Liu Shixian, Liu Hailing, Fan Siyu, Song Leming, Wang Zeyu
School of Law, Guangzhou College of Commerce, Guangzhou, 511363, China.
Law School, Guangzhou University, Guangzhou, 510006, China.
Sci Rep. 2025 Aug 5;15(1):28477. doi: 10.1038/s41598-025-13720-x.
This study explores how artificial intelligence technologies can enhance the regulatory capacity for legal risks in internet healthcare based on a machine learning (ML) analytical framework and utilizes data from the health insurance portability and accountability act (HIPAA) database. The research methods include data collection and processing, construction and optimization of ML models, and the application of a risk assessment framework. Firstly, the data are sourced from the HIPAA database, encompassing various data types, such as medical records, patient personal information, and treatment costs. Secondly, to address missing values and noise in the data, preprocessing methods such as denoising, normalization, and feature extraction are employed to ensure data quality and model accuracy. Finally, in the selection of ML models, this study experiments with several common algorithms, including extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), and deep neural network (DNN). Each algorithm has its strengths and limitations depending on the specific legal risk assessment task. RF enhances classification performance by integrating multiple decision trees, while SVM achieves efficient classification by identifying the maximum margin hyperplane. DNN demonstrates strong capabilities in handling complex nonlinear relationships, and XGBoost further improves classification accuracy by optimizing decision tree models through gradient boosting. Model performance is evaluated using metrics such as accuracy, recall, precision, F1 score, and area under curve (AUC) value. The experimental results indicate that the DNN model performs excellently in terms of F1 score, accuracy, and recall, showcasing its efficiency and stability in legal risk assessment. The principal component analysis-random forest (PCA+RF) and RF models also exhibit stable performance, making them suitable for various application scenarios. In contrast, the SVM and K-Nearest Neighbor models perform relatively weaker, although they still retain some validity in certain contexts, their overall performance is inferior to deep learning and ensemble learning methods. This study not only provides effective ML tools for legal risk assessment in internet healthcare but also offers theoretical support and practical guidance for future research in this field.
本研究基于机器学习（ML）分析框架，探讨人工智能技术如何增强互联网医疗中法律风险的监管能力，并利用来自《健康保险流通与责任法案》（HIPAA）数据库的数据。研究方法包括数据收集与处理、ML模型的构建与优化以及风险评估框架的应用。首先，数据来源于HIPAA数据库，涵盖各种数据类型，如医疗记录、患者个人信息和治疗费用。其次，为解决数据中的缺失值和噪声问题，采用去噪、归一化和特征提取等预处理方法，以确保数据质量和模型准确性。最后，在ML模型的选择上，本研究对几种常见算法进行了实验，包括极端梯度提升（XGBoost）、支持向量机（SVM）、随机森林（RF）和深度神经网络（DNN）。每种算法根据具体的法律风险评估任务都有其优势和局限性。RF通过集成多个决策树来提高分类性能，而SVM通过识别最大间隔超平面实现高效分类。DNN在处理复杂非线性关系方面表现出强大能力，XGBoost则通过梯度提升优化决策树模型进一步提高分类准确性。使用准确率、召回率、精确率、F1分数和曲线下面积（AUC）值等指标评估模型性能。实验结果表明，DNN模型在F1分数、准确率和召回率方面表现出色，展示了其在法律风险评估中的效率和稳定性。主成分分析-随机森林（PCA+RF）和RF模型也表现出稳定的性能，使其适用于各种应用场景。相比之下，SVM和K近邻模型的性能相对较弱，尽管它们在某些情况下仍具有一定的有效性，但其整体性能不如深度学习和集成学习方法。本研究不仅为互联网医疗中的法律风险评估提供了有效的ML工具，还为该领域未来的研究提供了理论支持和实践指导。
