A two-step approach for mining patient treatment pathways in administrative healthcare databases

被引:25
作者
Najjar, Ahmed [1 ]
Reinharz, Daniel [2 ]
Girouard, Catherine [3 ]
Gagne, Christian [1 ]
机构
[1] Univ Laval, Lab Vis & Syst Numer, Dept Genie Elect & Genie Informat, Quebec City, PQ G1V 0A6, Canada
[2] Univ Laval, Dept Med Sociale & Prevent, Lab Simulat Depistage, Quebec City, PQ G1V 0A6, Canada
[3] CISSS Chaudiere Appalaches, Sect Alphonse Desjardins, Levis, PQ G6V 3Z1, Canada
基金
加拿大自然科学与工程研究理事会;
关键词
Process clustering; Process mining; Mixed variables; HMM; k-Prototypes; Healthcare databases; Medical treatment process; HIDDEN MARKOV-MODELS; MANAGEMENT;
D O I
10.1016/j.artmed.2018.03.004
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Clustering electronic medical records allows the discovery of information on healthcare practices. Entries in such medical records are usually composed of a succession of diagnostics or therapeutic steps. The corresponding processes are complex and heterogeneous since they depend on medical knowledge integrating clinical guidelines, the physician's individual experience, and patient data and conditions. To analyze such data, we are first proposing to cluster medical visits, consultations, and hospital stays into homogeneous groups, and then to construct higher-level patient treatment pathways over these different groups. These pathways are then also clustered to distill typical pathways, enabling interpretation of clusters by experts. This approach is evaluated on a real-world administrative database of elderly people in Quebec suffering from heart failures. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:34 / 48
页数:15
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