CN115050449A - Theophylline guiding medication device based on weighted regression - Google Patents
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- ZFXYFBGIUFBOJW-UHFFFAOYSA-N theophylline Chemical compound O=C1N(C)C(=O)N(C)C2=C1NC=N2 ZFXYFBGIUFBOJW-UHFFFAOYSA-N 0.000 title claims abstract description 70
- 229960000278 theophylline Drugs 0.000 title claims abstract description 35
- 239000003814 drug Substances 0.000 title claims abstract description 29
- 229940079593 drug Drugs 0.000 title claims abstract description 27
- 238000012549 training Methods 0.000 claims abstract description 38
- 238000012360 testing method Methods 0.000 claims abstract description 34
- 210000002966 serum Anatomy 0.000 claims abstract description 10
- 238000012417 linear regression Methods 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000010276 construction Methods 0.000 claims 1
- 208000006673 asthma Diseases 0.000 description 3
- 230000001269 cardiogenic effect Effects 0.000 description 3
- 238000000034 method Methods 0.000 description 3
- 210000002460 smooth muscle Anatomy 0.000 description 3
- BHPQYMZQTOCNFJ-UHFFFAOYSA-N Calcium cation Chemical compound [Ca+2] BHPQYMZQTOCNFJ-UHFFFAOYSA-N 0.000 description 2
- 229910001424 calcium ion Inorganic materials 0.000 description 2
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- SFLSHLFXELFNJZ-QMMMGPOBSA-N (-)-norepinephrine Chemical compound NC[C@H](O)C1=CC=C(O)C(O)=C1 SFLSHLFXELFNJZ-QMMMGPOBSA-N 0.000 description 1
- UCTWMZQNUQWSLP-VIFPVBQESA-N (R)-adrenaline Chemical compound CNC[C@H](O)C1=CC=C(O)C(O)=C1 UCTWMZQNUQWSLP-VIFPVBQESA-N 0.000 description 1
- 229930182837 (R)-adrenaline Natural products 0.000 description 1
- 206010007522 Cardiac asthma Diseases 0.000 description 1
- 208000004327 Paroxysmal Dyspnea Diseases 0.000 description 1
- 208000005374 Poisoning Diseases 0.000 description 1
- 210000005091 airway smooth muscle Anatomy 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 208000023819 chronic asthma Diseases 0.000 description 1
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- 229960005139 epinephrine Drugs 0.000 description 1
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- 230000002427 irreversible effect Effects 0.000 description 1
- 229960002748 norepinephrine Drugs 0.000 description 1
- SFLSHLFXELFNJZ-UHFFFAOYSA-N norepinephrine Natural products NCC(O)C1=CC=C(O)C(O)=C1 SFLSHLFXELFNJZ-UHFFFAOYSA-N 0.000 description 1
- 238000013488 ordinary least square regression Methods 0.000 description 1
- 231100000572 poisoning Toxicity 0.000 description 1
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- 230000001225 therapeutic effect Effects 0.000 description 1
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- 229940043263 traditional drug Drugs 0.000 description 1
- 238000012800 visualization Methods 0.000 description 1
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Abstract
本发明涉及一种基于加权回归的茶碱指导用药装置，包括：数据集获取模块：用于获取数据集，所述数据集为不同患者在服用不同剂量茶碱后，在不同时间采集的血清内茶碱浓度；分类模块：用于将所述数据集分为训练集和测试集；模型构建模块：用于构建回归模型，利用所述回归模型对训练集进行训练，得到训练好的回归模型，并通过所述测试集对训练好的回归模型进行测试，得到拟合好的回归模型；预测模块：用于通过所述拟合好的回归模型对需要用药指导的患者进行血清茶碱浓度的预测。本发明能够对患者实现精确的茶碱用药指导。
The invention relates to a device for guiding medication of theophylline based on weighted regression, comprising: a data set acquisition module: used to acquire a data set, the data set is the serum samples collected at different times from different patients after taking different doses of theophylline. Theophylline concentration; classification module: for dividing the data set into a training set and a test set; model building module: for constructing a regression model, and using the regression model to train the training set to obtain a trained regression model, And test the trained regression model through the test set to obtain a fitted regression model; prediction module: for predicting the serum theophylline concentration for patients who need medication guidance through the fitted regression model . The invention can realize precise theophylline medication guidance for patients.
Description
技术领域technical field
本发明涉及辅助医疗技术领域，特别是涉及一种基于加权回归的茶碱指导用药装置。The invention relates to the technical field of auxiliary medical treatment, in particular to a device for guiding medication of theophylline based on weighted regression.
