Acupoint Selection Standards of Auricular Therapy in the Treatment of Maintenance Hemodialysis Insomnia Based on Data Mining

Background:Insomnia is a high-incidence complication in patients undergoing maintenance hemodialysis(MHD).Auricular therapy can effectively improve sleep with few adverse effects.Acupoint selection affects the impact of auricular therapy.However,there is currently a lack of analysis on the standards of acupoint selection.Our study used data mining technology to investigate the acupoint selection principles and characteristics of auricular therapy for the treatment of MHD-related insomnia.Objective:The objective of the study is to explore the standards of acupoint selection in auricular therapy for the treatment of MHD-related insomnia through data mining technology.Materials and Methods:We searched three English(PubMed,WOS,and Embase)and four Chinese(CNKI,VIP,Wangfang,and CBM)databases for studies on auricular therapy for MHD-related insomnia from self-establishment to November 14,2022.Results:Eighty-one publications were involved,which included 33 acupoints.The most common auricular points in patients with MHD-related insomnia were the Shenmen,heart,and kidney points.More applications involved the visceral,nervous system,and specific acupoints.Five effective clusters and two clusters were obtained through cluster analysis,including specific auricular points for insomnia,such as the multi-dream area,neurasthenia area,deep sleep point,and anterior ear lobe.Complex network analysis showed that the core auricular acupoint combinations for the intervention of MHD-related insomnia were Shenmen with kidney,Shenmen with heart,heart with kidney,heart with Shenmen,and heart and Shenmen with subcortex.Conclusions:The selection of auricular points for the treatment of MHD-related insomnia was guided by the heart theory of traditional Chinese medicine.Clinical treatment attaches importance to the use of the multi-dream area,neurasthenia area,and other acupoints.

A Usable Selection Range Standard Based on Test Suite Reduction Algorithms

In this paper, we analyze the features and distinctions of 6 classical algorithms： greedy algorithm （G）, greedy evolution algorithm （GE）, heuristics algorithm （H）, greedy heuristic G （GRE）, integer linear programming algorithm （ILP） and genetic algorithm （GA） to ensure the main influencing factors-the performance of algorithms and the running time of algorithms. What＇s more, we would not only present a research design that aims at gaining deeper understanding about the algorithm classification and its function as well as their distinction, but also make an empirical study in order to obtain a practical range standard that can guide the selection of reduction algorithms. When the size of a test object （product of test requirements and test cases） is smaller than 2000×2000, G algorithm is the commonly recommended algorithm. With the growth of test size, the usage of GE and GRE becomes more general.