Research For Clinical Path Decision Based On Parallel FKM With MapReduce

Used as basic of decision-making method, application of intelligent algorithm to complete decision support has become the development trend of clinical path decision. There are many problems in the existing large deviation of clustering results, such as a large deviation auxiliary decision results and the actual truth values, lowly data processing efficiency and unable to handle large amounts of data. Therefore a clinical pathway decision method of parallel fuzzy clustering analysis method based on an algorithm FKM parallel algorithm was put forward based on MapReduce. Firstly the improved FKM algorithm based on partitioning method is adopted to analyze the medical data processing,Established discriminant standard path types according to the calculation results, calculated decision results according to the decision-making process. And then the improved FKM algorithm based on MapReduce model partitioning method is used by decomposition algorithm process, assigned separately to the MapReduce stage and the MapReduce stage independently using the parallel computing. Experiments showed that the clinical pathway decision method of the improved FKM algorithm based on partitioning method can effectively improve the calculation results fit with the actual truth values and significantly improve processing efficiency, which significantly improve the convergence of the algorithm, the efficiency and accuracy of the results. The FKM parallel algorithm based on partitioning method using the MapReduce can handle a large number of stable data. Using this algorithm can accurately Establish and update the standard of path type in a big data environment. Consequently, the clinical pathway decision method based on distinguish standard can assist doctors to make effective clinical path decisions.