CN117219293A - Method for monitoring and intervening insulin resistance of rheumatoid diabetes - Google Patents
Method for monitoring and intervening insulin resistance of rheumatoid diabetes Download PDFInfo
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Abstract
The invention discloses a monitoring and intervention method for insulin resistance of rheumatoid diabetes, which belongs to the technical field of medical treatment, and comprises the following specific steps: (1) drawing a patient blood sample and performing a genetic analysis; (2) monitoring the biomarker and storing the monitoring data; (3) integrating the collected data to make an individualized prediction; (4) formulating a personalized intervention plan according to the prediction result; (5) Resistance monitoring of the patient periodically to adjust the intervention plan; the invention can automatically process a large amount of medical data, lighten the workload of medical staff, improve the data processing efficiency, simultaneously provide high-precision insulin resistance prediction, be beneficial to early finding the potential health risk of patients and ensure more accurate treatment scheme.
Description
Technical Field
The invention relates to the technical field of medical treatment, in particular to a method for monitoring and intervening insulin resistance of rheumatoid diabetes.
Background
Rheumatoid diabetes is an autoimmune disease that causes the patient's immune system to attack its joints, causing chronic inflammation and pain. There is a complex correlation between RA and diabetes. In one aspect, RA patients are at high risk of developing diabetes, possibly associated with chronic inflammation, immune system abnormalities, and certain drug therapies. On the other hand, insulin resistance in diabetics may also be associated with the onset and course of RA. Management and treatment requires multidisciplinary collaboration including rheumatists, endocrinologists, nutritionists, and lifestyle coaches, among others. Against this background, an integrated monitoring and intervention method was developed aimed at assessing the insulin resistance of patients and providing personalized treatment advice, which is crucial for improving the quality of life and disease management of patients. The key objective of this approach is to provide early diagnosis, periodic monitoring and personalized interventions to reduce the risk of complications caused by diabetes and insulin resistance.
The existing method for monitoring and intervening the insulin resistance of the rheumatoid diabetes cannot automatically process a large amount of medical data, increases the workload of medical staff, has low data processing efficiency, is not beneficial to early finding the potential health risk of a patient, and has poor treatment scheme accuracy; for this reason, we propose a method for monitoring and intervening insulin resistance in rheumatoid diabetes.
Disclosure of Invention
The invention aims to solve the defects in the prior art and provides a method for monitoring and intervening insulin resistance of rheumatoid diabetes.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
a method for monitoring and intervening rheumatoid diabetes insulin resistance comprises the following specific steps:
(1) Drawing a patient blood sample and performing genetic analysis;
(2) Monitoring the biomarker and storing the monitoring data;
(3) Integrating the collected data to perform personalized predictions;
(4) Establishing an individualized intervention plan according to the prediction result;
(5) The patient is monitored for resistance on a regular basis to adjust the intervention plan.
As a further aspect of the present invention, the specific steps of the genetic analysis in the step (1) are as follows:
step one: collecting blood samples of all patients through vein blood sampling or adopting a blood collecting tube, respectively marking and recording the blood samples of all the patients with rheumatic diabetes, separating the blood samples of the patients into plasma, white blood cells and red blood cells through a centrifugal machine, and precipitating the white blood cells to the bottom of the centrifugal tube under the action of the centrifugal machine;
step two: removing the supernatant to leave a leukocyte pellet, washing the leukocyte pellet with physiological saline or buffer solution to remove impurities and plasma residues remained in the leukocyte pellet, adding Tris-EDTA buffer solution into the leukocyte pellet, and stirring to fully contact the Tris-EDTA buffer solution with the leukocytes;
step three: incubating at a suitable temperature for a period of time to promote rupture of the cell membrane, centrifuging the sample by a centrifuge to separate cell debris and nuclei, and purifying the DNA from other components by a silica gel column to obtain a purified DNA sample;
step four: amplifying the gene fragments of each patient into a sufficient number of DNA fragments, detecting the mutation of a specific gene by SNP analysis, PCR analysis or sequencing technology, comparing the analysis result with reference data in a database to determine the genotype of the patient, and simultaneously determining the genetic mutation condition related to rheumatoid diabetes and insulin resistance.
