Method for predicting potassium content of traditional Chinese medicine compound decoction

Abstract

The invention relates to the technical field of chemical component analysis, in particular to a traditional Chinese medicine compound decoction potassium content prediction method which comprises the following steps: constructing a basic database and a prescription library, storing the potassium content of a traditional Chinese medicine sample through the basic database, and storing a prescription made by a doctor through the prescription library; analyzing the potassium content of the traditional Chinese medicine sample, selecting the maximum value and storing in the basic database; constructing a traditional Chinese medicine decoction potassium content prediction algorithm through a machine learning algorithm; and predicting the potassium content according to the input medicinal materials and the specific dosage. By establishing the basic database and the prescription library, the potassium content in the decoction prepared by the prescription can be predicted after the prescription is input, so that a doctor can know the potassium content in the prescription in advance and carry out subsequent treatment planning.

Description

Technical Field

[0001] This invention relates to the technical field of chemical composition analysis, and in particular to a method for predicting the potassium content of traditional Chinese medicine compound decoctions. Background Technology

[0002] Hyperkalemia is a common and serious complication of chronic kidney disease (CKD). Its persistent or recurrent episodes can significantly accelerate the progression of kidney disease and increase the risk of cardiovascular-related and all-cause mortality. It may also lead to dose reduction or discontinuation of renin-angiotensin-aldosterone system inhibitors, thus affecting treatment efficacy. Traditional Chinese medicine (TCM) often demonstrates unique advantages in clinical disease treatment, but the potassium content of TCM decoctions and their potential impact on patients with hyperkalemia remains controversial in the academic community. The "2020 Expert Recommendations on Risk Assessment and Management of Hyperkalemia in Chronic Kidney Disease" points out that taking TCM decoctions may also increase the risk of hyperkalemia, firstly because TCM itself has a high potassium content, and secondly because TCM may inhibit potassium excretion. However, clinical research results show no significant association between TCM and serum potassium levels (whether low or high potassium); patients taking TCM decoctions as usual do not experience an increase in serum potassium levels.

[0003] Given the high solubility of potassium ions in water, the potassium content in decoctions made from traditional Chinese medicine (TCM) formulas is often confused with the potassium content of the raw herbs. However, it is important to note that the actual dissolution rate of potassium ions is influenced by many factors, meaning that the potassium dissolution rate in a TCM decoction is not simply the sum of the potassium contents of the raw herbs. For example, some potassium elements may exist in ionic form and are easily soluble in water, while others may be tightly bound to other components in the herbs and difficult to extract with water. Furthermore, the type of herb, the part used, and the texture of the herb can all affect the final dissolution rate of potassium ions in the decoction. In addition, different processing methods in different decoction formulas can also cause significant differences in potassium extraction losses. Therefore, in-depth exploration and clarification of the influencing factors of potassium ion dissolution in traditional Chinese medicine compound decoctions are of great significance for the safety of medication and the improvement of treatment effects in patients with chronic kidney disease. It is one of the key issues that urgently need to be solved in the current research field of traditional Chinese medicine. However, there is currently no reliable and effective method to predict and analyze the potassium content of traditional Chinese medicine compound decoctions, so as to provide effective technical support for clinical treatment, scientific research and other work. Summary of the Invention

[0004] In order to analyze and predict the potassium content in traditional Chinese medicine decoctions, this invention provides a method for predicting the potassium content in compound traditional Chinese medicine decoctions.

[0005] This invention provides a method for predicting the potassium content of traditional Chinese medicine compound decoctions, employing the following technical solution: A method for predicting the potassium content of a traditional Chinese medicine compound decoction includes the following steps: Construct a basic database and a prescription database. The basic database stores the potassium content of Chinese herbal medicine samples, and the prescription database stores prescriptions written by doctors. Analyze the potassium content of Chinese herbal medicine samples and select the maximum value to store in the basic database; A machine learning algorithm was used to construct a method for predicting the potassium content of traditional Chinese medicine decoctions. The potassium content is predicted based on the input medicinal materials and specific dosage.

