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Method for establishing disease risk adjustment model
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A technology for model establishment and disease risk, applied in the field of disease risk adjustment model establishment, which can solve problems such as falsely high cost treatment, biased judgment results, and increase model instability, so as to avoid overfitting and improve accuracy and reliability. Effect
Active Publication Date: 2017-08-01
中科厚立信息技术(成都)有限公司
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However, the calculation method based on CMI has its inherent deficiencies in evaluating medical quality, operating efficiency, and rational drug use. First, this model does not take into account the characteristics of the disease itself and other clinically relevant factors, and does not conform to medical laws; Secondly, the inflated cost of treatment itself caused by over-examination and treatment will also increase the instability of the model, resulting in biased judgment results
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example 1
[0075] Example 1: Correlation analysis of predictors in a renal failure mortality model
[0076] Model #218: DRG 682,683,684
[0077] Data source: Memorial Herman Hospital, Texas Medical Center, USA
[0078] Number of patient samples: 2587 Discharge time 7 / 1 / 2004-6 / 30 / 2014
[0079] Number of significant variables screened out: 37
[0083] Note: The strong correlation is detected from the 37 variables screened out. The value below is the VIF value, and the variables with VIF>5 are marked.
example 2
[0084] Example 2: Importance List of Predictor Variables in a Mortality Model for Patients with Renal Failure
[0085] Model #218: DRG 682,683,684
[0086] Data source: Memorial Herman Hospital, Texas Medical Center, USA
[0087] Number of patient samples: 2587 Discharge time 7 / 1 / 2004-6 / 30 / 2014
[0088] Total number of model variables: 280
[0089] Number of significant variables screened out: 37
[0090] Statistical Methods: LASSO
[0091]
[0092] Note: The value below the variable represents the importance of the variable. The higher the value, the greater the influence of the variable on the model.
example 3
[0093] Example 3: Predictor selection for a renal failure mortality model
[0094] Model #218: DRG 682,683,684
[0095] Data source: Memorial Herman Hospital, Texas Medical Center, USA
[0096] Number of patient samples: 2587 Discharge time 7 / 1 / 2004-6 / 30 / 2014
[0097] Total number of model variables: 280
[0098] Number of significant variables screened out: 37
[0099] Statistical Methods: LASSO
[0100]
[0101]
[0102] NOTE: The variables rhabdomyolysis, chronic liver disease, and later were removed in conjunction with model and clinical experience; the strongly correlated variable - intra-organ tube was also removed.
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Abstract
The invention discloses a disease risk regulation model establishment method. According to the method, for historical data of patients in a hospital or all hospitals in a region, accompanying complications of the patients during hospitalization, population characteristics of patient individuals, hospitalization state sources and the like are integrated into affecting variable factors of disease treatment, and statistic models are established according to the DRG type of a disease diagnosis related group and final treatment information of the patients respectively for performing numerical prediction and analysis on the death rate, the hospitalization period and the hospitalization medical cost of the hospital patients. According to a risk regulation model, the complications and other hospitalization information variables of the patients during hospitalization are innovatively utilized, a check and regression method in classical statistics is adopted, and modeling is carried out in combination with an LASSO method, so that over-fitting caused by too many variables is avoided, the death rate, the hospitalization period and the hospitalization cost of the patients are quantitatively predicted, and new means and way are created for medical analysis and hospital management.
Description
technical field [0001] The present invention relates to a method for establishing a disease risk adjustment model. Background technique [0002] In recent years, due to the rapid development of domestic hospital IT technology, the accumulation of original data on patients and diseases has been preliminarily completed. However, due to the lack of methodology, these data cannot be effectively refined into guiding information and decision-making basis for hospital management, resulting in absolutely no method. Most of the data can only be stored in the hospital's data warehouse, wasting resources. If we can fully learn from the successful model and excellent methodology of the US government in hospital management, and then make localized improvements, it will not only allow domestic medical management institutions to increase effective monitoring methods and means, but also promote hospitals to speed up the transformation from extensive to The pace of transformation of the ref...
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