湿疹与银屑病鉴别诊断的多中心门诊队列数据集

数据集获取（Figshare）

• 源站标题：Table 1_Differential diagnosis of eczema and psoriasis using routine clinical data and machine learning: development of a web-based tool in a multicenter outpatient cohort.docx
• DOI：10.3389/fmed.2025.1667794.s001
• 发布日期：2025-10-17
• 文件数：1 个
• 文件总体积：15.35 KB
• 源站页面：https://frontiersin.figshare.com/articles/dataset/Table_1_Differential_diagnosis_of_eczema_and_psoriasis_using_routine_clinical_data_and_machine_learning_development_of_a_web-based_tool_in_a_multicenter_outpatient_cohort_docx/30381622

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curl -L -o "Table 1_Differential diagnosis of eczema and psoriasis using routine clinical data and machine learning: development of a web-based tool in a multicen.docx" "https://ndownloader.figshare.com/files/58819255"

文件清单

• Table 1_Differential diagnosis of eczema and psoriasis using routine clinical data and machine learning: development of a web-based tool in a multicen.docx（15.35 KB）

文件总体积 15.35 KB（源站 API 实测）。

数据说明

BackgroundEczema and psoriasis are common chronic dermatoses with overlapping features, making early differential diagnosis difficult. While biopsy is the gold standard, its invasiveness and dependence on clinician expertise restrict routine application, especially in primary care. To overcome these limitations, we developed a machine learning-based diagnostic tool using routine laboratory data, enabling non-invasive, accurate, and practical differentiation between eczema and psoriasis in outpatient settings.
MethodsWe retrospectively analyzed clinical and routine laboratory data from 57,518 patients with eczema and psoriasis across three medical centers. Patients with confirmed diagnoses and complete laboratory records were included, while those with missing key data were excluded. Eight machine learning models were trained using data from Shengjing Hospital. Model performance was evaluated using accuracy, AUC, sensitivity, specificity, PPV, NPV, F1 score, and confusion matrix. The best-performing model, XGBoost, was externally validated on independent cohorts from two other hospitals. SHapley Additive exPlanation (SHAP) were applied to assess feature importance. Finally, a web-based tool was developed integrating the optimal model with optical character recognition (OCR) for automatic data input.
ResultsXGBoost demonstrated the best performance, with AUCs of 0.891, 0.830, and 0.812 for the training, internal test, and external test sets, respectively. Key predictive features included dNLR, neutrophil count, SIRI, RDW, and eosinophil count, which were consistent with known clinical patterns. The final model was deployed as an interactive web tool, allowing manual or OCR-based data input to provide real-time prediction probabilities.
ConclusionThis machine learning-based diagnostic tool showed strong performance and interpretability in differentiating eczema from psoriasis using routine laboratory data. The user-friendly web interface enables rapid, non-invasive decision support in outpatient clinical settings.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.3389/fmed.2025.1667794.s001（source: https://frontiersin.figshare.com/articles/dataset/Table_1_Differential_diagnosis_of_eczema_and_psoriasis_using_routine_clinical_data_and_machine_learning_development_of_a_web-based_tool_in_a_multicenter_outpatient_cohort_docx/30381622）。