OBJECTIVE To simulate the clinical reasoning of doctors, retrieve analogous patients of an index patient automatically and predict diagnoses by the similar/dissimilar patients. METHODS We proposed a novel patient-similarity-based framework for diagnostic prediction, which is inspired by the structure-mapping theory about analogy reasoning in psychology. Patient similarity is defined as the similarity between two patients' diagnoses sets rather than a dichotomous (absence/presence of just one disease). The multilabel classification problem is converted to a single-value regression problem by integrating the pairwise patients' clinical features into a vector and taking the vector as the input and the patient similarity as the output. In contrast to the common k-NN method which only considering the nearest neighbors, we not only utilize similar patients (positive analogy) to generate diagnostic hypotheses, but also utilize dissimilar patients (negative analogy) are used to reject diagnostic hypotheses. RESULTS The patient-similarity-based models perform better than the one-vs-all baseline and traditional k-NN methods. The f-1 score of positive-analogy-based prediction is 0.698, significantly higher than the scores of baselines ranging from 0.368 to 0.661. It increases to 0.703 when the negative analogy method is applied to modify the prediction results of positive analogy. The performance of this method is highly promising for larger datasets. CONCLUSION The patient-similarity-based model provides diagnostic decision support that is more accurate, generalizable, and interpretable than those of previous methods and is based on heterogeneous and incomplete data. The model also serves as a new application for the use of clinical big data through artificial intelligence technology.

中文翻译：

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基于患者相似度的诊断预测模型。

目的为了模拟医生的临床推理，自动检索索引患者的相似患者并预测相似/相似患者的诊断。方法我们提出了一种新颖的基于患者相似度的诊断预测框架，该框架受到心理学中类比推理的结构映射理论的启发。患者相似度定义为两个患者的诊断集之间的相似度，而不是二分法（仅一种疾病的存在/存在）。通过将成对患者的临床特征整合到向量中，并将向量作为输入，将患者相似度作为输出，将多标签分类问题转换为单值回归问题。与仅考虑最近邻居的普通k-NN方法相反，我们不仅利用相似的患者（正面类比）来生成诊断假设，而且利用不同的患者（负面类比）来拒绝诊断假设。结果基于患者相似度的模型的性能优于所有基线和传统的k-NN方法。基于阳性相似度的预测的f-1分数为0.698，显着高于基线分数从0.368到0.661的分数。当应用否定类比方法修改肯定类比的预测结果时，该值增加到0.703。对于较大的数据集，此方法的性能非常有前途。结论基于患者相似度的模型可提供更准确，可概括，并且比以前的方法可解释，并且基于异类和不完整的数据。该模型还可以作为通过人工智能技术使用临床大数据的新应用程序。