ArticleJournal of medical Internet research2023
Machine Learning Approaches for Predicting Psoriatic Arthritis Risk Using Electronic Medical Records: Population-Based Study.
Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Diagnostic accuracy of machine learning approaches to identify psoriatic arthritis: a meta-analysis.Clinical and experimental medicine · 2025Pooled it
- Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Construction of risk prediction models for psoriasis based on 45 dietary nutrients using machine learning and SHAP analysis.Asia Pacific journal of clinical nutrition · 2026Article
- Artificial Intelligence and Machine Learning in Rheumatology and Systemic Inflammatory Diseases: From Pattern Recognition to Signal Analysis and Clinical Decision Support.Journal of clinical medicine · 2026Review
- A data and knowledge cross-level fusion-driven learning framework for detecting missing diagnosis.NPJ digital medicine · 2026Article
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- The Role of Artificial Intelligence in the Diagnosis and Management of Rheumatoid Arthritis.Medicina (Kaunas, Lithuania) · 2025Article
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- AI for imaging evaluation in rheumatology: applications of radiomics and computer vision-current status, future prospects and potential challenges.Rheumatology advances in practice · 2025Review
- Review
- Using Automated Machine Learning to Predict Necessary Upcoming Therapy Changes in Patients With Psoriasis Vulgaris and Psoriatic Arthritis and Uncover New Influences on Disease Progression: Retrospective Study.JMIR formative research · 2024Article
- Principles, applications, and future of artificial intelligence in dermatology.Frontiers in medicine · 2023Review
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPsoriasis (PsO) is a chronic, systemic, immune-mediated disease with multiorgan involvement. Psoriatic arthritis (PsA) is an inflammatory arthritis that is present in 6%-42% of patients with PsO. Approximately 15% of patients with PsO have undiagnosed PsA. Predicting patients with a risk of PsA is crucial for providing them with early examination and treatment that can prevent irreversible disease progression and function loss.
objectiveThe aim of this study was to develop and validate a prediction model for PsA based on chronological large-scale and multidimensional electronic medical records using a machine learning algorithm.
methodsThis case-control study used Taiwan's National Health Insurance Research Database from January 1, 1999, to December 31, 2013. The original data set was split into training and holdout data sets in an 80:20 ratio. A convolutional neural network was used to develop a prediction model. This model used 2.5-year diagnostic and medical records (inpatient and outpatient) with temporal-sequential information to predict the risk of PsA for a given patient within the next 6 months. The model was developed and cross-validated using the training data and was tested using the holdout data. An occlusion sensitivity analysis was performed to identify the important features of the model.
resultsThe prediction model included a total of 443 patients with PsA with earlier diagnosis of PsO and 1772 patients with PsO without PsA for the control group. The 6-month PsA risk prediction model that uses sequential diagnostic and drug prescription information as a temporal phenomic map yielded an area under the receiver operating characteristic curve of 0.70 (95% CI 0.559-0.833), a mean sensitivity of 0.80 (SD 0.11), a mean specificity of 0.60 (SD 0.04), and a mean negative predictive value of 0.93 (SD 0.04).
conclusionsThe findings of this study suggest that the risk prediction model can identify patients with PsO at a high risk of PsA. This model may help health care professionals to prioritize treatment for target high-risk populations and prevent irreversible disease progression and functional loss.
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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.