[0006] In a specific feasible implementation, the analysis of potassium content in traditional Chinese medicine samples includes: Analyze the differences and stability of potassium content in different batches of the same type of traditional Chinese medicine samples; Analyze the differences in potassium content between prepared slices of Chinese medicinal herbs and their decoctions; The correlation between potassium content in medicinal slices and medicinal parts, processing methods, and efficacy was analyzed. The relationship between potassium content in compound decoctions and potassium content in medicinal slices was analyzed.

[0007] In a specific feasible implementation, the algorithm for predicting the potassium content of traditional Chinese medicine decoctions includes the following steps: Calculate the potassium content of each herb in the prescription, and then calculate the total potassium content of all herbs in the prescription.

[0008] In a specific feasible implementation, when inputting medicinal materials and specific dosages, similar prescriptions can be screened. The screening criteria for similar prescriptions are: the medicinal materials in the prescription are exactly the same, and the specific dosage deviation is ≤5%.

[0009] In a specific feasible implementation, when a medicinal material is input, the medicinal material information is output.

[0010] In a specific feasible implementation, the medicinal material information includes efficacy classification, total potassium content, water-soluble potassium content, and the percentage of soluble potassium.

[0011] In a specific feasible implementation, after predicting the potassium content, the medicinal materials in the prescription are replaced by screening the efficacy and parts of medicinal materials in the basic database.

[0012] In a specific feasible implementation, when screening medicinal materials, the current prescription is first identified, and medicinal materials that conflict with those in the current prescription are removed from the screening results.

[0013] In a specific feasible implementation plan, the Chinese medicine samples include clinically commonly used Chinese herbal decoction pieces, decoctions of single-herb decoction pieces, and actual compound decoctions.

[0014] In a specific feasible implementation plan, the decoction method of the medicinal materials is determined before predicting the potassium content.

[0015] In summary, the present invention has the following beneficial effects: 1. Construct a basic database and a prescription database to store the potassium content of Chinese herbal medicine samples and various prescriptions. The basic database stores the potassium content of different medicinal materials. When medicines are entered into a prescription, the potassium content of the prescription is automatically calculated as a reference for the potassium content of subsequent decoctions, which helps doctors plan subsequent treatments.

[0016] 2. By searching similar prescriptions in the prescription database, doctors can gain a better understanding of the potassium content in the prescriptions, thus enabling them to make more accurate predictions about the potassium content in the decoctions. Attached Figure Description

[0017] Figure 1 This is a flowchart of a method for predicting the potassium content in traditional Chinese medicine compound decoctions.

[0018] Figure 2 This is a comparison chart of potassium content distribution in different medicinal parts of the decoction pieces.

[0019] Figure 3 This is a comparison chart of potassium content distribution between artificially processed and natural medicinal slices.

[0020] Figure 4 This is a comparison chart of potassium content distribution in different textures of medicinal slices.

[0021] Figure 5 It shows the distribution of dissolution rates of medicinal materials from different medicinal parts. Detailed Implementation

[0022] The following combination Figures 1-5 The present invention will be described in further detail below.

[0023] The method for predicting potassium content in traditional Chinese medicine compound decoctions disclosed in this invention relies on a potassium content prediction model for traditional Chinese medicine. This model is developed based on the WPF framework. WPF (Windows Presentation Foundation) is one of the three core development libraries released by Microsoft for the Vista operating system. It is a user interface framework launched by Microsoft based on Windows Vista and is part of the .NET Framework 3.0. It provides a unified programming model, language, and framework, thus separating the work of interface designers and developers.

[0024] The method for predicting the potassium content of traditional Chinese medicine compound decoctions includes the following steps: S1, build the basic database and prescription library.

[0025] A basic database of potassium content in different traditional Chinese medicine (TCM) samples was constructed. These samples included commonly used TCM decoction pieces, single-herb decoctions, and actual compound decoctions. When determining the potassium content in TCM samples, flame atomic absorption spectrometry was used, following the detection methods in GB5009.91-2017 "Determination of Potassium and Sodium in Food". For example, when determining the potassium content in single-herb decoctions, the traditional decoction process involved soaking and two decoctions. For herbs requiring special decoction methods, the corresponding special decoction methods were used to prepare single-herb decoctions, and the unit potassium content of the single-herb decoctions was determined.

[0026] Build a prescription database to store different prescriptions for easy comparison and research later.

[0027] S2 analyzes the potassium content of traditional Chinese medicine samples and stores the data in a basic database.

