ArticleData in brief2025
Diagnosis of rheumatic and autoimmune diseases dataset.
Article in Data in brief, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
Who cites it
2 citing papers in PubMed.
- Calibrated, explainable machine learning on routine laboratory data to characterize diagnostic assignment patterns in rheumatic diseases: a retrospective study of 12,085 patients.BMC rheumatology · 2025Article
- Fuzzy evaluation and explainable machine learning for diagnosis of rheumatic and autoimmune diseases.PeerJ. Computer science · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study presents a primary dataset collected from one hospital and three laboratories in Iraq between 2019 and 2024. The dataset includes both the case and control groups, the case group comprising patients diagnosed with six common rheumatic and autoimmune diseases: rheumatoid arthritis, reactive arthritis, ankylosing spondylitis, Sjögren syndrome, systemic lupus erythematosus, and psoriatic arthritis. The dataset contains records of 12,085 patients with rheumatic and autoimmune diseases. Patient privacy is ensured through data anonymization. The dataset includes 14 features in seven classes, aiding the development of machine learning models for the early and accurate diagnosis of rheumatic and autoimmune diseases. This dataset is valuable for clinical decision support, remote healthcare, drug development, and medical diagnosis. It facilitates early diagnosis, supports explainable artificial intelligence models, advances precision medicine, enhances research, and reduces diagnostic costs and time.
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What OpenQuestion holds
Registered trials
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.