Machine learning identifies girls with central precocious puberty based on multi-source data

Objective: The study aimed to develop simplified diagnostic models for identifying girls with central precocious puberty (CPP), without the expensive and cumbersome gonadotropin-releasing hormone (GnRH) stimulation test, which is the gold standard for CPP diagnosis. Materials and Methods: Female patients who had secondary sexual characteristics before 8 years old and had taken a GnRH analog (GnRHa) stimulation test at a medical center in Guangzhou, China were enrolled. Data from clinical visiting, laboratory tests and medical image examinations were collected. We first extracted features from unstructured data such as clinical reports and medical images. Then, models based on each single-source data or multi-source data were developed with Extreme Gradient Boosting (XGBoost) classifier to classify patients as CPP or non-CPP. Results: The best performance achieved an AUC of 0.88 and Youden index of 0.64 in the model based on multi-source data. The performance of single-source model...研究目标：本研究旨在开发简化诊断模型，用于识别中枢性性早熟（central precocious puberty, CPP）女性患者，无需使用该疾病诊断的金标准——昂贵且操作繁琐的促性腺激素释放激素（gonadotropin-releasing hormone, GnRH）激发试验。 材料与方法：本研究纳入中国广州某医疗中心内，于8岁前出现第二性征且接受过促性腺激素释放激素类似物（gonadotropin-releasing hormone analog, GnRHa）激发试验的女性患者。收集其临床就诊、实验室检测及医学影像检查相关数据。研究首先从临床报告、医学影像等非结构化数据中提取特征；随后采用极限梯度提升（Extreme Gradient Boosting, XGBoost）分类器，基于单源数据或多源数据构建模型，以区分患者为CPP或非CPP。 研究结果：基于多源数据构建的模型取得最优性能，其受试者工作特征曲线下面积（Area Under Curve, AUC）为0.88，约登指数（Youden index）为0.64；单源模型的性能……