Time biomedical treatment method based on different phenotypes of allergic rhinitis

Abstract

The invention is applied to the technical field of rhinitis treatment, and particularly discloses a time biomedical treatment method based on different phenotypes of allergic rhinitis, and the time biomedical treatment method comprises determination of a time biomedical treatment scheme and construction of a time treatment model. According to the time biomedicine treatment method based on different phenotypes of allergic rhinitis, different phenotypes of allergic rhinitis patients are classified, and the patients are classified and marked according to different symptom attack time, allergen types and influence degrees on life quality; according to different classification numbers of phenotypes and the circadian rhythm characteristics of rhinitis attacks of the patients, extraction is carried out according to different intrinsic type interested parts of the allergic rhinitis patients, the circadian rhythm characteristics of the rhinitis attacks of the patients are obtained, according to different classification numbers of phenotypes and the circadian rhythm characteristics, a time biomedical treatment scheme is generated according to the patients, drug treatment of the patients is combined with biological clock, and the drug treatment effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of rhinitis treatment, in particular to a time-based biomedical treatment method for different phenotypes of allergic rhinitis. Background Art

[0002] Allergic rhinitis (AR) is a chronic non-infectious inflammatory disease of the nasal mucosa mediated mainly by immunoglobulin E (IgE) after atopic individuals are exposed to allergens. Its incidence rate is as high as 10%-40%, and it is increasing year by year, causing serious impact on social economy, patients' life and study. It can be divided into mild and moderate-severe according to clinical symptoms, and the corresponding medication regimens are also different. The "four major" typical clinical symptoms of allergic rhinitis (nasal congestion, nasal itching, large amounts of clear nasal discharge and paroxysmal sneezing) show the characteristics of 24-hour circadian rhythm changes, and they are mostly aggravated in the early morning or midnight. This feature suggests that the biological clock plays an important role in the occurrence and development of allergic rhinitis. Chronobiomedical treatment refers to the use of time on the physiological functions, pathological changes and pharmacological effects of the human body to provide the best time for disease prevention, treatment or medication.

[0003] Currently, the first-line treatment for allergic rhinitis is still mainly drug therapy, but the treatment plan does not take into account the rhythmic changes of the biological clock on allergic rhinitis. Since the symptoms and time of allergic rhinitis show obvious circadian rhythm changes, drug treatment is carried out at specific times according to the law of circadian changes of allergic rhinitis, that is, chronobiomedical treatment, which can effectively alleviate the patient's clinical symptoms to the greatest extent, improve the treatment effect, and enhance the quality of life. It can enhance the actual efficacy of the drug by optimizing the medication time and reduce its potential side effects, thereby realizing visual, personalized, and precise treatment, and increasing patient acceptance and satisfaction. Summary of the invention

[0004] The purpose of the present invention is to provide a chronobiomedical treatment method based on different phenotypes of allergic rhinitis to solve the problem that the traditional treatment scheme for allergic rhinitis proposed in the above background technology does not take the circadian rhythm characteristics of allergic rhinitis into consideration.

[0005] To achieve the above-mentioned purpose, the present invention provides the following technical solutions: a chronobiomedical treatment method based on different phenotypes of allergic rhinitis, the chronobiomedical treatment method comprising determining a chronobiomedical treatment plan and constructing a chronotherapy model, the chronobiomedical treatment plan utilizing the symptom phenotype and circadian rhythm characteristics of the patient to determine the patient's treatment plan, the chronotherapy model is constructed utilizing patient data generation and a training model to achieve rapid generation of chronotherapy plans for different allergic rhinitis patients;

[0006] Preferably, the generation of the chronobiomedical treatment plan includes the following steps: S1. Collecting clinical symptoms and onset characteristics information of multiple patients with allergic rhinitis to generate sample data; S2. Collecting auxiliary examination information of patients with allergic rhinitis in each sample data obtained, including examination items such as blood routine, allergens, nasal endoscopy, sinus CT, olfactory test, nasal acoustic reflex and nasal resistance; S3. Distinguishing and classifying different phenotypes in the sample data; S4. Extracting the different intrinsic parts of interest of each sample data to obtain the circadian rhythm characteristics of the onset of each sample data patient; S5. Generating a chronobiomedical treatment plan for patients with allergic rhinitis in each data sample based on different phenotypes and circadian rhythm characteristics, the treatment plan including medication time, medication type and medication quantity.

[0007] The above technical solution is used to analyze and process the acquired data of allergic rhinitis patients, so as to obtain the different phenotypes of patients and the circadian rhythm characteristics of attacks, and thus generate targeted chronobiomedical treatment plans for allergic rhinitis patients according to different types of patients and circadian rhythm characteristics.

