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.

近年来，人工智能（AI）在医疗决策支持方面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，而没有解释其潜在的决策过程。在涉及致命疾病（例如乳腺癌）的情况下，采用辅助预测的医生会冒很大风险，因为错误的决定可能会给患者带来非常有害的后果。我们提出了一种辅助决策支持系统，将集成学习与基于案例的推理相结合，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了基于案例的解释，更容易让医生理解，帮助他们评估系统预测的可靠性并做出相应的决策。我们在专注于乳腺癌复发预测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相当准确的预测，而且还受到了肿瘤学家的好评。