细胞减少症是感染HIV的住院患者常见的并发症之一。它可能对这些患者的治疗结果产生负面影响。然而，通过利用机器学习技术和电子病历，可以开发一个预测模型，评估感染HIV患者住院期间发生细胞减少症的风险。这样的模型对于为HIV患者设计更个体化和基于证据的治疗策略至关重要。本研究是在2016年6月至2021年10月期间入住广西胸科医院的HIV患者上进行的。我们从电子病历中提取了共66个临床特征，并利用这些特征训练了5个机器学习预测模型（人工神经网络[ANN]、自适应增强[AdaBoost]、k近邻[KNN]、支持向量机[SVM]、决策树[DT]）。模型使用20%的数据进行了测试。以接受者工作特征曲线下面积（AUC）等指标评估了模型的性能。最佳的预测模型通过shapley加性解释（SHAP）进行了解释。ANN模型具有更好的预测能力。根据ANN模型的SHAP解释，低蛋白血症和癌症是感染HIV住院患者细胞减少症的最重要的预测特征。与此同时，较低的血红蛋白/红细胞分布宽度比（HGB/RDW）、低密度脂蛋白胆固醇（LDL-C）水平、CD4+ T细胞计数和肌酸清除率（Ccr）水平增加了感染HIV住院患者细胞减少症的风险。本研究利用机器学习和电子病历信息构建了HIV患者住院期间细胞减少症的风险预测模型。该预测模型对于合理管理HIV住院患者和制定个性化治疗计划至关重要。 版权所有 © 2023 Huang, Xie, Zhang, Xu, Su, Lv, Lu, Qin, Pang, Qiu, Li, Wei, Huang, Meng, Hu和Lv.

Cytopenia is a frequent complication among HIV-infected patients who require hospitalization. It can have a negative impact on the treatment outcomes for these patients. However, by leveraging machine learning techniques and electronic medical records, a predictive model can be developed to evaluate the risk of cytopenia during hospitalization in HIV patients. Such a model is crucial for designing a more individualized and evidence-based treatment strategy for HIV patients.The present study was conducted on HIV patients who were admitted to Guangxi Chest Hospital between June 2016 and October 2021. We extracted a total of 66 clinical features from the electronic medical records and employed them to train five machine learning prediction models (artificial neural network [ANN], adaptive boosting [AdaBoost], k-nearest neighbour [KNN] and support vector machine [SVM], decision tree [DT]). The models were tested using 20% of the data. The performance of the models was evaluated using indicators such as the area under the receiver operating characteristic curve (AUC). The best predictive models were interpreted using the shapley additive explanation (SHAP).The ANN models have better predictive power. According to the SHAP interpretation of the ANN model, hypoproteinemia and cancer were the most important predictive features of cytopenia in HIV hospitalized patients. Meanwhile, the lower hemoglobin-to-RDW ratio (HGB/RDW), low-density lipoprotein cholesterol (LDL-C) levels, CD4+ T cell counts, and creatinine clearance (Ccr) levels increase the risk of cytopenia in HIV hospitalized patients.The present study constructed a risk prediction model for cytopenia in HIV patients during hospitalization with machine learning and electronic medical record information. The prediction model is important for the rational management of HIV hospitalized patients and the personalized treatment plan setting.Copyright © 2023 Huang, Xie, Zhang, Xu, Su, Lv, Lu, Qin, Pang, Qiu, Li, Wei, Huang, Meng, Hu and Lv.