Integrated dynamic drug metabolism monitoring and early warning system

Abstract

The invention provides an integrated dynamic drug metabolism monitoring and early warning system, and relates to the technical field of drug metabolism monitoring and early warning, the integrated dynamic drug metabolism monitoring and early warning system comprises a data acquisition module, a data processing module, a model construction module, an early warning mechanism module and a user interaction module, the data acquisition module is composed of an optical sensor, a chemical sensor and an electrophysiological sensor, according to the system, the concentration of drugs and related physiological parameters are monitored in real time, different types of biological signals are captured, and the sensitivity and selectivity of the sensors are considered in the design of the data acquisition module so as to ensure the stability and accuracy in different physiological states. Drug concentration and physiological parameters are monitored in real time from multiple dimensions, sensors select and cooperate to form an efficient acquisition network according to performance expression, data quality is optimized in combination with a multi-source data fusion algorithm (such as Kalman filtering and a weighted average method), and a data processing module adopts a statistical analysis algorithm to clean standardized data.

Description

Technical Field

[0001] The present invention relates to the technical field of drug metabolism monitoring and early warning, and in particular to an integrated dynamic drug metabolism monitoring and early warning system. Background Art

[0002] In the healthcare field, drug metabolism monitoring is crucial to ensure the safety and effectiveness of drug treatment. Traditional drug metabolism monitoring methods mainly rely on regular blood sampling and laboratory analysis. Although this method can provide certain information such as drug concentration, it has many limitations.

[0003] In recent years, with the continuous development of sensor technology, information technology and data analysis algorithms, some new methods of drug metabolism monitoring have emerged. For example, various types of sensors are used to monitor drug concentrations and related physiological parameters in real time. Optical sensors can reflect changes in drug concentrations by detecting the optical absorption or emission characteristics of specific substances; chemical sensors can accurately detect specific chemical components; electrophysiological sensors can be used to monitor bioelectric signals to understand the effects of drugs on body organ functions. In terms of data processing, statistical analysis algorithms are widely used to clean, standardize and analyze the collected data to improve the quality and availability of the data. Frequency domain analysis techniques such as Fourier transform and wavelet transform are also gradually applied to signal processing to reveal the frequency components and dynamic change characteristics in the data.

[0004] In terms of model construction, the classical pharmacokinetic model provides a basic framework for understanding the metabolic process of drugs in the body. At the same time, machine learning algorithms such as support vector machines and long short-term memory networks have also begun to be introduced to combine the individual differences and physiological characteristics of users to build more personalized and accurate drug metabolism models. The establishment of an early warning mechanism helps to detect abnormal drug metabolism in a timely manner, and reminds users and medical staff to pay attention to potential risks by setting thresholds and various notification methods. User interaction modules are also increasingly valued, and the friendly user interface and custom display function enable users to more easily understand their health status and drug metabolism information.

[0005] However, there are still some problems with existing drug metabolism monitoring technologies:

[0006] Problem 1: The accuracy and stability of sensors need to be improved. Different sensors may have different performances under different physiological conditions, making it difficult to ensure that reliable data is always provided. For example, some sensors may be more sensitive to environmental factors, resulting in large measurement errors. The data collection mode is relatively single and cannot adapt well to the individual differences and diverse needs of different users. The static monitoring mode cannot reflect the dynamic changes of drug metabolism in real time, while the dynamic monitoring mode may not be able to provide comprehensive baseline information in some cases. The collaboration between sensors is not close enough, and the data fusion effect is not ideal. The data collected by different types of sensors may have compatibility issues, making it difficult to achieve efficient integration, which affects the accuracy and completeness of the overall data;

[0007] Problem 2: The data cleaning and standardization process may not be accurate enough. The outlier detection algorithm may misjudge some normal data fluctuations as outliers, or miss real abnormal data points. Missing value filling methods may also introduce errors, affecting the accuracy of subsequent analysis, and the application of frequency domain analysis technology is not mature enough. The Fourier transform has limitations when processing non-stationary signals and may not accurately capture the rapidly changing frequency characteristics of drug metabolism. The parameter selection and application scenarios of the wavelet transform need to be further optimized to better extract useful time domain and frequency domain features;

[0008] Question 3: Although the classical pharmacokinetic model has a certain theoretical basis, it does not adequately consider individual differences in practical applications. Different users’ physiological characteristics and genetic factors have a significant impact on drug metabolism, and the existing model is difficult to accurately adapt to the specific situation of each user;

[0009] Problem 4: The setting of thresholds lacks personalization and scientificity. Current thresholds are usually set based on group data or experience, without fully considering the individual differences and physiological characteristics of each user, resulting in low sensitivity and specificity of warnings, and prone to false alarms or missed alarms.

