (negative, neutral and positive). DISCUSSION: A major part of living with sarcoidosis takes place outside the view of the hospital, but this part often remains invisible. MLD is an innovative approach, providing a comprehensive overview of patients' perspectives on health and care. Integrating, these findings in the design of health care delivery has the potential to improve patient-centered care.

Indexed as

Machine LearningSarcoidosisFemaleHumansMaleNetherlandsPatient-Centered CareQuality of LifeArtificial intelligenceInnovating careInterstitial lung diseasePatient-centered carePatient perspectivesQuality of lifeSarcoidosis

Identifiers

PMID40537776
PMCPMC12178051

What Socratic holds

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LicenceCC BY-NC-ND
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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 Socratic graph.