[0028] The study aimed to compare and analyze the differences and stability of potassium content in different batches of the same type of traditional Chinese medicine (TCM) samples; compare and analyze the differences in potassium content among different TCM samples; analyze the correlation between potassium content in TCM samples and medicinal parts, processing methods, and efficacy; and analyze the relationship between potassium content in compound decoctions and potassium content in processed medicinal slices.

[0029] S2.1, Comparative analysis of the differences and stability of potassium content in different batches of the same type of traditional Chinese medicine samples.

[0030] S2.2, Comparative analysis of the differences in potassium content between prepared slices of Chinese herbal medicine and decoction.

[0031] S2.3, Compare and analyze the correlation between potassium content of medicinal slices and medicinal parts, processing methods, and efficacy.

[0032] S2.4, Comparative analysis of the relationship between potassium content in compound decoctions and potassium content in medicinal slices.

[0033] It is understandable that there is no fixed order between steps S2.1-S2.4, and different step numbers are used for easy differentiation and explanation.

[0034] S3 uses machine learning algorithms to construct a potassium content prediction algorithm for traditional Chinese medicine decoctions.

[0035] Based on the analysis results in step S2, benchmark information such as the maximum potassium content of a single herb, the potassium content of the decoction of a single herb, the potassium content of the combined decoction, and the dissolution rate are selected as the basis for calculation. In subsequent calculations, based on the input herbs and their corresponding quantities, the potassium content of each herb is automatically calculated, and the potassium contents of multiple herbs are added together. After the prescription is input, the system can automatically predict the potassium content of the prescription and the amount of potassium dissolved. (Prescription potassium content) The calculation formula is:

[0036] In the formula, This refers to the total number of medicinal herbs in the prescription. For the first The maximum potassium content of medicinal herbs; For the first Dosage of medicinal herbs.

[0037] Potassium content dissolved from the prescription The calculation formula is:

[0038] in, For the first Dissolution rate of medicinal herbs.

[0039] For example, a prescription includes four herbs: stir-fried Atractylodes macrocephala, stir-fried white hyacinth bean, areca nut, and Imperata cylindrica root, 10g each. The maximum potassium content of stir-fried Atractylodes macrocephala is 7327.90 mg / kg, the potassium content of the decoction of the single herb is 5403.86 mg / kg, and the potassium content of the combined decoction is 787.02 mg / L, with a dissolution rate of 73.74%. The stir-fried white hyacinth bean had a maximum potassium content of 11710.58 mg / kg, a single herb decoction with a potassium content of 9933.20 mg / kg, and a combined decoction with a potassium content of 1056.53 mg / L, with a dissolution rate of 84.82%. Areca nut has a maximum potassium content of 5725.44 mg / kg, a potassium content of 5984.50 mg / kg in a decoction of a single herb, and a potassium content of 488.10 mg / L in the combined decoction, with a dissolution rate of 69.59%. Imperata cylindrica root has a maximum potassium content of 10222.67 mg / kg, a potassium content of 9783.59 mg / kg in decoction of a single herb, and a potassium content of 867.18 mg / L in the combined decoction, with a dissolution rate of 95.70%. At this point, the potassium content of the prescription is: 7327.90 × +11710.58× +5725.44× +10222.67× ≈349.87mg; The dissolved potassium content is: 7327.90 × 73.74% × +11710.58×84.82%× +5725.44×69.59%× +10222.67×95.70%× ≈291.05mg.

[0040] Understandably, multiple studies have found that there is usually a significant difference in potassium content between decoctions prepared manually and those prepared automatically by machines. Manually prepared decoctions contain far more potassium than machine-prepared ones. Therefore, the basic database also stores potassium content under different decoction methods. When doctors prescribe medicine, they can ask patients about their decoction methods to more accurately calculate the potassium intake of patients.

[0041] S4 outputs medicinal material information such as efficacy classification, total potassium content, water-soluble potassium content, and percentage of soluble potassium, based on the input medicinal materials and specific dosage.

[0042] S5, based on the input prescription, outputs prescription information including theoretical potassium content and theoretical soluble potassium content. Medicinal herbs can be added or removed from the prescription as needed to adjust the prescription; alternatively, the prescription can be deleted directly. The overall potassium intake is assessed using the output prescription information.

