Healthcare trajectory mining by combining multidimensional component and itemsets
Sequential pattern mining is aimed at extracting correlations among temporal data. Many different methods were proposed to either enumerate sequences of set valued data (i.e., itemsets) or sequences containing multidimensional items. However, in real-world scenarios, data sequences are described as events of both multidimensional items and set valued information. These rich heterogeneous descriptions cannot be exploited by traditional approaches. For example, in healthcare domain, hospitalizations are defined as sequences of multi-dimensional attributes (e.g. Hospital or Diagnosis) associated with two sets, set of medical procedures (e.g. $ \lbrace $ Radiography, Appendectomy $\rbrace$) and…
A data mining approach for grouping and analyzing trajectories of care using claim data: the example of breast cancer
International audience; BACKGROUND: With the increasing burden of chronic diseases, analyzing and understanding trajectories of care is essential for efficient planning and fair allocation of resources. We propose an approach based on mining claim data to support the exploration of trajectories of care. METHODS: A clustering of trajectories of care for breast cancer was performed with Formal Concept Analysis. We exported Data from the French national casemix system, covering all inpatient admissions in the country. Patients admitted for breast cancer surgery in 2009 were selected and their trajectory of care was recomposed with all hospitalizations occuring within one year after surgery. Th…