WO2021082444A1 - 用于大规模脑病历分割的多粒度Spark超信任模糊方法 - Google Patents
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- the medical health service big data project not only requires the construction of electronic health records and electronic medical records databases, but also a medical health management and service big data application system covering public health, medical services, medical security, drug supply, family planning and integrated management services.
- a medical health management and service big data application system covering public health, medical services, medical security, drug supply, family planning and integrated management services.
- we need to make full use of various information technologies such as big data, cloud computing and mobile Internet to promote the effective interoperability of electronic medical record databases and electronic health record databases, and Realize benign interaction to implement the big data project of medical and health services.
- the present invention discloses a multi-granular Spark super-trust fuzzy method for large-scale brain medical record segmentation.
- the large-scale brain medical record data attribute set is divided into different multi-granular evolutionary subpopulations Granu-population i on the Spark cloud platform; Design a super-trust model based on multi-granularity Spark to build trust among different super elites in multi-granularity populations; adjust multi-granularity center threshold, use multi-granularity sub-population balance adjustment strategy for super elites to dynamically update, and large-scale brain disease records
- the invention can stably segment a large-scale brain disease history knowledge reduction collection, and provide an important diagnosis basis for the intelligent diagnosis and auxiliary treatment of brain diseases.
- step B the specific steps of step B are as follows:
- the population trust between the h-th multi-granularity population and the u-th multi-granularity population center is Calculated as follows:
- ⁇ is the similarity threshold, and the range is ⁇ [0,1], then the multi-granularity population conforms to the subpopulation trust relationship in different granular spaces;
- ⁇ is the confidence factor of the direct trust between super elites.
- the value of ⁇ is related to the number of super elite interactions. The greater the number of interactions, the greater the value of ⁇ , 0 ⁇ 1.
- the size of the large-scale brain disease record attribute set is determined by different granularity spaces.
- the neutron population trust relationship is dynamically updated iteratively.
- step C A further improvement of the present invention lies in: the specific steps of step C are as follows:
- the distance between the particle size center c 1 and the initial particle size center c 0 after the first iteration of the particle size subpopulation is d(c 1 , c 0 ), and the new particle size center c′ and the original particle size center after the i-th iteration
- step E the specific steps of the step E are as follows:
- the global optimal consensus probability of obtaining all super elites is t ⁇ 1,2,...,s ⁇ , construct the optimal consistent equilibrium degree and probability degree pair of large-scale brain disease record attribute segmentation as t ⁇ 1,2,...,s ⁇ ;
- the present invention constructs a multi-granularity population super-elite dynamic cooperative operation mechanism on the Spark cloud platform based on the dynamic elite dominant area, and achieves the optimal and consistent balance of large-scale brain medical record segmentation, and reduces the complexity cost of large-scale brain medical record feature segmentation. It further improves the granularity and robustness of large-scale parallel feature extraction of brain medical records on the cloud computing Spark cloud platform, and lays a good foundation for the development of intelligent services such as brain medical record feature selection, rule mining, and clinical decision support.
- Figure 1 is the overall flow chart of the system
- Figure 3-5 is a diagram of the dynamic fuzzy collaborative operation process of multi-granularity population super elites
- n is the total number of elites
- SP i is the i-th super elite
- P ij is the j-th ordinary elite in the i-th multi-granularity population
- Re ij is the credibility of the i-th super elite to the j-th super elite
- R mj is the partial trust recommended by the m-th ordinary elite in the population to the j-th super elite
- I(j) is the The set of all elites in j multi-granularity populations GP j ,
- is the potential of the set;
- the population trust between the h-th multi-granularity population and the u-th multi-granularity population center is Calculated as follows:
- ⁇ is the similarity threshold, and the range is ⁇ [0,1], then the multi-granularity population conforms to the subpopulation trust relationship in different granular spaces;
- ⁇ is the confidence factor of the direct trust between super elites.
- the value of ⁇ is related to the number of super elite interactions. The greater the number of interactions, the greater the value of ⁇ , 0 ⁇ 1.
- the size of the large-scale brain disease record attribute set is determined by different granularity spaces.
- the neutron population trust relationship is dynamically updated iteratively.
- the distance between the particle size center c 1 and the initial particle size center c 0 after the first iteration of the particle size subpopulation is d(c 1 , c 0 ), and the new particle size center c′ and the original particle size center after the i-th iteration
- the invention adopts a multi-granular Spark super trust model to construct trust between different super elites in a multi-granular population, uses different multi-granular sub-population balance adjustment strategies for super elites to dynamically update, and performs global search and segmentation of large-scale brain disease records With local refined segmentation, super elites can collaboratively extract knowledge reduction subsets in their respective regions, which greatly reduces the execution time and improves the accuracy of large-scale brain medical record segmentation.
- the present invention constructs a multi-granularity population super-elite dynamic cooperative operation mechanism on the Spark cloud platform based on the dynamic elite dominant area, achieves the optimal and consistent balance of large-scale brain disease record segmentation, reduces the complexity cost of large-scale brain disease record feature segmentation, and further improves
- the fine-grained and robustness of large-scale parallel feature extraction of brain medical records on the cloud computing Spark cloud platform has laid a good foundation for the development of intelligent services such as brain medical record feature selection, rule mining, and clinical decision support.
