发表机构
Department of Computer Science and Engineering; Jashore University of Science and Technology（计算机科学与工程系; 贾绍尔科学技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对病理检查推荐延迟问题，引入基于分类器链技术的系统，将其构建为多标签分类问题。收集数据应用多种算法比较模型，通过可解释人工智能技术确保模型透明度和临床可解释性，提高传统算法在诊断过程中的效率并提供准确推荐。
AI 中文摘要
准确及时的诊断对优质患者护理至关重要，但诊断检查推荐延迟和医生主观解读会阻碍有效护理。本研究引入一种病理检查推荐系统，利用患者症状在医生会诊前加速检查选择过程。将推荐任务构建为多标签分类问题，采用分类器链技术考虑检查间的依赖性。收集相关数据创建自定义数据集，应用多种机器学习算法比较模型。逻辑回归与分类器链模型总体准确率最高，为98.83%，多数投票集成模型在精确率、召回率和F1分数上表现最佳。使用可解释人工智能技术确保模型透明度和临床可解释性，模型诊断推理与既定医学知识一致，有助于医生在关键场景做出逻辑决策，表明分类器链可提高传统算法在诊断过程中的效率并提供准确检查推荐。
英文摘要
Diagnostic decision making often relies on a sequence of pathology tests that bridge patient symptoms and final disease diagnosis. Existing clinical decision-support systems typically focus on predicting single diseases and do not explicitly recommend sets of intermediate tests or model dependencies among them. In this paper, we formulate pathology test recommendation as a multi-label classification problem where each case is associated with multiple, interdependent tests. We propose an AI-based framework that applies classifier chains with logistic regression, decision trees, random forests, and their ensemble to capture label dependencies between tests. Experiments on an expert-curated dataset from a private pathology laboratory show that classifier-chain models outperform their independent counterparts, improving F1-score and reducing Hamming loss while maintaining high accuracy across common and rare tests. To enhance trust and transparency, we integrate SHAP-based explainable AI, providing symptom-level attributions that align with established clinical reasoning in most cases. The results demonstrate that classifier chains combined with SHAP offer an effective and interpretable approach for multi-label pathology test recommendation, with potential to support clinicians in selecting appropriate diagnostic tests at an early stage.
