Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei, Taiwan.
Graduate Institute of Clinical Medicine, Taipei Medical University, Taipei, Taiwan.
J Med Internet Res. 2023 Mar 28;25:e39972. doi: 10.2196/39972.
BACKGROUND: Psoriasis (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. OBJECTIVE: The 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. METHODS: This 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. RESULTS: The 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). CONCLUSIONS: The 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.
背景：银屑病（PsO）是一种慢性、全身性、免疫介导的疾病，多器官受累。银屑病关节炎（PsA）是一种炎症性关节炎，存在于 6%-42%的银屑病患者中。大约 15%的银屑病患者未被诊断为 PsA。预测患有 PsA 风险的患者对于为他们提供早期检查和治疗至关重要，这可以防止不可逆的疾病进展和功能丧失。 目的：本研究旨在使用机器学习算法，基于时间顺序的大规模多维电子病历，开发和验证一种针对 PsA 的预测模型。 方法：本病例对照研究使用了台湾的全民健康保险研究数据库，时间范围为 1999 年 1 月 1 日至 2013 年 12 月 31 日。原始数据集按照 80:20 的比例分为训练集和验证集。使用卷积神经网络开发预测模型。该模型使用具有时间顺序信息的 2.5 年诊断和病历（住院和门诊）来预测给定患者在接下来 6 个月内患 PsA 的风险。使用训练数据开发和交叉验证模型，并使用验证数据进行测试。进行遮挡敏感性分析以确定模型的重要特征。 结果：预测模型共纳入 443 例早期诊断为 PsO 且有 PsA 的患者和 1772 例无 PsA 的 PsO 患者作为对照组。使用序列诊断和药物处方信息作为时间表型图谱的 6 个月 PsA 风险预测模型，其接收者操作特征曲线下面积为 0.70（95%CI 0.559-0.833），平均敏感度为 0.80（SD 0.11），平均特异性为 0.60（SD 0.04），平均阴性预测值为 0.93（SD 0.04）。 结论：本研究结果表明，该风险预测模型可识别出患有银屑病且患银屑病关节炎风险较高的患者。该模型可帮助医疗保健专业人员为高危人群提供优先治疗，防止不可逆的疾病进展和功能丧失。
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