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
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example 1
[0075] Example 1: Correlation Analysis of Predictor Variables in Renal Failure Mortality Model
[0076] Model #218: DRG 682,683,684
[0077] Data source: Memorial Hermann Hospital, Texas Medical Center, USA
[0078] Number of patient samples: 2587 Discharge time 7 / 1 / 2004-6 / 30 / 2014
[0079] Number of Significant Variables Filtered: 37
[0080] Number of strongly correlated variables: 4
[0081] Statistical method: Variance Inflation Factor (VIF)
[0082]
[0083] Note: From the 37 variables that were screened out, the strong correlation test was carried 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 the mortality model for patients with renal failure
[0085] Model #218: DRG 682,683,684
[0086] Data source: Memorial Hermann 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 Filtered: 37
[0090] Statistical method: LASSO
[0091]
[0092] Note: The value below the variable represents the importance of the variable, and the higher the value, the greater the impact of the variable on the model.
example 3
[0093] Example 3: Predictor Variable Selection for a Renal Failure Mortality Model
[0094] Model #218: DRG 682,683,684
[0095] Data source: Memorial Hermann 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 Filtered: 37
[0099] Statistical method: LASSO
[0100]
[0101]
[0102] Note: Combining the model and clinical experience, the variable rhabdomyolysis, chronic liver disease and later variables were deleted; the strongly correlated variable-organ internal tube was also deleted.
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