As a further aspect of the present invention, the genetic variation in the fourth step is specifically a single nucleotide polymorphism, insertion/deletion or gene rearrangement.
As a further aspect of the present invention, the biomarker monitoring in step (2) specifically comprises the following steps:
step 1: determining biomarkers which are related to insulin resistance and rheumatoid diabetes according to a gene analysis result, collecting blood samples of a patient through blood collection needles and blood collection tubes, marking each group of blood collection tubes, and recording specific information of the collection time and sample sources;
step 2: the collected blood samples are separated into serum, plasma and white blood cells by centrifugation of a centrifugal machine, after the separation of the samples is finished, the blood samples of each group are frozen or stored, and the concentration of the biomarkers in the serum, the plasma and the white blood cells after centrifugation is measured by enzyme-linked immunosorbent assay or liquid chromatography;
step 3: the measured biomarker concentration is recorded and the data is stored in a database, after which the biomarker concentration in the patient's blood is continuously monitored by implanting a micro-biosensor or using a portable monitoring device.
As a further aspect of the invention, the biomarker in step 1 specifically comprises serum insulin level, C-reactive protein, white blood cell count, lipid parameter, blood glucose, and insulin resistance index.
As a further aspect of the present invention, the step (3) of individually predicting specifically includes the following steps:
step I: collecting data from multiple groups of sources, cleaning and preprocessing the collected data to remove error values, abnormal values and missing data, and carrying out standardized processing on each group of data to form a uniform format;
step II: feature mining is carried out on each set of processed data, the most relevant and important features of insulin resistance and rheumatoid diabetes are selected, and then interaction among the features is captured by combining values of different features to create new features;
step III: randomly dividing a data set into a training set, a verification set and a test set, setting iteration times, batch processing parameters and node discarding rate, training a neural network model through the training set, updating parameters of the model through back propagation, and simultaneously monitoring the performance of the model by using the verification set;
step IV: evaluating the performance of the neural network model after training by using a test set, calculating a loss value of the model through root mean square error, minimizing the loss value through an Adam optimizer, and if the loss value does not meet a preset threshold, retraining the neural network model;
step V: the latest acquired patient data is imported into the neural network model as input data, the input data starts to pass through all hidden layers of the model from the input layer of the neural network model, all hidden layers respectively perform linear transformation and nonlinear activation on the input data, the processed data is transferred layer by layer through weights and activation functions among all layers, and then the output layer outputs a final prediction result.
As a further aspect of the present invention, the personalized intervention plan customization in step (4) comprises the following specific steps:
step (1): collecting gene analysis results, biomarker monitoring data and family medical history, life style, eating habits and exercise level of a patient, and analyzing the neural network model prediction results corresponding to the patient;
step (2): if the model predicts that insulin resistance exists, patient data and a prediction result are fed back to a corresponding doctor, the relevant doctor adjusts an insulin treatment scheme or recommends other medicines, and a targeted diet and exercise plan is formulated according to the life style of the patient; if the model predicts an elevated level of inflammation in the patient, selecting by the relevant physician an anti-inflammatory drug or adjusting the diet to reduce inflammation;
step (3): a periodic follow-up and monitoring schedule is set to track patient progress and periodically monitor patient condition and biomarkers, and adjustments to the intervention schedule are made based on the monitoring results while recording treatment progress and outcome for each patient.
