Objective To extract the classification elements of indexes such as structure, process and outcome of pre-hospital emergency treatment for elderly patients with osteoporotic fracture based on medical big data, and to analyze their related characteristics by applying modern mathematical models for interdisciplinary classification optimization research.
Methods The literature review method was used to search for 20 relevant literatures on pre-hospital emergency treatment for elderly patients with osteoporosis fracture, and the iterative clustering analysis (K-MEANS algorithm) algorithm was used to classify and optimize the pre-hospital emergency treatment indicators.
Results Through matrix analysis, it was confirmed that the weights of the dimensions in the primary indicators of structural indicators, process indicators and outcome indicators were 0.332 4, 0.139 5 and 0.527 6, respectively; the consistency results (CR) of the matrix were all smaller than 0.1. Among them, the CR values of structure, process and outcome indicators were 0.034 5, 0.039 4 and 0.039 5, which met the requirements of the test.
Conclusion Applications of the K-MEANS algorithm and the DeepFM predictive model are beneficial for predication, qualification and quantification of the complex relationship between classification indicators for pre-hospital emergency care in elderly patients with osteoporotic fracture based on medical big data, which can provide necessary conditions for optimizing the interdisciplinary classification and implementation of various clinical foundational work for this disease.
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