[0008] Preferably, the different phenotypes in the sample data include symptom onset time, allergen type and the degree of impact on quality of life, the symptom onset time includes intermittent and continuous, the allergen type includes seasonal and perennial, and the degree of impact on quality of life includes mild and moderate-severe.

[0009] By adopting the above technical solution, a large amount of allergic rhinitis patient data can be classified by utilizing the differences in different phenotypes, so as to generate corresponding treatment plans for different symptoms.

[0010] Preferably, the classification method of different phenotypes in the sample data is as follows: the symptom onset time is marked as the first position, the intermittent is recorded as 1, and the persistent is recorded as 0; the allergen type is marked as the second position, the seasonal is recorded as 1, and the perennial is recorded as 0; the degree of impact on the quality of life is marked as the third position, the mild is recorded as 1, and the moderate to severe is recorded as 0; the phenotype is recorded in the order of the first position, the second position and the third position to obtain the classification number, and 8 classification numbers can be obtained, including intermittent seasonal mild, classification number 111; intermittent seasonal moderate to severe, classification number 110; intermittent perennial mild, classification number 101; intermittent perennial moderate to severe, classification number 100; persistent seasonal mild, classification number 011; persistent seasonal moderate to severe, classification number 010; persistent perennial mild, classification number 001; persistent perennial moderate to severe, classification number 000.

[0011] By adopting the above technical solution, different classification numbers can be used to intuitively classify patients with allergic rhinitis of different phenotypes for easy labeling.

[0012] Preferably, the construction of the time treatment model includes the following steps: S1. Constructing a prediction model based on sample data and corresponding clinical treatment plans, and the prediction model can generate a conventional treatment plan based on the clinical symptoms of patients with allergic rhinitis; S2. Extracting the features of the interested part and the clinical treatment plan in the sample data, and screening the features of the interested part and the features of the clinical treatment plan respectively to obtain the screened features; S3. Constructing a sample set based on the screened features corresponding to each sample data and the patient's time biomedical treatment plan, and using the sample set to train the prediction model to obtain the time treatment model for allergic rhinitis; S4. Inputting the clinical symptom information, objective examination information and intrinsic type of the patient to be predicted, and using the time treatment model to make a prediction, thereby obtaining the time treatment plan for allergic rhinitis of the patient to be predicted.

[0013] By adopting the above technical solution, a prediction model is constructed using sample data and clinical treatment plans. The prediction model is trained using the original data features and a sample set consisting of time biomedical treatment plans to obtain a time treatment model and quickly implement a time treatment plan for allergic rhinitis in patients.

[0014] Compared with the prior art, the beneficial effects of the present invention are: the time-based biomedical treatment method for different phenotypes of allergic rhinitis:

[0015] 1. The present invention classifies different phenotypes of allergic rhinitis patients, classifies and labels the patients according to the time of symptom onset, type of allergens and the degree of impact on quality of life, and extracts the different intrinsic types of allergic rhinitis patients. The circadian rhythm characteristics of the patients' rhinitis attacks are obtained. According to the different classification numbers of the phenotypes and the circadian rhythm characteristics, a time biomedical treatment plan is generated for the patients, and the drug treatment of the patients is combined with the biological clock to increase the effect of drug treatment;

[0016] 2. In the present invention, a prediction model is constructed by analyzing and processing the data of patients with allergic rhinitis. The prediction model can be used to generate a conventional treatment plan. At the same time, the characteristic part in the data is combined with the time biomedical treatment plan to train the prediction model, so as to combine the prediction model with time treatment to form a time treatment model for allergic rhinitis. The patient's clinical information is input into the trained time treatment model to quickly derive a time treatment plan for patients with allergic rhinitis. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 A schematic diagram of a process structure for generating a chronobiomedical treatment plan of the present invention;

[0018] Figure 2 This is a schematic diagram of the construction process of the time treatment model of the present invention;

[0019] Figure 3 It is a schematic diagram of the phenotypic classification tree structure of the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] See also Figure 1-3 The present invention provides a technical solution: a time-based biomedical treatment method for different phenotypes of allergic rhinitis.

[0022] The treatment method of chronobiomedicine includes the determination of chronobiomedicine treatment plan and the construction of chronotherapy model. The chronobiomedicine treatment plan uses the symptom phenotype and circadian rhythm characteristics of the patient to determine the patient's treatment plan. The construction of the chronotherapy model uses the patient data generation and training model to achieve the rapid generation of chronotherapy plans for different allergic rhinitis patients.

[0023] The treatment method for patients with allergic rhinitis is mainly drug therapy, that is, a targeted drug treatment plan is drawn up based on the patient's clinical symptoms, including the time, type and amount of medication. Since there are commonalities in the symptom phenotypes among patients, the same treatment plan can be proposed for the same type of patients with the same phenotype. Combining the symptom phenotype with the characteristics of the circadian rhythm can increase the effect of drug treatment and improve the actual efficacy of the drug.