背景技术Background technique
茶碱(Theophylline)可使平滑肌张力降低，呼吸道扩张；可促进内源性肾上腺素、去甲肾上腺素的释放，气道平滑肌松弛；抑制钙离子由平滑肌内质网释放，降低细胞内钙离子浓度而产生呼吸道扩张作用。茶碱对平滑肌的松弛作用较强。Theophylline can reduce smooth muscle tone and dilate the airway; promote the release of endogenous epinephrine and norepinephrine, and relax airway smooth muscle; inhibit the release of calcium ions from the smooth muscle endoplasmic reticulum and reduce the intracellular calcium ion concentration produce airway dilation. Theophylline has a strong relaxing effect on smooth muscle.
茶碱是用于治疗支气管性与心源性哮喘的常用药，传统的用药方案为医生根据经验定性指导，来达到治疗的目的。难以根据每个患者的情况定量指导用药。通过人为判断的方法常常判断不准确，如果用药量过大，会给心源性哮喘的治疗带来负面影响，如果用药量过小，达不到治疗心源性哮喘的目的。Theophylline is a commonly used drug for the treatment of bronchial and cardiogenic asthma. The traditional drug regimen is based on the qualitative guidance of doctors based on experience to achieve the purpose of treatment. It is difficult to quantitatively guide medication according to the situation of each patient. The method of human judgment is often inaccurate. If the dosage is too large, it will have a negative impact on the treatment of cardiogenic asthma. If the dosage is too small, the purpose of treating cardiogenic asthma will not be achieved.
临床为了控制慢性哮喘病人的发作，血清茶碱浓度必须达到8—20μg/ml，超过20μg/ml可引起中毒反应。但是血清茶碱浓度影响因素多，个体差异大，在实际用药场景中很难根据病人临床表现个性化定量给药，造成不可挽回的后果。In order to control the attacks of chronic asthma patients, the serum theophylline concentration must reach 8-20μg/ml, and more than 20μg/ml can cause toxic reactions. However, there are many factors affecting the serum theophylline concentration, and individual differences are large. In actual drug use scenarios, it is difficult to personalize and quantitatively administer drugs according to the clinical manifestations of patients, resulting in irreversible consequences.
因此需要寻找一种合适的方法给患有心源性哮喘的病人提供与其病况相适应的茶碱，从而达到治疗效果。Therefore, it is necessary to find a suitable method to provide patients with cardiac asthma with theophylline suitable for their condition, so as to achieve the therapeutic effect.
发明内容SUMMARY OF THE INVENTION
本发明所要解决的技术问题是提供一种基于加权回归的茶碱指导用药装置，能够对患者实现精确的茶碱用药指导。The technical problem to be solved by the present invention is to provide a theophylline medication guidance device based on weighted regression, which can realize accurate theophylline medication guidance for patients.
本发明解决其技术问题所采用的技术方案是：提供一种基于加权回归的茶碱指导用药装置，包括：The technical scheme adopted by the present invention to solve the technical problem is to provide a weighted regression-based theophylline guided medication device, including:
数据集获取模块：用于获取数据集，所述数据集为不同患者在服用不同剂量茶碱后，在不同时间采集的血清内茶碱浓度；Data set acquisition module: used to acquire a data set, the data set is the serum theophylline concentration collected at different times after taking different doses of theophylline from different patients;
分类模块：用于将所述数据集分为训练集和测试集；Classification module: used to divide the data set into training set and test set;
模型构建模块：用于构建回归模型，利用所述回归模型对训练集进行训练，得到训练好的回归模型，并通过所述测试集对训练好的回归模型进行测试，得到拟合好的回归模型；Model building module: used to construct a regression model, use the regression model to train the training set to obtain a trained regression model, and test the trained regression model through the test set to obtain a fitted regression model ;
预测模块：用于通过所述拟合好的回归模型对需要用药指导的患者进行血清茶碱浓度的预测。Prediction module: used to predict the serum theophylline concentration for patients who need medication guidance through the fitted regression model.
所述分类模块中将所述数据集分为训练集和测试集，具体为：将所述数据集分为80％训练集和20％测试集。In the classification module, the data set is divided into a training set and a test set, specifically: the data set is divided into 80% training set and 20% test set.
所述模型构建模块中的回归模型为加权线性回归模型。The regression model in the model building module is a weighted linear regression model.
所述模型构建模块中的构建回归模型，利用所述回归模型对训练集进行训练，得到训练好的回归模型，还包括：在利用所述回归模型对训练集进行训练时，对训练集中不同偏移值的数据点赋予不同的权重，其中，所述偏移值为训练集中的数据点与所述回归模型对应的线或平面之间的距离。Building a regression model in the model building module, using the regression model to train the training set, to obtain a trained regression model, further includes: when using the regression model to train the training set, different biases in the training set are performed. Different weights are assigned to the shifted data points, wherein the offset value is the distance between the data points in the training set and the line or plane corresponding to the regression model.
