Title: Hospital infant emergency and critical information integration based on improved fuzzy clustering

Authors: Juan Xiao; Xiaoli Liu; Jina Zhang

Addresses: College of Nursing, HeNan Medical College, ZhengZhou, 450000, China ' Department of Nursing, The Second People's Hospital of Henan Province, ZhengZhou, 450000, China ' College of Nursing, HeNan Medical College, ZhengZhou, 450000, China

Abstract: In order to solve the problems of low recall rate, high integration error and long time consumption in traditional information integration methods, a hospital infant emergency and critical information integration method based on improved fuzzy clustering is designed. Firstly, Manhattan distance is introduced to measure the degree of correlation between information, and the information feature is extracted by mutual information method. Then, the information feature missing pattern is determined, and the KNN algorithm and MisForest interpolation algorithm are used to fill the feature missing. Finally, based on the filling of feature missing, the fuzzy clustering algorithm is improved according to the membership degree of feature information, and the improved algorithm is used to realise the integration of hospital infant's emergency and critical information. The experimental results show that the proposed method has high recall rate, low integration error and short time consumption.

Keywords: improve fuzzy clustering; hospital infant; emergency and critical; information integration; KNN algorithm; MisForest interpolation algorithm.

DOI: 10.1504/IJRIS.2025.145051

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.1, pp.40 - 49

Received: 09 Mar 2023
Accepted: 27 Apr 2023
Published online: 18 Mar 2025 *

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