[0024] The generation of a chronobiomedical treatment plan includes the following steps: S1. Collecting the clinical symptoms and onset characteristics of multiple allergic rhinitis patients to generate sample data; S2. Collecting the auxiliary examination information of allergic rhinitis patients in each sample data, including blood routine, allergens, nasal endoscopy, sinus CT, olfactory test, nasal acoustic reflex, nasal resistance and other examination items; S3. Differentiating and classifying different phenotypes in the sample data; S4. Extracting the different intrinsic parts of each sample data to obtain the circadian rhythm characteristics of the onset of each sample data patient; S5. Generate a chronobiomedical treatment plan for allergic rhinitis patients in each data sample based on different phenotypes and circadian rhythm characteristics. The treatment plan includes medication time, medication type and medication quantity;

[0025] like Figure 1As shown, the clinical symptoms and onset characteristics of a large number of patients with allergic rhinitis are collected, and sample data are generated. Auxiliary examination information of patients in each sample data is collected, including blood routine, allergens, nasal endoscopy, sinus CT, olfactory test, nasal acoustic reflex and nasal resistance, etc. The examination item results are matched with the patients one by one, and different phenotypes in the sample data patients are distinguished. At the same time, the interesting part of the intrinsic type is extracted to obtain the circadian rhythm characteristics of the patient's rhinitis attack, and the circadian rhythm characteristics are combined with different phenotypes. The samples of the same type of phenotype and the same circadian rhythm characteristics are classified into one category, and a time biomedical treatment plan for the same type of allergic rhinitis patients in each data sample is generated.

[0026] The different phenotypes in the sample data include symptom onset time, allergen type, and the degree of impact on quality of life. The symptom onset time includes intermittent and persistent, the allergen type includes seasonal and perennial, and the degree of impact on quality of life includes mild and moderate-severe. The classification method of the different phenotypes in the sample data is as follows: the symptom onset time is marked as the first position, intermittent is recorded as 1, and persistent is recorded as 0; the allergen type is marked as the second position, seasonal is recorded as 1, and perennial is recorded as 0; the degree of impact on quality of life is marked as the third position, mild is recorded as 1, Moderate to severe is recorded as 0; the phenotype is recorded in the order of the first, second and third positions to obtain the classification number, and 8 classification numbers can be obtained, including intermittent seasonal mild, classification number 111; intermittent seasonal moderate to severe, classification number 110; intermittent perennial mild, classification number 101; intermittent perennial moderate to severe, classification number 100; persistent seasonal mild, classification number 011; persistent seasonal moderate to severe, classification number 010; persistent perennial mild, classification number 001; persistent perennial moderate to severe, classification number 000;

[0027] like Figure 3 As shown, different phenotypes in the sample data are classified and labeled, so that each identical phenotype can be labeled with the same classification number, reducing the difficulty of identifying patients with the same type of allergic rhinitis. The classification process is marked in sequence with 1 and 0 according to the symptom onset time in the first position, the allergen type in the second position, and the degree of impact on the quality of life in the third position, to obtain a three-digit label number to mark a certain type of rhinitis clinical phenotype.

[0028] The construction of the time treatment model includes the following steps: S1. Construct a prediction model based on sample data and corresponding clinical treatment plans, and the prediction model can generate a conventional treatment plan based on the clinical symptoms of patients with allergic rhinitis; S2. Extract the features of the interested part and the clinical treatment plan in the sample data, and screen the features of the interested part and the features of the clinical treatment plan respectively to obtain the screened features; S3. Construct a sample set based on the screened features corresponding to each sample data and the patient's time biomedical treatment plan, and use the sample set to train the prediction model to obtain the time treatment model for allergic rhinitis; S4. Input the clinical symptom information, objective examination information and intrinsic type of the patient to be predicted, and use the time treatment model to predict, so as to obtain the time treatment plan for allergic rhinitis of the patient to be predicted;

[0029] like Figure 2 As shown, a prediction model is constructed based on the phenotypic classification number of the patient in the sample data and the corresponding clinical treatment plan. The prediction model can be used to generate a conventional treatment plan based on the patient's clinical symptoms. The screened features are obtained for the interesting parts in the sample data and the characteristics of the clinical treatment plan. The screened features are combined with the patient's time biomedical treatment plan to construct a sample set. The constructed sample set is input into the prediction model, and the prediction model is trained to associate the prediction model with time to obtain a time treatment model for allergic rhinitis. The generated time treatment model for allergic rhinitis can be used to quickly generate a time biomedical treatment plan for the patient, that is, the clinical symptom information of the patient to be predicted is input into the time treatment model, and the time treatment model is used to predict the patient's time biomedical treatment plan for allergic rhinitis.