[0010] Therefore, an integrated dynamic drug metabolism monitoring and early warning system is needed to solve the above problems. Summary of the invention

[0011] Technical issues solved

[0012] In view of the shortcomings of the prior art, the present invention provides an integrated dynamic drug metabolism monitoring and early warning system to solve the following problems:

[0013] 1. The accuracy and stability of sensors need to be improved. Different sensors may have different performances under different physiological conditions, making it difficult to ensure reliable data is always provided. For example, some sensors may be more sensitive to environmental factors, resulting in large measurement errors, and the data collection mode is relatively single, which cannot adapt well to the individual differences and diverse needs of different users. The static monitoring mode cannot reflect the dynamic changes of drug metabolism in real time, while the dynamic monitoring mode may not be able to provide comprehensive baseline information in some cases. The collaboration between sensors is not close enough, and the data fusion effect is not ideal. The data collected by different types of sensors may have compatibility issues, making it difficult to achieve efficient integration, which affects the accuracy and integrity of the overall data.

[0014] 2. The problem that the data cleaning and standardization process may not be accurate enough has been solved. The outlier detection algorithm may misjudge some normal data fluctuations as outliers, or miss real abnormal data points. The missing value filling method may also introduce errors, affecting the accuracy of subsequent analysis, and the application of frequency domain analysis technology is not mature enough. The Fourier transform has limitations when processing non-stationary signals and may not accurately capture the rapidly changing frequency characteristics during drug metabolism. The parameter selection and application scenarios of the wavelet transform need to be further optimized to better extract useful time domain and frequency domain features.

[0015] 3. Although the classical pharmacokinetic model has a certain theoretical basis, it does not fully consider individual differences in practical applications. The physiological characteristics and genetic factors of different users have a great influence on drug metabolism, and the existing model is difficult to accurately adapt to the specific situation of each user;

[0016] 4. Solve the problem of lack of personalization and scientificity in threshold setting. The current threshold is usually set based on group data or experience, without fully considering the individual differences and physiological characteristics of each user, resulting in low sensitivity and specificity of warnings, and prone to false alarms or missed alarms.

[0017] Technical Solution

[0018] To achieve the above objectives, the present invention is implemented through the following technical solutions: an integrated dynamic drug metabolism monitoring and early warning system, including a data acquisition module, a data processing module, a model building module, an early warning mechanism module and a user interaction module. The data acquisition module is composed of an optical sensor, a chemical sensor and an electrophysiological sensor to monitor drug concentration and related physiological parameters in real time and capture different types of biological signals. The data acquisition module design considers sensor sensitivity and selectivity to ensure stability and accuracy under different physiological states. The sensor is selected based on its performance in specific biomarker detection. The data acquisition module has multiple static and dynamic monitoring modes to adapt to individual differences and specific needs. The sensors collaborate through signal processing technology to form an efficient data acquisition network to achieve real-time continuous data monitoring.

[0019] Preferably, the data acquisition module adopts a multi-source data fusion algorithm, including Kalman filtering and weighted averaging to integrate multi-source data from biosensors and laboratory testing equipment. The data fusion process optimizes data quality by weighted processing through credibility assessment of different data sources. The data acquisition module also includes a data optimization strategy combined with signal processing technology to smooth and denoise the original data to improve the accuracy and effectiveness of subsequent analysis.

[0020] Preferably, the data processing module uses a statistical analysis algorithm to clean and standardize the collected drug metabolism data. Data cleaning performs quality control on the collected data by detecting outliers and filling missing values ​​to ensure data integrity and consistency. The algorithm selection in data processing considers the applicability and efficiency of processing complex data sets. The data processing module also integrates frequency domain analysis data processing technology to extract data feature information using Fourier transform and wavelet transform to form a comprehensive data representation based on time and frequency domains to facilitate subsequent model construction and optimization.

[0021] Preferably, the data processing module uses Fourier transform and wavelet transform to extract frequency domain features. Fourier transform converts time domain signals into frequency domain signals to reveal frequency components and provide information for subsequent analysis. Wavelet transform can analyze non-stationary signals and its multi-resolution characteristics enable the module to capture dynamic changes in drug metabolism at different scales to extract time domain and frequency domain features.

