医学人工智能工具箱（MAIT）：用于二分类、生存建模和回归分析的可解释机器学习框架
Medical artificial intelligence toolbox (MAIT): an explainable machine learning framework for binary classification, survival modelling, and regression analyses
AI总结：
本文提出开源可解释 Python 框架 MAIT，支持表格医学数据的二分类、回归与生存建模，并通过自动化流程、SHAP 解释、阈值优化和半监督删失处理提升模型开发、部署与解释。
摘要：
尽管机器学习提供了适合探索各类医学研究问题的多种技术，但一个具有凝聚力和协同性的框架能够促进新方法在统一模型开发与解释中的整合和理解。因此，我们推出医学人工智能工具箱（Medical Artificial Intelligence Toolbox，MAIT），这是一个可解释的开源 Python 流水线，用于在表格数据集上开发和评估二分类、回归及生存模型。MAIT 应对关键挑战（例如高维度、类别不平衡、混合变量类型和缺失），同时促进报告透明度（符合 TRIPOD+AI）。MAIT 为初学者提供自动化配置，并为专家提供可定制源代码，简化了两个主要用例：发现（通过统一评分进行特征重要性分析，例如 SHapley Additive exPlanations - SHAP）和预测（利用优化解决方案进行模型开发与部署）。此外，MAIT 提出了新技术，包括二分类中概率阈值的微调、累积风险曲线到二分类的转换、针对混合数据类型的增强型模型解释可视化，以及通过半监督学习处理删失，以适应广泛的数据约束和研究设计。我们在 GitHub 上提供了使用四个开放获取数据集的详细教程，以展示如何使用 MAIT 改进医学研究中机器学习模型的实施和解释。
英文摘要：
While machine learning offers diverse techniques suitable for exploring various medical research questions, a cohesive synergistic framework can facilitate the integration and understanding of new approaches within unified model development and interpretation. We therefore introduce the Medical Artificial Intelligence Toolbox (MAIT), an explainable, open-source Python pipeline for developing and evaluating binary classification, regression, and survival models on tabular datasets. MAIT addresses key challenges (e.g., high dimensionality, class imbalance, mixed variable types, and missingness) while promoting transparency in reporting (TRIPOD+AI compliant). Offering automated configurations for beginners and customizable source code for experts, MAIT streamlines two primary use cases: Discovery (feature importance via unified scoring, e.g., SHapley Additive exPlanations - SHAP) and Prediction (model development and deployment with optimized solutions). Moreover, MAIT proposes new techniques including fine-tuning of probability threshold in binary classification, translation of cumulative hazard curves to binary classification, enhanced visualizations for model interpretation for mixed data types, and handling censoring through semi-supervised learning, to adapt to a wide set of data constraints and study designs. We provide detailed tutorials on GitHub, using four open-access data sets, to demonstrate how MAIT can be used to improve implementation and interpretation of ML models in medical research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