[0043] When adjusting the medicinal materials in a prescription, a basic database can be used for screening, such as by screening for the efficacy of the medicinal materials and different parts of the medicinal materials. This allows for a reduction in the potassium content of the prescription while maintaining its original effects. The basic database also stores conflict relationships between different medicinal materials. When adjusting a prescription by screening medicinal materials, the database identifies the medicinal materials currently in the prescription and removes those that conflict with the current prescription from the screening results.

[0044] S6: Based on the input prescription, filter similar prescriptions from the prescription database and output them.

[0045] When screening similar prescriptions, based on the medicinal materials and dosages in the prescription, if the medicinal materials used in the prescription in the prescription database are exactly the same as those in the input prescription, and the dosage of a single medicinal material is within ±5% (inclusive), the prescription is considered similar; otherwise, it indicates that the prescriptions are different.

[0046] After actual use, the potassium content of the prescription is recorded in the basic database. This information is used to update the algorithm and data. When a new prescription is entered, by comparing it with similar prescriptions, the potassium content of the new prescription can be more accurately estimated based on the actual potassium content.

[0047] It is understandable that there is no fixed order between steps S4-S6. The steps can be adjusted and combined according to the actual situation, and different step numbers can be used for easy differentiation and explanation.

[0048] Example: To facilitate understanding, step S2 will be further explained below with reference to actual data.

[0049] 1. The effect of different medicinal parts on the potassium content of medicinal slices and decoction.

[0050] According to the Chinese Pharmacopoeia (2020 edition) and the standards of Traditional Chinese Medicine Identification, 254 kinds of Chinese medicinal herbs were classified into 12 categories based on their medicinal parts, including whole herbs and animal products. Among them, 205 kinds were plant-based (accounting for 81%), and animal products were further subdivided into animal insects and their products and animal shells. Analysis showed that there were extremely significant differences in potassium content among the medicinal parts and decoctions (p<0.01).

[0051] Regarding potassium content in processed medicinal materials, plant-based medicines generally have higher potassium content than mineral and animal-based medicines. Whole herbs and flowers have the highest potassium content. The order of potassium content by median is: whole herbs > flowers and leaves > fruits > roots and / or rhizomes > seeds and kernels > fermented products > stems and trees > animal products > barks > fungi and algae > minerals > animal shells. The potassium content of decoctions is similar to that of processed medicinal materials, but the potassium content of decoctions from animal products is lower than that from barks (see Table 1 and...). Figure 2 ).

[0052] Table 1

[0053] *p<0.05, **p<0.01 2. The effect of artificial intervention during the growth process of medicinal slices on potassium content.

[0054] Based on the differences in the cultivation methods of Chinese medicinal herbs, 254 kinds of prepared slices of Chinese medicinal herbs were divided into two groups according to the degree of human intervention during the growth process: artificially cultivated group (group 1, n=181, including cultivated plant medicines, farmed animal medicines, synthetic mineral medicines, etc.) and naturally grown group (group 2, n=73, wild plant medicines, animal medicines, natural mineral medicines, etc.) (see...). Figure 3 (See Table 2). The average potassium ion content of the processed medicinal slices and their decoction in the artificial intervention group was significantly higher than that in the naturally grown group (P < 0.05).

[0055] Table 2

[0056] *p<0.05, **p<0.01 3. The effect of the texture of medicinal slices on potassium content.

[0057] Based on the classification standards of the Chinese Pharmacopoeia (2020 edition) and the Identification of Traditional Chinese Medicine, this study systematically divided 254 kinds of Chinese medicinal herbs into 7 categories according to their texture characteristics: firm texture (including firm, relatively firm, etc.), loose texture (including loose, loose and brittle, loose and bubbly, etc.), hard texture (including hard, slightly hard, etc.), hard and brittle texture (including hard and brittle, hard and brittle, etc.), brittle texture (including brittle, relatively brittle, hard and brittle, nearly leathery and brittle, brittle, brittle and easily broken, slightly brittle, etc.), soft texture (including soft, slightly soft, etc.), and tough texture (including slightly tough, hard and tough, tough, slightly tough, etc.).

