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Patient Treatment Pathways Clustering


Ahmed Najjar, Christian Gagné and Daniel Reinharz


Abstract - Clustering electronic medical records allows discovery of information on health care practises. Entries in such medical records are usually made of a succession of diagnostics or therapeutic steps. The corresponding processes are complex and heterogeneous since they depend on medical knowledge integrating clinical guidelines, physicians 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 trajectories over these different groups. These patient trajectories 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 health failures.

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Bibtex:

@inproceedings{Najjar1125,
    author    = { Ahmed Najjar and Christian Gagné and Daniel Reinharz },
    title     = { Patient Treatment Pathways Clustering },
    booktitle = { NIPS 2015 Workshop on Machine Learning in Healthcare },
    year      = { 2015 },
    location  = { Montreal, QC, Canada }
}

Dernière modification: Nov 7 2015 11:41PM par cgagne

     
   
   

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