Research Center of Electrical and Information Technology, Seoul National University of Science and Technology, Seoul 01811, Korea.
Department of Industrial Engineering, Yonsei University, Seoul 03722, Korea.
Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, Seoul 01811, Korea.
患者相似性研究是医疗保健中最基本的任务之一，有助于做出决策，而不会在临床实践中产生额外的时间和成本。患者相似性还可以应用于各种医学领域，例如队列分析和个性化治疗建议。由于这一重要性，正在积极进行患者相似性测量研究。然而，医学数据具有复杂、不规则和连续的特征，使得测量相似性具有挑战性。因此，测量准确的相似度是一个重要的问题。现有的相似性测量研究使用监督学习来计算患者之间的相似性，而相似性测量研究仅针对一种特定疾病进行。但是，只考虑一种疾病是不现实的，因为通常伴随着其他条件；需要一项研究来衡量与多种疾病的相似性。本研究提出了一种基于卷积神经网络的模型，该模型联合特征学习和相似性学习来定义多种疾病患者的相似性。我们使用来自韩国国民健康保险共享服务的队列数据进行实验。实验结果证明，与其他用于测量多病患者相似性的现有模型相比，所提出的模型具有出色的性能。我们使用来自韩国国民健康保险共享服务的队列数据进行实验。实验结果证明，与其他用于测量多病患者相似性的现有模型相比，所提出的模型具有出色的性能。我们使用来自韩国国民健康保险共享服务的队列数据进行实验。实验结果证明，与其他用于测量多病患者相似性的现有模型相比，所提出的模型具有出色的性能。
Patient similarity research is one of the most fundamental tasks in healthcare, helping to make decisions without incurring additional time and costs in clinical practices. Patient similarity can also apply to various medical fields, such as cohort analysis and personalized treatment recommendations. Because of this importance, patient similarity measurement studies are actively being conducted. However, medical data have complex, irregular, and sequential characteristics, making it challenging to measure similarity. Therefore, measuring accurate similarity is a significant problem. Existing similarity measurement studies use supervised learning to calculate the similarity between patients, with similarity measurement studies conducted only on one specific disease. However, it is not realistic to consider only one kind of disease, because other conditions usually accompany it; a study to measure similarity with multiple diseases is needed. This research proposes a convolution neural network-based model that jointly combines feature learning and similarity learning to define similarity in patients with multiple diseases. We used the cohort data from the National Health Insurance Sharing Service of Korea for the experiment. Experimental results verify that the proposed model has outstanding performance when compared to other existing models for measuring multiple-disease patient similarity.
