ArticleNature communications2023
A machine learning model identifies patients in need of autoimmune disease testing using electronic health records.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 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
18 citing papers in PubMed, 27 citations in OpenAlex.
- Toward Precision Oral Medicine in Pemphigus Vulgaris: A Conceptual AI Framework for Rituximab Response Prediction.Pharmaceuticals (Basel, Switzerland) · 2026Article
- Capturing multi-disease states on a spectrum with machine learning and routine clinical data.Med (New York, N.Y.) · 2026Article
- Development and external validation of a machine learning model for predicting chronic critical illness in ICU patients with acute pancreatitis.BMC medical informatics and decision making · 2026Article
- Type 1 Diabetes and Other Autoimmune Diseases-Epidemiology, Pathophysiology and Screening.Endocrinology, diabetes & metabolism · 2026Review
- Natural language processing to intercept rheumatoid and psoriatic arthritis from clinical notes in the Emergency Department.Therapeutic advances in musculoskeletal disease · 2026Article
- Machine learning-based penetrance of genetic variants.Science (New York, N.Y.) · 2025Article
- Genetic analyses of eight complex diseases using predicted continuous representations of disease.Cell reports methods · 2025Article
- Machine Learning-Based Plasma Protein Risk Score Improves Atrial Fibrillation Prediction Over Clinical and Genomic Models.Circulation. Genomic and precision medicine · 2025Article
- Comparative analysis of large language models and traditional diagnostic decision support systems for rare rheumatic disease identification.EULAR rheumatology open · 2025Article
- Exposure to autoimmune disorders is associated with increased Alzheimer's disease risk in a multi-site electronic health record analysis.Cell reports. Medicine · 2025Observational
- Applications of Artificial Intelligence in Vasculitides: A Systematic Review.ACR open rheumatology · 2025Review
- Unlocking precision medicine: clinical applications of integrating health records, genetics, and immunology through artificial intelligence.Journal of biomedical science · 2025Review
- Review
- Achieving health equity in immune disease: leveraging big data and artificial intelligence in an evolving health system landscape.Frontiers in big data · 2025Review
- Machine learning for precision diagnostics of autoimmunity.Scientific reports · 2024Article
- Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data.Diabetologia · 2024Article
- Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases.Lupus science & medicine · 2024Article
- Cholesterol Contributes to Risk, Severity, and Machine Learning-Driven Diagnosis of Lyme Disease.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 3 institutions in 1 country.
Funding
Abstract
Systemic autoimmune rheumatic diseases (SARDs) can lead to irreversible damage if left untreated, yet these patients often endure long diagnostic journeys before being diagnosed and treated. Machine learning may help overcome the challenges of diagnosing SARDs and inform clinical decision-making. Here, we developed and tested a machine learning model to identify patients who should receive rheumatological evaluation for SARDs using longitudinal electronic health records of 161,584 individuals from two institutions. The model demonstrated high performance for predicting cases of autoantibody-tested individuals in a validation set, an external test set, and an independent cohort with a broader case definition. This approach identified more individuals for autoantibody testing compared with current clinical standards and a greater proportion of autoantibody carriers among those tested. Diagnoses of SARDs and other autoimmune conditions increased with higher model probabilities. The model detected a need for autoantibody testing and rheumatology encounters up to five years before the test date and assessment date, respectively. Altogether, these findings illustrate that the clinical manifestations of a diverse array of autoimmune conditions are detectable in electronic health records using machine learning, which may help systematize and accelerate autoimmune testing.
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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.