医疗领域的可解释机器学习：临床研究的方法、解释与应用
Explainable Machine Learning in Healthcare: Methods, Interpretation, and Applications for Clinical Research
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中文总结 AI 辅助
本文综述了SHAP、LIME等XML方法，用心脏病数据集展示其可刻画人群与患者层面特征效应，助力ML预测转化为临床可解释输出，为医疗ML应用提供可解释性支持。
中文摘要 AI 辅助
本文对常用的可解释机器学习（XML）方法进行了结构化综述，涵盖全局和局部可解释性工具，如SHapley加性解释（SHAP）、局部可解释模型无关解释（LIME）、偏依赖图（PDP）和个体条件期望（ICE）图。针对每种方法，我们从高层级解释其底层机制，可视化代表性输出，并提供关于解释、恰当使用及局限性的结构化指导，同时使用公开的心脏病数据集进行示例说明。XML技术提供了直观的可视化和定量见解，展示预测因子如何影响模型预测。全局方法刻画了人群层面的特征效应，而局部方法揭示了对个体化解释有用的患者层面贡献。我们的示例表明，XML输出可识别非线性关系、检测交互效应并揭示患者间预测风险的异质性，解决了将ML预测转化为临床研究可解释输出的关键挑战。XML工具为ML模型提供了有价值的可解释性，支持临床研究中更透明、负责任的ML应用。通过提供基于方法学的概述，以及每种方法优势与局限性的实际实现示例和结构化指导，本入门指南有助于弥合高级ML方法与临床适用性之间的差距。合理采用XML方法可促进医疗保健研究中对ML预测的更好理解、沟通和批判性评估，最终支持循证临床决策。
英文摘要
We present a structured review of commonly used Explainable machine learning (XML) methodologies, including global and local interpretability tools such as SHapley Additive exPlanations (SHAP), Local Interpretable Model-Agnostic Explanations (LIME), Partial Dependence Plots (PDP), and Individual Conditional Expectation (ICE) plots. For each method, we explain the underlying mechanism at a high level, visualize representative outputs, and provide structured guidance on interpretation, appropriate use, and limitations, illustrated using the publicly available Heart Disease dataset. XML techniques provided intuitive visual and quantitative insights into how predictors influence model predictions. Global methods characterized population-level feature effects, whereas local methods revealed patient-level contributions useful for individualized interpretation. Our worked examples demonstrate how XML outputs can identify nonlinear relationships, detect interaction effects, and reveal heterogeneity in predicted risk across patients, addressing key challenges in translating ML predictions into interpretable outputs for clinical research. XML tools offer valuable interpretability for ML models and support more transparent and accountable ML applications in clinical research. By providing a methodologically grounded overview alongside practical implementation examples and structured guidance on each method's strengths and limitations, this primer helps bridge the gap between advanced ML methodology and clinical applicability. Thoughtful adoption of XML approaches may facilitate better understanding, communication, and critical evaluation of ML predictions in healthcare research, ultimately supporting evidence-based clinical decision-making.
发表机构
- Madrigal Pharmaceuticals Inc（马德里加尔制药公司）
- Boston University School of Medicine（波士顿大学医学院）
- AbbVie Inc.（艾伯维公司）
- St. Elizabeth Healthcare（圣伊丽莎白医疗中心）
- Thermo Fisher Scientific（赛默飞世尔科技）
机构由 AI 辅助整理，请以论文原文为准。