所述对训练集中不同偏移值的数据点赋予不同的权重，公式为：
所述模型构建模块中的通过所述测试集对训练好的回归模型进行测试，得到拟合好的回归模型，具体为：利用所述测试集对训练好的回归模型进行测试，得到测试结果，将所述测试结果结果与真实结果进行比对，来计算训练好的回归模型的决定系数R^2；In the model building module, the trained regression model is tested through the test set to obtain a fitted regression model, specifically: using the test set to test the trained regression model to obtain a test result, The test result is compared with the real result to calculate the coefficient of determination R^2 of the trained regression model;
若所述决定系数R^2大于预设值，则将训练好的回归模型作为拟合好的回归模型，并输出拟合好的回归模型；If the coefficient of determination R^2 is greater than the preset value, the trained regression model is used as the fitted regression model, and the fitted regression model is output;
若所述决定系小R^2小于预设值，则调整训练好的回归模型的权重，直到决定系数R^2大于预设值。If the determination coefficient R^2 is smaller than the preset value, the weight of the trained regression model is adjusted until the determination coefficient R^2 is greater than the preset value.
所述预设值为0.7。The preset value is 0.7.
有益效果beneficial effect
由于采用了上述的技术方案，本发明与现有技术相比，具有以下的优点和积极效果：本发明相较于传统线性回归方法，采用的加权线性回归估计的回归系数标准误更小(更稳定、变异更小)，模型拟合更优；本发明将传统医学与现代计算机技术结合，实现精确的、个性化的用药指导方案；本发明的患者无需再去医院获得指导用药方案，降低医疗成本，提高治疗效率。Compared with the prior art, the present invention has the following advantages and positive effects due to the adoption of the above-mentioned technical solution: compared with the traditional linear regression method, the standard error of the regression coefficient estimated by the weighted linear regression of the present invention is smaller (more The invention combines traditional medicine with modern computer technology to realize an accurate and personalized medication guidance plan; the patients of the present invention do not need to go to the hospital to obtain a medication guidance plan, reducing medical costs. cost and improve treatment efficiency.
附图说明Description of drawings
图1是本发明实施方式的流程图。FIG. 1 is a flowchart of an embodiment of the present invention.
具体实施方式Detailed ways
下面结合具体实施例，进一步阐述本发明。应理解，这些实施例仅用于说明本发明而不用于限制本发明的范围。此外应理解，在阅读了本发明讲授的内容之后，本领域技术人员可以对本发明作各种改动或修改，这些等价形式同样落于本申请所附权利要求书所限定的范围。The present invention will be further described below in conjunction with specific embodiments. It should be understood that these examples are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of the present application.
本发明的实施方式涉及一种基于加权回归的茶碱指导用药装置，请参阅图1，包括：Embodiments of the present invention relate to a weighted regression-based device for guiding medication of theophylline, please refer to FIG. 1 , including:
数据集获取模块：用于获取数据集，所述数据集为不同患者在服用不同剂量茶碱后，在不同时间采集的血清内茶碱浓度。Data set acquisition module: used to acquire a data set, the data set is the serum theophylline concentration collected at different times after taking different doses of theophylline from different patients.
分类模块：用于将所述数据集分为训练集和测试集。Classification module: used to divide the data set into training set and test set.
所述分类模块中将所述数据集分为训练集和测试集，具体为：将所述数据集分为80％训练集和20％测试集。In the classification module, the data set is divided into a training set and a test set, specifically: the data set is divided into 80% training set and 20% test set.
模型构建模块：用于构建回归模型，利用所述回归模型对训练集进行训练，得到训练好的回归模型，并通过所述测试集对训练好的回归模型进行测试，得到拟合好的回归模型。Model building module: used to construct a regression model, use the regression model to train the training set to obtain a trained regression model, and test the trained regression model through the test set to obtain a fitted regression model .
进一步地，所述模型构建模块中的回归模型为加权线性回归模型。Further, the regression model in the model building module is a weighted linear regression model.
传统的线性回归采用普通最小二乘法拟合回归模型求得残差，以残差平方(或残差绝对值)对合适的解释变量进行回归，获得回归方程。然而对于偏移值不同的数据点都采用统一化处理，会影响模型的拟合效果。因此本实施方式对不同偏移值的数据点进行改进，具体如下：Traditional linear regression uses ordinary least squares to fit a regression model to obtain residuals, and uses residual squares (or absolute values of residuals) to regress appropriate explanatory variables to obtain regression equations. However, the data points with different offset values are unified, which will affect the fitting effect of the model. Therefore, this embodiment improves data points with different offset values, as follows:
进一步地，所述模型构建模块中的构建回归模型，利用所述回归模型对训练集进行训练，得到训练好的回归模型，还包括：在利用所述回归模型对训练集进行训练时，对训练集中不同偏移值的数据点赋予不同的权重，其中，所述偏移值为训练集中的数据点与所述回归模型对应的线或平面之间的距离。此处的线或平面(二维空间对应的是线，多维空间对应的是平面)即为求解回归问题时的线或平面，也就是本实施方式中回归模型的一种可视化结果。Further, constructing a regression model in the model building module, using the regression model to train a training set, to obtain a trained regression model, further includes: when using the regression model to train the training set, performing training on the training set. Data points with different offset values in the set are given different weights, wherein the offset value is the distance between the data points in the training set and the line or plane corresponding to the regression model. The line or plane here (two-dimensional space corresponds to a line, multi-dimensional space corresponds to a plane) is the line or plane when solving the regression problem, that is, a visualization result of the regression model in this embodiment.