[0030] Working principle: The present invention obtains clinical symptoms and disease characteristics information of multiple patients with allergic rhinitis, obtains auxiliary examination information of patients with allergic rhinitis, and distinguishes the clinical phenotypes of patients, and generates corresponding classification numbers for a certain type of phenotype using symptoms of different phenotypes, which is convenient for recording and processing, and on the basis of clinical phenotypes, generates corresponding chronobiomedical treatment plans for the circadian rhythm characteristics of patients with allergic rhinitis, links drug treatment with the biological clock, and increases the therapeutic effect of drugs, and constructs a prediction model based on the patient's phenotypic classification number and the corresponding clinical treatment plan. After screening the interesting parts in the sample data and the characteristics of the clinical treatment plan, the screened characteristics are combined with the patient's chronobiomedical treatment plan to construct a sample set, and the prediction model is trained to obtain a chronotherapy model for allergic rhinitis. The generated chronotherapy model for allergic rhinitis can be used to quickly generate chronobiomedical treatment plans for patients.

[0031] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A chronobiomedical treatment method for different phenotypes of allergic rhinitis, characterized by: The chronobiomedical treatment method includes determining a chronobiomedical treatment plan and constructing a chronotherapy model. The chronobiomedical treatment plan utilizes the patient's symptom phenotype and circadian rhythm characteristics to determine the patient's treatment plan. The chronotherapy model is constructed by utilizing patient data to generate and train a model to achieve rapid generation of chronotherapy plans for different patients with allergic rhinitis.

2. A chronobiomedical treatment method based on different phenotypes of allergic rhinitis according to claim 1, characterized in that: The generation of the chronobiomedical treatment plan comprises the following steps: S1. Collect clinical symptoms and characteristics of multiple patients with allergic rhinitis to generate sample data; S2. Collect auxiliary examination information of allergic rhinitis patients in each sample data, including blood routine, allergens, nasal endoscopy, sinus CT, olfactory test, nasal acoustic reflex, nasal resistance and other examination items; S3. Differentiate and classify different phenotypes in sample data; S4. Extract the different intrinsic interesting parts of each sample data to obtain the circadian rhythm characteristics of each sample data patient; S5. Generate a temporal biomedical treatment plan for allergic rhinitis patients in each data sample based on different phenotypes and circadian rhythm characteristics. The treatment plan includes medication time, medication type, and medication quantity.

3. A chronobiomedical treatment method based on different phenotypes of allergic rhinitis according to claim 2, characterized in that: The different phenotypes in the sample data include symptom onset time, allergen type, and the degree of impact on quality of life. The symptom onset time includes intermittent and continuous, the allergen type includes seasonal and perennial, and the degree of impact on quality of life includes mild and moderate-severe.

4. A chronobiomedical treatment method based on different phenotypes of allergic rhinitis according to claim 3, characterized in that: The classification method of different phenotypes in the sample data is as follows: The symptom onset time is marked as the first position, the intermittent is marked as 1, and the continuous is marked as 0; The allergen type is marked as the second position, the seasonal type is recorded as 1, and the perennial type is recorded as 0; The degree of impact on the quality of life is marked as the third digit, where mild is recorded as 1 and moderate to severe is recorded as 0; The phenotypes were recorded in the order of position 1, position 2, and position 3 to obtain the classification number, and eight classification numbers were obtained, including intermittent seasonal mild, classification number 111; Intermittent seasonal moderate to severe, classification number 110; Intermittent perennial mild, classification number 101; Intermittent perennial moderate to severe, classification number 100; Persistent seasonal mild, classification number 011; Persistent seasonal moderate to severe, classification number 010; Persistent perennial mild, classification number 001; Persistent, perennial, moderate to severe, classification number 000.

5. The method of claim 1 for treating allergic rhinitis based on chronobiomedical methods for different phenotypes, characterized in that: The construction of the time treatment model includes the following steps: S1. Construct a prediction model based on sample data and corresponding clinical treatment plans. The prediction model can generate a conventional treatment plan based on the clinical symptoms of patients with allergic rhinitis; S2. extracting the features of the part of interest and the clinical treatment plan in the sample data, and screening the features of the part of interest and the features of the clinical treatment plan respectively to obtain the screened features; S3. Based on the screened features corresponding to each sample data and the patient's temporal biomedical treatment plan, a sample set is constructed, and the prediction model is trained using the sample set to obtain a temporal treatment model for allergic rhinitis; S4. Input the clinical symptom information, objective examination information and intrinsic type of the patient to be predicted, and use the time treatment model to make a prediction, so as to obtain the time treatment plan for allergic rhinitis of the patient to be predicted.

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