[0058] The results showed that the median potassium content of the processed medicinal slices and the potassium content of the decoction were consistent across different groups (see [link]). Figure 4 (as shown in Table 3) All samples were classified as tough > brittle > soft > firm > hard > hard and brittle > loose, with extremely significant differences (P<0.01).

[0059] Table 3

[0060] *p<0.05, **p<0.01 4. Analysis of factors related to potassium ion dissolution rate in aqueous decoction To further analyze the correlation between the potassium content of the decoction of a single herb and the potassium content of the herb itself, the dissolution rate of the decoction for each herb was calculated. Since the dissolution rate is considered to basically conform to a normal distribution, a multi-factor analysis of variance was used.

[0061] The results showed that after removing the influence of potassium content in the medicinal materials, only the medicinal parts had a significant effect on the potassium ion dissolution rate (p<0.01) (see Table 4). Further post-hoc multiple comparison studies revealed that the dissolution rates of animal shells and mineral-based medicinal materials were significantly lower, while the dissolution rates of whole herbs and flower / leaf medicinal materials were higher (P<0.05) (see Table 5 and Table 6). Figure 5 ).

[0062] Table 4

[0063] Table 5

[0064] *p<0.05, **p<0.01 The above are all preferred embodiments of the present invention and are not intended to limit the scope of protection of the present invention. Therefore, all equivalent changes made in accordance with the structure, shape and principle of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A method for predicting the potassium content of a traditional Chinese medicine compound decoction, characterized in that: Includes the following steps: Construct a basic database and a prescription database. The basic database stores the potassium content of Chinese herbal medicine samples, and the prescription database stores prescriptions written by doctors. Analyze the potassium content of Chinese herbal medicine samples and select the maximum value to store in the basic database; A machine learning algorithm was used to construct a method for predicting the potassium content of traditional Chinese medicine decoctions. The potassium content is predicted based on the input medicinal materials and specific dosage.

2. The method for predicting potassium content in traditional Chinese medicine compound decoctions according to claim 1, characterized in that: The analysis of potassium content in traditional Chinese medicine samples included: Analyze the differences and stability of potassium content in different batches of the same type of traditional Chinese medicine samples; Analyze the differences in potassium content between prepared slices of Chinese medicinal herbs and their decoctions; The correlation between potassium content in medicinal slices and medicinal parts, processing methods, and efficacy was analyzed. The relationship between potassium content in compound decoctions and potassium content in medicinal slices was analyzed.

3. The method for predicting potassium content in traditional Chinese medicine compound decoctions according to claim 1, characterized in that: The algorithm for predicting the potassium content of traditional Chinese medicine decoctions includes the following steps: Calculate the potassium content of each herb in the prescription, and then calculate the total potassium content of all herbs in the prescription.

4. The method for predicting potassium content in traditional Chinese medicine compound decoctions according to claim 1, characterized in that: When entering the medicinal materials and specific dosage, you can filter similar prescriptions. The criteria for filtering similar prescriptions are: the medicinal materials in the prescription are exactly the same and the specific dosage deviation is ≤5%.

5. The method for predicting potassium content in traditional Chinese medicine compound decoctions according to claim 1, characterized in that: When a medicinal herb is input, the herb information is output.

6. The method for predicting potassium content in traditional Chinese medicine compound decoctions according to claim 5, characterized in that: The medicinal material information includes efficacy classification, total potassium content, water-soluble potassium content, and the percentage of soluble potassium.

7. The method for predicting potassium content in traditional Chinese medicine compound decoctions according to claim 1, characterized in that: After predicting the potassium content, the medicinal materials in the prescription are replaced by screening the efficacy and parts of the medicinal materials in the basic database.

8. The method for predicting potassium content in traditional Chinese medicine compound decoctions according to claim 7, characterized in that: When screening medicinal materials, first identify the current prescription and remove medicinal materials that conflict with those in the current prescription from the screening results.

9. The method for predicting potassium content in traditional Chinese medicine compound decoctions according to claim 1, characterized in that: The Chinese medicine samples include commonly used Chinese herbal medicine decoction pieces, decoctions of single-herb decoction pieces, and actual compound decoctions.

10. The method for predicting potassium content in traditional Chinese medicine compound decoctions according to claim 1, characterized in that: Before predicting potassium content, the decoction method of the medicinal materials should be determined.

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