Hypertension medication recommendation model based on recursive partitioning calculation and construction method thereof
A construction method and high blood pressure technology, applied in the field of medicine, can solve the problems of increasing adverse events, affecting the treatment effect of patients, ignoring the difference in curative effect and adverse reactions, etc., and achieving the effect of rapid selection
- Summary
- Abstract
- Description
- Claims
- Application Information
AI Technical Summary
Problems solved by technology
Method used
Image
Examples
Embodiment 1
[0055] Passed the ICD-10 diagnosis code of hypertension (I10.X00, I10.X01, I10.X02, I10.X03, I10.X04, I10.X05, I10.X06, I11.900, I12.903, I15.000) Matching is automatically obtained through the data analysis set, and the data structure of the data analysis set can be seen in Table 1.
[0056]
[0057]
[0058] Table 1 The model building analysis data set obtained by grabbing the ICD diagnostic code
[0059] The data is automatically divided into a test set and a validation set by a stratified sampling function, wherein the test set contains 148 samples, and the validation set contains 63 samples; for the test data set, the two regimens of single drug and combined drug are used as dependent variables. Age, gender, body mass index (BMI index), height, weight, and whether the diagnosis is accompanied by (diabetes, chronic kidney disease, atherosclerosis or cerebral infarction) are used to select nodes for classification, and the BMI index is obtained by height and weight ,...
Embodiment 2
[0064] The original data set is automatically divided into a test set and a validation set by a stratified sampling function. The validation set contains 63 samples, including 39 samples of the single drug regimen and 24 samples of the combined drug regimen group; whether the samples are elderly patients, Whether it is accompanied by diabetes, whether it is accompanied by atherosclerosis, whether it is accompanied by chronic kidney disease, BMI index and gender, six parameters are used to automatically judge a hypertension drug recommendation model, and realize the prediction of the patient's medication plan. As a result, 16 patients were predicted to be in the combined drug group, and 47 patients were predicted to be in the single drug group (Table 3), and the model judgment error probability was only 22%.
[0065]
[0066]
[0067] Table 3 Comparison between the prediction results of the hypertension drug recommendation model and the actual results
Embodiment 3
[0069] By the method of the present invention, select corresponding suitable 2-3 items variable among a plurality of classification variables, draw the following concrete medication recommendation example, specific as Figure 4 shown.
[0070] 1: The newly diagnosed hypertensive patient A is 75 years old and does not have diabetes. He can be classified into category 1 according to the hypertension drug recommendation model, and combined medication is recommended.
[0071] 2: The newly diagnosed hypertensive patient B is 35 years old and has diabetes, and can be classified into category 2 according to the hypertension drug recommendation model, and combined medication is recommended.
[0072] 3: Newly diagnosed hypertensive patient B is 42 years old and does not have diabetes, but he is found to have complications of hyperlipidemia, he can be classified into category 2 according to the hypertension drug recommendation model, and combined medication is recommended.
[0073] 4: ...
PUM
Abstract
Description
Claims
Application Information
- R&D Engineer
- R&D Manager
- IP Professional
- Industry Leading Data Capabilities
- Powerful AI technology
- Patent DNA Extraction
Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.
© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com