A Review on the Patient Similarity Analysis Based on Electronic Medical Records
Jia Zheng1, Zong Ruijie2, Duan Huilong1, Li Haomin3,4*
1 College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou 310027, China 2 Qingdao Blood Center, Qingdao 266071, Shandong, China 3 The Children’s Hospital, Zhejiang University School of Medicine, Hangzhou 310052, China 4 The Institute of Translational Medicine, Zhejiang University, Hangzhou, 310029, China
Abstract：Predicting the future health status of a patient has important social and scientific value. At the same time, the accumulating big data in health care domain provides a new basis for obtaining predictive models or establishing predictive methods through medical big data analysis. The patient similarity analysis that provides a general-purpose computer assistant clinical decision support framework based on the predictive knowledge mining from the large practice clinical data generated by a mount of routine patients using the patient distance assessment has paved a way to personalized medicine. Up to date, this method that had been initially approved in many medical domains such as cancer, endocrine diseases and heart diseases, has become a very important direction in clinical translation of artificial intelligence technology. In this paper,the theoretical basis and research progress of the patient similarity analysis were reviewed through introducing the common structure of the patient similarity calculation framework and corresponding key technologies in different processes, such as data preprocessing, dimension reduction, measuring distance of different concepts and generation of similarity. At the same time, existing problems and challenges faced by the patient similarity analysis were proposed.
贾峥,宗瑞杰,段会龙,李昊旻. 基于电子病历的患者相似性分析综述[J]. 中国生物医学工程学报, 2018, 37(3): 353-366.
Jia Zheng, Zong Ruijie, Duan Huilong, Li Haomin. A Review on the Patient Similarity Analysis Based on Electronic Medical Records. Chinese Journal of Biomedical Engineering, 2018, 37(3): 353-366.
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