Disease risk regulation model establishment method

A technology for model establishment and disease risk, applied in the field of disease risk adjustment model establishment, which can solve the problems of artificially high cost treatment, biased judgment results, failure to consider disease characteristics and other clinically relevant influencing factors, and avoid overfitting. , the effect of improving accuracy and reliability

Active Publication Date: 2015-10-21
中科厚立信息技术(成都)有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

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 oth

Method used

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  • Disease risk regulation model establishment method

Examples

Experimental program
Comparison scheme
Effect test

Example

[0075] Example 1: Correlation analysis of predictive variables in the mortality model of renal failure

[0076] Model #218: DRG 682,683,684

[0077] Data source: Herman Memorial Hospital, Texas Medical Center

[0078] Number of patient samples: 2587 Discharge time 7 / 1 / 2004-6 / 30 / 2014

[0079] Number of significant variables selected: 37

[0080] Number of strongly correlated variables: 4

[0081] Statistical method: Variance Inflation Factor (VIF)

[0082]

[0083] Note: The strong correlation test is performed from the 37 variables selected. The lower value is the VIF value, and the variables with VIF>5 are marked.

Example

[0084] Example 2: Importance list of predictors of mortality model for patients with renal failure

[0085] Model #218: DRG 682,683,684

[0086] Data source: Herman Memorial Hospital, Texas Medical Center

[0087] Number of patient samples: 2587 Discharge time 7 / 1 / 2004-6 / 30 / 2014

[0088] Total model variables: 280

[0089] Number of significant variables selected: 37

[0090] Statistical method: 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

[0093] Example 3: Selection of predictors for the mortality model of renal failure

[0094] Model #218: DRG 682,683,684

[0095] Data source: Herman Memorial Hospital, Texas Medical Center

[0096] Number of patient samples: 2587 Discharge time 7 / 1 / 2004-6 / 30 / 2014

[0097] Total model variables: 280

[0098] Number of significant variables selected: 37

[0099] Statistical method: LASSO

[0100]

[0101]

[0102] Note: Combining the model and clinical experience, the variables rhabdomyolysis, chronic liver disease and later variables were deleted; the strongly correlated variable-organ internal ducts were also deleted.

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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 invention relates to a method for establishing a disease risk adjustment model. Background technique [0002] In recent years, due to the rapid development of IT technology in domestic hospitals, the accumulation of raw data of patients and diseases has been preliminarily completed. Most of the data can only be stored in the hospital's data warehouse, wasting resources. If we can fully learn from the US government's successful model and excellent methodology of hospital management, and then make localized improvements, it will not only allow domestic medical management agencies to increase effective monitoring methods and means, but also promote hospitals to accelerate the transition from extensive to The pace of refined management model transformation. [0003] Recently, the government actively advocates and encourages the transformation of traditional industries to "Internet +", making full use of digital technology to improve the medical quali...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 杨思坦李涛陶金蓝陈霞
Owner 中科厚立信息技术(成都)有限公司
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