[0022] Preferably, the model building module uses a machine learning algorithm to achieve dynamic adjustment based on the classical pharmacokinetic model combined with the user's physiological characteristics and individual differences, and takes the user's individualized data into consideration to establish a drug metabolism model that adapts to the specific physiological state. The model building process uses advanced algorithms such as support vector machines and long short-term memory networks to process data to improve accuracy and adaptability.

[0023] Preferably, the model building module learns the user's drug metabolism data in real time and continuously updates the model parameters to adapt to the user's physiological changes. The module is designed with an efficient learning mechanism that dynamically adjusts the model structure and parameter settings through historical data analysis to ensure that the user's latest physiological state is reflected. An online learning method is used to quickly respond to the user's physiological changes and timely update the drug metabolism prediction results to enhance the real-time performance of the system. Through real-time data monitoring, the user's changing trends are automatically identified to adjust the model to improve prediction accuracy and user satisfaction.

[0024] Preferably, the early warning mechanism module sets personalized drug concentration and physiological indicator thresholds. When the monitoring data exceeds the threshold, the system automatically generates an alarm and notifies the user. The module reasonably sets the threshold by analyzing the user's historical monitoring data to ensure the sensitivity and specificity of the early warning, and takes into account the individual differences and physiological characteristics of the users to make the early warning mechanism adaptable to different users. When the early warning is triggered, a variety of notification methods are provided to ensure that the user receives the information in time. The module also has the function of recording historical early warning information to facilitate subsequent analysis and improvement. The design goal of the module is to help users monitor their health status, prevent potential adverse drug reactions, and ensure medication safety through a scientific early warning mechanism.

[0025] Preferably, the early warning mechanism module continuously optimizes the early warning strategy through a reinforcement learning algorithm and can dynamically adjust the threshold according to historical data and user feedback. The built-in algorithm analyzes the historical effects of early warnings and identifies the optimal early warning strategy through data mining to ensure flexibility and accuracy.

[0026] Preferably, the user interaction module provides a user-friendly interface to intuitively display drug metabolism status, historical data and warning information so that users can easily understand their health status. The user interface supports a custom display function so that users can select data types and formats according to personal needs to enhance the experience.

[0027] Beneficial Effects

[0028] The present invention provides an integrated dynamic drug metabolism monitoring and early warning system. It has the following beneficial effects:

[0029] 1. The present invention integrates optical, chemical and electrophysiological sensors to monitor drug concentration and physiological parameters in real time from multiple dimensions. Sensors are selected based on performance and collaborate to form an efficient acquisition network. Multi-source data fusion algorithms (such as Kalman filtering and weighted averaging) are combined to optimize data quality. At the same time, the data processing module uses statistical analysis algorithms to clean and standardize data, and uses Fourier transform and wavelet transform to extract feature information, which improves the accuracy and comprehensiveness of monitoring, reduces errors and interference, and provides high-quality data for subsequent analysis and modeling, better revealing the periodic laws and dynamic change characteristics of drug metabolism.

[0030] 2. The model building module of the present invention is based on the classic pharmacokinetic model. It adopts a machine learning algorithm for dynamic adjustment in combination with the user's physiological characteristics and individual differences, incorporates individualized data and learns and updates in real time, so that the drug metabolism model fits the user's actual physiological state. The early warning mechanism module sets personalized thresholds and continuously optimizes them through a reinforcement learning algorithm. It is dynamically adjusted according to the user's historical data and feedback to ensure the sensitivity and specificity of the early warning, realize personalized medical care, and improve the safety of medication and the level of health management.

[0031] 3. The user interaction module of the present invention provides a friendly interface, displays information in a concise and intuitive manner, supports customized display functions to meet users' personalized needs, and the information feedback mechanism allows users to evaluate system suggestions and warnings, providing a basis for system optimization. The system's integrated design and automated operation improve medical management efficiency, reduce manual intervention costs, and enhance the overall medical management level, making it convenient for users to monitor their health status in real time and participate in self-health management. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 It is a specific flow chart of the present invention;

[0033] Figure 2 It is a simulation diagram of drug concentration detection of the system of the present invention;

[0034] Figure 3 This is the system frequency domain analysis simulation diagram of the present invention

[0035] Figure 4 It is a simulation diagram of the physiological parameters of the system of the present invention. DETAILED DESCRIPTION

