A case-based ensemble learning system for explainable breast cancer recurrence prediction
A case-based ensemble learning system for explainable breast cancer recurrence prediction
复制标题
基于案例的集成学习系统，用于可解释的乳腺癌复发预测
DOI：
10.1016/j.artmed.2020.101858
复制
发表时间：
2020-07-01
影响因子：
7.5
通讯作者：
Zhao, Huimin
中科院分区：
文献类型：
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作者：
Gu, Dongxiao;Su, Kaixiang;Zhao, Huimin
近年来，人工智能（AI）在医疗决策支持中的应用取得了重大进展。然而，许多基于人工智能的系统通常只向医生提供最终预测，而不解释其潜在的决策过程。在涉及乳腺癌等致命疾病的情况下，医生采用辅助预测是在冒很大的风险，因为一个错误的决定可能会对病人造成非常有害的后果。我们提出了一种将集成学习与基于案例推理相结合的辅助决策支持系统，以帮助医生提高乳腺癌复发预测的准确性。该系统对其预测提供基于案例的解释，这对医生来说更容易理解，帮助他们评估系统预测的可靠性，并做出相应的决策。我们在一个以乳腺癌复发预测为重点的案例研究中的应用和评估表明，所提出的系统不仅提供了合理准确的预测，而且得到了肿瘤学家的好评。
Significant progress has been achieved in recent years in the application of artificial intelligence (AI) for medical decision support. However, many AI-based systems often only provide a final prediction to the doctor without an explanation of its underlying decision-making process. In scenarios concerning deadly diseases, such as breast cancer, a doctor adopting an auxiliary prediction is taking big risks, as a bad decision can have very harmful consequences for the patient. We propose an auxiliary decision support system that combines ensemble learning with case-based reasoning to help doctors improve the accuracy of breast cancer recurrence prediction. The system provides a case-based interpretation of its prediction, which is easier for doctors to understand, helping them assess the reliability of the system's prediction and make their decisions accordingly. Our application and evaluation in a case study focusing on breast cancer recurrence prediction shows that the proposed system not only provides reasonably accurate predictions but is also well-received by oncologists.