Diagnosis related group grouping study of senile cataract patients based on E-CHAID algorithm

AIM： To figure out the contributed factors of the hospitalization expenses of senile cataract patients（HECP） and build up an area-specified senile cataract diagnosis related group（DRG） of Shanghai thereby formulating the reference range of HECP and providing scientific basis for the fair use and supervision of the health care insurance fund.METHODS： The data was collected from the first page of the medical records of 22 097 hospitalized patients from tertiary hospitals in Shanghai from 2010 to 2012 whose major diagnosis were senile cataract. Firstly, we analyzed the influence factors of HECP using univariate and multivariate analysis. DRG grouping was conducted according to the exhaustive Chi-squared automatic interaction detector（E-CHAID） model, using HECP as target variable. Finally we evaluated the grouping results using non-parametric test such as Kruskal-Wallis H test, RIV, CV, etc.RESULTS： The 6 DRGs were established as well as criterion of HECP, using age, sex, type of surgery and whether complications/comorbidities occurred as the key variables of classification node of senile cataract cases.CONCLUSION： The grouping of senile cataract cases based on E-CHAID algorithm is reasonable. And the criterion of HECP based on DRG can provide a feasible way of management in the fair use and supervision of medical insurance fund.

Probabilistic fault diagnosis of clustered faults for multiprocessor systems

With the development of high-performance computing and the expansion of large-scale multiprocessor sys-tems,it is significant to study the reliability of systems.Probabilistic fault diagnosis is of practical value to the reliabilityanalysis of multiprocessor systems.In this paper,we design a linear time diagnosis algorithm with the multiprocessor sys-tem whose threshold is set to 3,where the probability that any node is correctly diagnosed in the discrete state can be cal-culated.Furthermore,we give the probabilities that all nodes of a d-regular and d-connected graph can be correctly diag-nosed in the continuous state under the Weibull fault distribution and the Chi-square fault distribution.We prove thatthey approach to 1,which implies that our diagnosis algorithm can correctly diagnose almost all nodes of the graph.

Node Grouping in System-Level Fault Diagnosis

With the popularization of network applications and multiprocessor systems, dependability of systems has drawn considerable attention. This paper presents a new technique of node grouping for system-level fault diagnosis to simplify the complexity of large system di-agnosis. The technique transforms a complicated system to a group network, where each group may consist of many nodes that are either fault-free or faulty. It is proven that the transformation leads to a unique group network to ease system diagnosis. Then it studies systematically one-step t-faults diagnosis problem based on node grouping by means of the concept of hide-pendent point sets and gives a simple sufficient and necessary condition. The paper presents a diagnosis procedure for t-diagnosable systems. Furthermore, an efficient probabilistic diagnosis algorithm for practical applications is proposed based on the belief that most of the nodes in a system are fault-free. The result of software simulation shows that the probabilistic diagnosis provides high probability of correct diagnosis and low diagnosis cost, and is suitable for systems of any kind of topology.