所述对训练集中不同偏移值的数据点赋予不同的权重，公式为：
进一步地，所述模型构建模块中的通过所述测试集对训练好的回归模型进行测试，得到拟合好的回归模型，具体为：利用所述测试集对训练好的回归模型进行测试，得到测试结果，将所述测试结果结果与真实结果进行比对，来计算训练好的回归模型的决定系数R^2；Further, in the model building module, the trained regression model is tested through the test set to obtain a fitted regression model, specifically: using the test set to test the trained regression model to obtain Test result, compare described test result result with real result, calculate the coefficient of determination R^2 of the well-trained regression model;
若所述决定系数R^2大于预设值，则将训练好的回归模型作为拟合好的回归模型，并输出拟合好的回归模型；If the coefficient of determination R^2 is greater than the preset value, the trained regression model is used as the fitted regression model, and the fitted regression model is output;
若所述决定系小R^2小于预设值，则调整训练好的回归模型的权重，直到决定系数R^2大于预设值。所述预设值为0.7。If the determination coefficient R^2 is smaller than the preset value, the weight of the trained regression model is adjusted until the determination coefficient R^2 is greater than the preset value. The preset value is 0.7.
预测模块：用于通过所述拟合好的回归模型对需要用药指导的患者进行血清茶碱浓度的预测，当茶碱浓度小于8μg/ml时说明患者需要吃药，当茶碱浓度在8～20μg/ml时说明茶碱正在发挥药效，无需吃药；当茶碱浓度大于20μg/ml时说明患者服药剂量过大或用药间隔过短，有中毒风险，应及时就医进行进一步检查。Prediction module: used to predict the serum theophylline concentration of patients who need medication guidance through the fitted regression model. When the theophylline concentration is less than 8 μg/ml, it means that the patient needs to take medicine. When the concentration of theophylline is greater than 20 μg/ml, it means that the patient takes too much medication or the medication interval is too short, and there is a risk of poisoning, and should seek medical attention in time for further examination.
前述对本发明的具体示例性实施方案的描述是为了说明和例证的目的。这些描述并非想将本发明限定为所公开的精确形式，并且很显然，根据上述教导，可以进行很多改变和变化。对示例性实施例进行选择和描述的目的在于解释本发明的特定原理及其实际应用，从而使得本领域的技术人员能够实现并利用本发明的各种不同的示例性实施方案以及各种不同的选择和改变。本发明的范围意在由权利要求书及其等同形式所限定。The foregoing descriptions of specific exemplary embodiments of the present invention have been presented for purposes of illustration and description. These descriptions are not intended to limit the invention to the precise form disclosed, and obviously many changes and modifications are possible in light of the above teachings. The exemplary embodiments were chosen and described for the purpose of explaining certain principles of the invention and their practical applications, to thereby enable one skilled in the art to make and utilize various exemplary embodiments and various different aspects of the invention. Choose and change. The scope of the invention is intended to be defined by the claims and their equivalents.
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| US20050187445A1 (en) * | 2004-02-11 | 2005-08-25 | Han Sang-Joon | Method of determining a weighted regression model and method of predicting a component concentration of mixture using the weighted regression model |
| CN113555079A (en) * | 2021-09-16 | 2021-10-26 | 台州市中心医院（台州学院附属医院） | Medication prediction system based on neural network |
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| US20050187445A1 (en) * | 2004-02-11 | 2005-08-25 | Han Sang-Joon | Method of determining a weighted regression model and method of predicting a component concentration of mixture using the weighted regression model |
| CN113555079A (en) * | 2021-09-16 | 2021-10-26 | 台州市中心医院（台州学院附属医院） | Medication prediction system based on neural network |
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| Title |
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| 张懋;蒋昆谕;韩龙;赵妍;王欣;孟胜男;: "茶碱在正常及肝纤维化大鼠体内的药物动力学", 中国生化药物杂志, no. 06, 20 December 2012 (2012-12-20), pages 747 - 753 * |
| 黄露;熊欣;郭源源;熊桂斌;: "复方胆氨胶囊中氨茶碱在健康人体的生物等效性研究", 中国药师, no. 09, 5 September 2020 (2020-09-05), pages 1873 - 1875 * |
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