عنوان انگلیسی مقاله:
A patient-similarity-based model for diagnostic prediction
ترجمه فارسی عنوان مقاله:
یک مدل مبتنی بر شباهت بیمار برای پیش بینی تشخیصی
Sciencedirect - Elsevier - International Journal of Medical Informatics, 135 (2020) 104073: doi:10:1016/j:ijmedinf:2019:104073
Zheng Jiaa, Xian Zenga, Huilong Duana, Xudong Lua, Haomin Lib,*
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
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
Keywords: Patient similarity | Diagnostic prediction | Analogy reasoning | Machine learning