Aim: Thinking of the relationship between Multiple attributes and length of stay(LOS).This paper provide LOS level prediction method with applying a classification estimation of multi-avariables statistic,also provides suggestions of diversion management for kidney disease patients. Methods: We use three steps to finish the estimation. Firstly, Using the correlation-coefficient between the variable and LOS to find some sensitive factors; secondly,using projection pursuit clustering analysis to find the weight of each factor, then calculation the weighting value to discriminant the LOS level.thirdly, using mean test to give the multi-level classification of LOS based on the most sensitive factor. Results: We use 12547 kidney disease patients’ data in the year 2016 from a large hospital in the Department of Nephrology to applying the estimation method. The correlations results shows that the influence of variables on LOS are ranked as disease types>age>cost.The means test results demonstrate that patients with kidney disease are divided into 3 levels between 2-20 days of los. Short-term patients (los=2-9) are mainly treated with regular treatment; medium-term patients (los=10-15) are accompanied by emergency and acute attacks; long-term patients (los=16-20) need more treatments and long hospital observation period.Tested by multi-attribute multi-level discriminant model, Indicated that the kidney patients sample data can be modeled well by the builded models. Conclusion: We build the multi-variable multi-level discriminant model for LOS estimation, which is successively applied in kidney disease. The research offers a new way for LOS estimation through the Multi-variables. Key words:LOS; Multi-variable multi-level; Projection pursuit; Clustering and discriminant
Aim: Exploring the impact of nephropathy patient characteristics on length of stay (LOS) grading, proposing a path of LOS classification based on the characteristics of patients, and providing suggestions for the accurate management of shunting of patients with nephropathy patient to promote the sustainable development of the hospital. Methods: The data of inpatients from the Department of Nephrology of a large hospital in 2016 were used, including five variables: gender, age, patient type, medical insurance type, and LOS. Based on quantifying patient attribute variables, We use three steps to finish the grading. Firstly, using the factor analysis to extracte the common factors of patient characteristics. Sencendly, according to the results of factor analysis, using k-means clustering analysis to classify the patients. Finally, According to the characteristics of different types of patients and the law of LOS differences, a LOS classification path based on patient characteristics is proposed. Results: The factor analysis shows that the LOS common factor characteristics are disease characteristics, attribute characteristics and reimbursement ratio characteristics. The k-means clustering indicates that the patients are divided into 5 categories: the mean LOS in category 1 is 15.78, Patient characteristics: Mostly elderly women with the blood resuscitates patients(38.2%) or tumor recovery patients(30.3%), city medical insurance(50%);the mean LOS in category 2 is 10.5, Patient characteristics: Mostly strong men with the ordinary patients(62.5%), City medical insurance(79.2%);the mean LOS in category 3 is 7.62, Patient characteristics: Mostly young men with the other patients(99.7%), Provincial medical insurance(73.1%);the mean LOS in category 4 is 13.7, Patient characteristics: Mostly women in pre-old age with the Ward daytime patient(38.9%) or other patients(31.8%), Urban rural medical insurance(60.6%);the mean LOS in category 5 is 6.73, Patient characteristics: Mostly young men with the other patients(99.3%), Provincial medical insurance(54.4%).According to the characteristic differences among patients and the law of LOS differences, a model of patients’ LOS classification path was proposed. Conclusion: The LOS classification path based on patient characteristics can realize the pre-classification management of patients, which has practical significance for early intervention of hospital resources.
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