Compared with the prior art, the invention has the beneficial effects that:
the method for monitoring and intervening the insulin resistance of the rheumatoid diabetes mellitus comprises the steps of collecting data from a plurality of groups of sources, cleaning and preprocessing the collected data, carrying out feature mining on each group of processed data, selecting the most relevant and important features of the insulin resistance and the rheumatoid diabetes mellitus, capturing the interaction among the features by combining the values of different features to create new features, randomly dividing the data into a training set, a verification set and a test set, setting model parameters, training a neural network model and updating the parameters through the training set, monitoring the performance of the model by using the verification set, evaluating the performance of the neural network model after training by using the test set, and training the neural network model again if the loss value does not meet a preset threshold value, the latest acquired patient data is used as input data to be imported into the neural network model, the input data starts to pass through all hidden layers of the model from the input layer of the neural network model, all hidden layers respectively conduct linear transformation and nonlinear activation on the input data, the processed data are transferred layer by layer through weights and activation functions among all layers, then the output layer outputs a final prediction result, an intervention plan is formulated according to the prediction result and the patient data, and the intervention plan is regularly monitored, so that a large amount of medical data can be automatically processed, the workload of medical staff is reduced, the data processing efficiency is improved, meanwhile, high-precision insulin resistance prediction can be provided, the potential health risks of patients can be found early, and the treatment scheme is ensured to be more accurate.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention.
FIG. 1 is a block flow diagram of a method for monitoring and intervening insulin resistance of rheumatoid diabetes.
Detailed Description
Example 1
Referring to fig. 1, a method for monitoring and intervening insulin resistance of rheumatoid diabetes comprises the following specific steps:
a blood sample of the patient is drawn and subjected to genetic analysis.
Specifically, blood samples of all patients are collected through vein blood sampling or blood collection tubes, and are respectively identified and recorded for the blood samples of all types of rheumatic diabetics, the blood samples of all types of rheumatic diabetics are separated into plasma, leucocytes and erythrocytes through a centrifuge, meanwhile, leucocytes are precipitated to the bottom of a centrifuge tube under the action of the centrifuge, the supernatant is removed, the leucocyte precipitation is left, physiological saline or buffer solution is used for washing the blood samples to remove impurities and plasma residues remained in the leucocyte precipitation, tris-EDTA buffer solution is added into the leucocyte precipitation, the Tris-EDTA buffer solution is fully contacted with the leucocytes through stirring, the cells are warmed for a period of time at a proper temperature to promote rupture of cell membranes, the samples are centrifuged through the centrifuge to separate cell fragments and cell nuclei, DNA is separated from other components through silica gel column purification to obtain pure DNA samples, gene fragments of all patients are amplified into DNA fragments with sufficient quantity, mutation of specific genes is detected through SNP analysis, PCR analysis or sequencing technology, the analysis results are compared with reference data in a database to determine genotype of the patients, and insulin resistance conditions of the diabetes related to the rheumatic diabetes are determined.
It is further noted that the genetic variation is specifically a single nucleotide polymorphism, insertion/deletion or gene rearrangement.
Biomarkers are monitored and monitoring data is stored.
Specifically, the biomarker which is known to be associated with insulin resistance and rheumatoid diabetes is determined according to the result of gene analysis, a blood sample of a patient is collected through a blood taking needle and a blood taking tube and is marked for each group of blood taking tubes, the collection time and specific information of sample sources are recorded, the collected blood sample is usually separated into serum, plasma and white blood cells by centrifugation of a centrifuge, after the separation of the sample is finished, each group of blood sample is frozen or stored, the concentration of the biomarker in the serum, plasma and white blood cells after centrifugation is measured by enzyme-linked immunosorbent assay or liquid chromatography, the measured concentration of the biomarker is recorded, the data is stored in a database, and then the concentration of the biomarker in the blood of the patient is continuously monitored by implanting a micro-biological sensor or using a portable monitoring device.
It is further noted that biomarkers specifically include serum insulin levels, C-reactive proteins, white blood cell counts, lipid parameters, blood glucose, and insulin resistance index.
The collected data are integrated for personalized prediction.
