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

Authors:
Daniel Reinharz
Daniel Reinharz
Laval University
Canada
Christian Gagne
Christian Gagne
Université Laval

Artif Intell Med 2018 May 7;87:34-48. Epub 2018 Apr 7.

Laboratoire de vision et systèmes numériques, Département de génie électrique et de génie informatique, Université Laval, Québec, QC G1V 0A6, Canada. Electronic address:

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 Québec suffering from heart failures.

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http://dx.doi.org/10.1016/j.artmed.2018.03.004DOI Listing
May 2018
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