用深度学习+可解释性技术预测甲状腺癌复发，准确率达96%。
An Explainable AI Model for Predicting the Recurrence of Differentiated Thyroid Cancer
- 基于临床病理特征数据，构建深度学习模型预测复发
- 测试集准确率达96%，训练集达98%
- 结合LIME与敏感性分析提升决策可解释性，适合临床医生使用
甲状腺癌发病率上升，尽管分化型甲状腺癌（DTC）预后通常良好，但仍有部分患者面临复发风险。本研究采用机器学习，特别是深度学习模型，基于患者临床病理特征数据预测DTC复发，旨在实现个性化治疗。模型在训练阶段准确率达98%，测试阶段达96%。为增强可解释性，引入LIME和莫里斯敏感性分析，揭示模型决策依据。结果表明，将深度学习与可解释性技术结合，有助于快速识别复发风险，支持临床制定精准治疗方案。
原文摘要 · Abstract (English)
Thyroid carcinoma, a significant yet often controllable cancer, has seen a rise in cases, largely due to advancements in diagnostic methods. Differentiated thyroid cancer (DTC), which includes papillary and follicular varieties, is typically associated with a positive prognosis in academic circles. Nevertheless, there are still some individuals who may experience a recurrence. This study employs machine learning, particularly deep learning models, to predict the recurrence of DTC, with the goal of improving patient care through personalized treatment approaches. By analysing a dataset containing clinicopathological features of patients, the model achieved remarkable accuracy rates of 98% during training and 96% during testing. To improve the model's interpretability, we used techniques like LIME and Morris Sensitivity Analysis. These methods gave us valuable insights into how the model makes decisions. The results suggest that combining deep learning models with interpretability techniques can be extremely useful in quickly identifying the recurrence of thyroid cancer in patients. This can help in making informed therapeutic choices and customizing treatment approaches for individual patients.
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