Specifically, data from multiple groups of sources are collected, the collected data are cleaned and preprocessed to remove error values, abnormal values and missing data, standardized processing is carried out on the data of each group to obtain a unified format, feature mining is carried out on the processed data of each group, the most relevant and important features of insulin resistance and rheumatoid diabetes are selected, then interaction among the features is captured through combining values of different features to create new features, the data are randomly divided into a training set, a verification set and a test set, iteration times, batch processing parameters and node discarding rate are set, a neural network model is trained through the training set, parameters of an update model are transmitted in a back direction, performance of the model is monitored through the verification set, performance of the neural network model after training is evaluated through the test set, meanwhile, loss values of the model are calculated through root mean square errors, loss values are minimized through an Adam optimizer, if the loss values do not meet preset thresholds, the latest collected patient data are guided into the neural network model as input data, the input data are input into the neural network model, the input data are input into the neural network, the neural network model is enabled to be subjected to linear transformation layers and the layers are enabled to be respectively, the hidden layers are enabled, the linear transformation layers are enabled to be different layers, the linear transformation layers are enabled to be enabled, the hidden layers are enabled to be activated, and the final results are output, and the final results are not enabled, and the hidden layers are output are activated, and the layer by the layer is enabled.
And making an individualized intervention plan according to the prediction result.
Specifically, collecting gene analysis results, biomarker monitoring data and family history, life style, eating habit and movement level of a patient, analyzing a neural network model prediction result corresponding to the patient, if the model predicts insulin resistance, feeding back the patient data and the prediction result to a corresponding doctor, adjusting an insulin treatment scheme or recommending other medicines by the relevant doctor, and simultaneously making a targeted eating and movement plan according to the life style of the patient; if the model predicts an elevated level of inflammation in the patient, the relevant physician chooses to use anti-inflammatory drugs or adjust the diet to reduce inflammation, sets up periodic follow-up and monitoring plans to track patient progress, and periodically monitors patient conditions and biomarkers, adjusts the intervention plan based on the monitoring results, and records the treatment progress and results for each patient.
The patient is monitored for resistance on a regular basis to adjust the intervention plan.
Claims (7)
1. The monitoring and intervention method for the insulin resistance of the rheumatoid diabetes is characterized by comprising the following specific steps of:
(1) Drawing a patient blood sample and performing genetic analysis;
(2) Monitoring the biomarker and storing the monitoring data;
(3) Integrating the collected data to perform personalized predictions;
(4) Establishing an individualized intervention plan according to the prediction result;
(5) The patient is monitored for resistance on a regular basis to adjust the intervention plan.
2. The method for monitoring and intervening insulin resistance in rheumatoid diabetes according to claim 1, wherein the genetic analysis in step (1) is specifically as follows:
step one: collecting blood samples of all patients through vein blood sampling or adopting a blood collecting tube, respectively marking and recording the blood samples of all the patients with rheumatic diabetes, separating the blood samples of the patients into plasma, white blood cells and red blood cells through a centrifugal machine, and precipitating the white blood cells to the bottom of the centrifugal tube under the action of the centrifugal machine;
step two: removing the supernatant to leave a leukocyte pellet, washing the leukocyte pellet with physiological saline or buffer solution to remove impurities and plasma residues remained in the leukocyte pellet, adding Tris-EDTA buffer solution into the leukocyte pellet, and stirring to fully contact the Tris-EDTA buffer solution with the leukocytes;
step three: incubating at a suitable temperature for a period of time to promote rupture of the cell membrane, centrifuging the sample by a centrifuge to separate cell debris and nuclei, and purifying the DNA from other components by a silica gel column to obtain a purified DNA sample;
step four: amplifying the gene fragments of each patient into a sufficient number of DNA fragments, detecting the mutation of a specific gene by SNP analysis, PCR analysis or sequencing technology, comparing the analysis result with reference data in a database to determine the genotype of the patient, and simultaneously determining the genetic mutation condition related to rheumatoid diabetes and insulin resistance.
