ArticlePloS one2021
Predicting psoriasis using routine laboratory tests with random forest.
Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 13 citations in OpenAlex.
- The association between platelet indices and presence and severity of psoriasis: a systematic review and meta-analysis.Clinical and experimental medicine · 2023Pooled it
- Cell-type-specific causal effects of CEBPD in CD8 + S100B + Tcells and ZFP36 in monocytes modulate the protective and risk phenotypes in psoriasis.Mammalian genome : official journal of the International Mammalian Genome Society · 2026Article
- Complications and Laboratory Test Findings Among Patients With Generalized Pustular Psoriasis: A Retrospective Chart Review Study.Experimental dermatology · 2026Article
- Development and Validation of an Artificial Intelligence-Driven Model for Accurate Classification of Erythrodermic Psoriasis Severity: Erythrodermic Psoriasis Integrated Classification System (EPICS).American journal of clinical dermatology · 2025Article
- Early Risk Prediction for Biologic Therapy in Psoriasis Using Machine Learning Models Based on Routine Health Records.Journal of clinical medicine · 2025Article
- Enhanced Deep Learning Approach for Accurate Eczema and Psoriasis Skin Detection.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Psoriasis is a chronic inflammatory skin disease that affects approximately 125 million people worldwide. It has significant impacts on both physical and emotional health-related quality of life comparable to other major illnesses. Accurately prediction of psoriasis using biomarkers from routine laboratory tests has important practical values. Our goal is to derive a powerful predictive model for psoriasis disease based on only routine hospital tests. We collected a data set including 466 psoriasis patients and 520 healthy controls with 81 variables from only laboratory routine tests, such as age, total cholesterol, HDL cholesterol, blood pressure, albumin, and platelet distribution width. In this study, Boruta feature selection method was applied to select the most relevant features, with which a Random Forest model was constructed. The model was tested with 30 repetitions of 10-fold cross-validation. Our classification model yielded an average accuracy of 86.9%. 26 notable features were selected by Boruta, among which 15 features are confirmed from previous studies, and the rest are worth further investigations. The experimental results demonstrate that the machine learning approach has good potential in predictive modeling for the psoriasis disease given the information only from routine hospital tests.
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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.