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Abstract
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Claims (4)
- 用于大规模脑病历分割的多粒度Spark超信任模糊方法,其特征在于:具体步骤如下:A.在大数据Spark云平台上将大规模脑病历属性集分割至不同的多粒度进化种群Granu-population i,i=1,2,…n,脑病历属性分割任务分解为多个并行化的作业任务,然后在分解后的多个作业任务中计算出不同脑病历候选属性集的等价类;B.设计基于多粒度超信任模型,将第i个多粒度进化种群Granu-population i用于脑病历第i个属性集的约简和分割处理,构建多粒度种群内不同超级精英之间信任度,计算多粒度种群的信任偏差,大规模脑病历属性集的规模大小通过不同粒度空间中子种群信任度关系进行动态迭代更新;C.设置用于大规模脑病历分割的多粒度Spark超信任中心调整阈值为λ,在第i次迭代完成后,将粒度中心调整量大于阈值λ的多粒度子种群Granu-population i进行下一次迭代调整,设置粒度中心迁移的调整阈值为ε和多粒度子种群数目调整阈值为θ,优化多粒度V tj的中心c tj,并添加到最终多粒度种群中心集合中,形成包含k个多粒度中心集合;D.对多粒度子种群中超级精英使用均衡调整策略动态更新,将多粒度子种群超级精英划分到一个等腰直角三角形内容,分别计算各自的粒度值 如果两个超级精英具有相同较低粒度 则他们的近似度属性值收敛于均衡对为 如果两个超级精英具有相同较高粒度 则他们的近似度属性值收敛于均衡对为 该均衡调整策略有利于增加多粒度子种群最优一致均衡度。E.构建多粒度子种群超级精英动态模糊协同分割策略,在动态精英优势区域内对大规模脑病历属性进行全局搜索分割与局部精化分割,在多粒度子种群中执行竞争和合作的混合协同,构建大规模脑病历属性分割最优一致均衡度和概率度,使超级精英在各自对应的Pareto优势区域内协同提取知识约简子集,并能稳定分割大规模脑病历不同的属性区域,求得大规模脑病历最优特征集F.比较上述求出的大规模脑病历分割精度RC与预先设定精度值η关系,若满足RC≥η,则输出大规模脑病历最优分割知识集。否则,继续执行上述C、D和E步骤,直至大规模脑病历分割精度满足RC≥η;
- 根据权利要求1所述一种用于大规模脑病历分割的多粒度Spark超信任模糊方法,其特征在于:所述步骤B的具体步骤如下:a.设置多粒度种群个数为n,且n≥2,初始化多粒度种群为GP h且h∈{1,...,n};d.在相同粒度子种群中第i个超级精英的信任度定义如下:其中n是精英总数,SP i为第i个超级精英,P ij为在第i个多粒度种群中第j个普通精英;f.设多粒度种群中心 之间相似度在当前的循环次数为t,t∈{2,...,n-1},每一个多粒度种群中心 的信任度由上一轮第t-1次迭代计算出来,这样大规模脑病历属性集的规模大小将通过不同粒度空间中子种群信任度关系进行动态迭代更新;g.计算多粒度种群中不同超级精英SP i和SP j信任度间的信任偏差Diff ij,计算公式为式中Re ij为第i个超级精英对第j个超级精英的信誉度,R mj为种群中任选第m个普通精英对第j个超级精英推荐的局部信任度,I(j)为第j个多粒度种群GP j中所有精英集合,|I(j)|为该集合的势;g.构建多粒度种群内不同超级精英之间信任度关系公式,定义为其中λ是超级精英之间直接信任度的信心因子,λ的取值和超级精英交互的数目有关,交互的数目越多则λ取值越大,0≤λ≤1。我们取λ=h/H Lmt,其中h为超级精英i和超级精英j之间交互的数目,H Lmt为设定的交互数目门限值,大规模脑病历属性集的规模大小通过不同粒度空间中子种群信任度关系进行动态迭代更新。
- 根据权利要求1所述一种用于大规模脑病历分割的多粒度Spark超信任模糊方法,其特征在于:所述步骤C的具体步骤如下:b.设多粒度子种群集和中心都为空集,V=Φ和C=Φ,迭代次数t=1。计算每个多粒度子种群与多粒度中心的距离,按最小距离原则将大规模脑病历属性集划分到相应的多粒度中心,形成k个 并记录各中心中超级精英个数 设置初始的调整标号d.粒度子种群在第一次迭代后粒度中心c 1与初始粒度中心c 0之间距离为d(c 1,c 0),在第i次迭代后新的粒度中心c′与原粒度中心c之间距离d(c,c′),如果 ε为相似度阈值,范围为ε∈[0,1],则以c′为代表的粒度中心不再参与下轮迭代调整,否则继续进行迭代调整;e.计算标号f tj=1的多粒度种群中每个超级精英与参与调整多粒度种群中心的距离,按最小距离原则将脑病历属性划分到相应的多粒度种群,形成k个新多粒度种群{V tj},并记录各多粒度种群中超级精英个数{N tj},求出调整后用于大规模脑病历属性分割的超级精英个数ΔN tj;
- 根据权利要求1所述一种用于大规模脑病历分割的多粒度Spark超信任模糊方法,其特征在于:所述步骤E的具体步骤如下:c.在多粒度子种群中执行竞争和合作的混合协同的大规模脑病历分割,假设S i为第i个超级精英,在i=1至|S i|执行如下操作:(1)***S i超级精英的代表S i,rep到P i t中;(2)如果n x>|S i|,从多粒度子种群Granu-subpopulation i中选择超级精英P i t;(3)将所有的S i,j和其他多粒度子种群Granu-subpopulation i的解进行组合,将其进行排序值和计算出S i,j的小生成境数;(4)更新S i的超级精英代表取得Pareto优势区域内非优势解,决定获胜的多粒度子种群,并更新S i=S k;d.超级精英的模糊成员度 uCh(P i)采用相似成员方式计算,其中参考值P i和超级精英中心C h之间的距离定义为d(P i,C h);
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