3. The method according to claim 2, wherein the genetic variation in step four is a single nucleotide polymorphism, insertion/deletion or gene rearrangement.
4. The method for monitoring and intervening insulin resistance in rheumatoid diabetes according to claim 2, wherein the biomarker monitoring in step (2) is specifically performed as follows:
step 1: determining biomarkers which are related to insulin resistance and rheumatoid diabetes according to a gene analysis result, collecting blood samples of a patient through blood collection needles and blood collection tubes, marking each group of blood collection tubes, and recording specific information of the collection time and sample sources;
step 2: the collected blood samples are separated into serum, plasma and white blood cells by centrifugation of a centrifugal machine, after the separation of the samples is finished, the blood samples of each group are frozen or stored, and the concentration of the biomarkers in the serum, the plasma and the white blood cells after centrifugation is measured by enzyme-linked immunosorbent assay or liquid chromatography;
step 3: the measured biomarker concentration is recorded and the data is stored in a database, after which the biomarker concentration in the patient's blood is continuously monitored by implanting a micro-biosensor or using a portable monitoring device.
5. The method according to claim 4, wherein the biomarkers in step 1 comprise serum insulin levels, C-reactive proteins, white blood cell count, lipid parameters, blood glucose and insulin resistance index.
6. The method of claim 4, wherein the step (3) of individually predicting insulin resistance in rheumatoid diabetes is as follows:
step I: collecting data from multiple groups of sources, cleaning and preprocessing the collected data to remove error values, abnormal values and missing data, and carrying out standardized processing on each group of data to form a uniform format;
step II: feature mining is carried out on each set of processed data, the most relevant and important features of insulin resistance and rheumatoid diabetes are selected, and then interaction among the features is captured by combining values of different features to create new features;
step III: randomly dividing a data set into a training set, a verification set and a test set, setting iteration times, batch processing parameters and node discarding rate, training a neural network model through the training set, updating parameters of the model through back propagation, and simultaneously monitoring the performance of the model by using the verification set;
step IV: evaluating the performance of the neural network model after training by using a test set, calculating a loss value of the model through root mean square error, minimizing the loss value through an Adam optimizer, and if the loss value does not meet a preset threshold, retraining the neural network model;
step V: the latest acquired patient data is imported into the neural network model as input data, the input data starts to pass through all hidden layers of the model from the input layer of the neural network model, all hidden layers respectively perform linear transformation and nonlinear activation on the input data, the processed data is transferred layer by layer through weights and activation functions among all layers, and then the output layer outputs a final prediction result.
7. The method of claim 1, wherein the personalized intervention plan customization in step (4) is specifically as follows:
step (1): collecting gene analysis results, biomarker monitoring data and family medical history, life style, eating habits and exercise level of a patient, and analyzing the neural network model prediction results corresponding to the patient;
step (2): if the model predicts that insulin resistance exists, patient data and a prediction result are fed back to a corresponding doctor, the relevant doctor adjusts an insulin treatment scheme or recommends other medicines, and a targeted diet and exercise plan is formulated according to the life style of the patient; if the model predicts an elevated level of inflammation in the patient, selecting by the relevant physician an anti-inflammatory drug or adjusting the diet to reduce inflammation;
step (3): a periodic follow-up and monitoring schedule is set to track patient progress and periodically monitor patient condition and biomarkers, and adjustments to the intervention schedule are made based on the monitoring results while recording treatment progress and outcome for each patient.
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| CN117558435A (en) * | 2024-01-11 | 2024-02-13 | 四川省医学科学院·四川省人民医院 | Remote dialysis guiding system based on BCM equipment |
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| CN117558435A (en) * | 2024-01-11 | 2024-02-13 | 四川省医学科学院·四川省人民医院 | Remote dialysis guiding system based on BCM equipment |
| CN117558435B (en) * | 2024-01-11 | 2024-03-19 | 四川省医学科学院·四川省人民医院 | Remote dialysis guiding system based on BCM equipment |
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