[0036] 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. Specific embodiment one:

[0038] like Figure 1-4As shown in the figure, an integrated dynamic drug metabolism monitoring and early warning system includes a data acquisition module, a data processing module, a model building module, an early warning mechanism module and a user interaction module. The data acquisition module is composed of optical sensors, chemical sensors and electrophysiological sensors, which can monitor drug concentrations and related physiological parameters in real time and capture different types of biological signals. Its design takes into account the sensitivity and selectivity of sensors to ensure stability and accuracy under different physiological states. Sensors are selected according to the detection performance of specific biomarkers. It has multiple monitoring modes to adapt to individual differences and needs. Through signal processing technology, an efficient data acquisition network is formed to achieve real-time continuous monitoring. This module uses multi-source data fusion algorithms, such as Kalman filtering and weighted averaging to integrate multi-source data, optimizes data quality through weighted processing of data source credibility assessment, and also includes data optimization strategies combined with signal processing technology for smoothing and denoising to improve the accuracy and effectiveness of subsequent analysis. The data processing module uses statistical analysis algorithms to clean and standardize the collected data, ensures data integrity and consistency through outlier detection and missing value filling, considers the applicability and efficiency of the algorithm to complex data sets, and integrates frequency domain analysis technology to extract feature information using Fourier transform and wavelet transform to form a comprehensive data representation for subsequent model construction and optimization. The data processing module uses Fourier transform and wavelet transform to extract frequency domain features. Fourier transform converts time domain signals to frequency domain to reveal frequency components. Wavelet transform analyzes non-stationary signals with multi-resolution characteristics to capture dynamic changes in metabolism at different scales to extract time and frequency domain features. The model construction module uses machine learning algorithms to dynamically adjust based on the classical pharmacokinetic model combined with user physiological characteristics and individual differences, incorporates individualized data to establish a drug metabolism model that adapts to physiological states, and uses advanced algorithms to process data to improve accuracy and adaptability. The model construction module can learn user drug metabolism data in real time to update model parameters to adapt to physiological changes. Through efficient learning mechanisms, it analyzes historical data and dynamically adjusts model structure and parameter settings to ensure that the latest physiological state is reflected. It uses online learning methods to quickly respond and update prediction results in a timely manner to enhance real-time performance. Through real-time data monitoring, it identifies change trends and adjusts models to improve prediction accuracy and user satisfaction. The early warning mechanism module sets personalized drug concentration and physiological indicator thresholds. When the monitoring data exceeds the threshold, it automatically generates an alarm to notify the user. By analyzing historical monitoring data, it reasonably sets thresholds to ensure the sensitivity and specificity of the early warning to adapt to different users. The early warning trigger provides multiple notification methods and has the function of recording historical early warning information for subsequent analysis and improvement. The early warning mechanism module continuously optimizes the early warning strategy through reinforcement learning algorithms, dynamically adjusts the threshold according to historical data and user feedback, and uses built-in algorithms to analyze the historical effects of early warnings to mine and identify the optimal strategy to ensure flexibility and accuracy. The user interaction module provides a friendly interface to intuitively display drug metabolism status, historical data and early warning information so that users can easily understand their health status. It supports custom display functions to allow users to select data types and formats according to their needs to improve the experience. Specific embodiment 2:

[0040] like Figure 1-4 As shown, the key algorithm mentioned in Example 1 is analyzed in detail below, including its core mathematical formula and explanation:

[0041] 1. Kalman filter algorithm

[0042] Core Mathematical Formulas

[0043] Prediction equation:

[0044]

[0045] Update equation:

[0046]

[0047] in:

[0048] is the prior state estimate (prediction value) at time k.

[0049] is the posterior state estimate (updated value) at time k.

[0050] x k is the true state value at time k (usually unknown).

[0051] A is the state transfer matrix, which describes the state change of the system from time k-1 to time k

[0052] B is the control input matrix, and the control quantity u k-1 Related.

[0053] u k-1 is the control input at time k-1.

[0054] is the prior estimation error covariance matrix.

[0055] P k is the posterior estimation error covariance matrix.

[0056] Q is the process noise covariance matrix, which represents the uncertainty within the system.

[0057] H is the observation matrix, which relates the state variables to the observations.

[0058] 2. Weighted average algorithm:

[0059] For n data points x 1 ,x 2 ,…,x n , and its corresponding weight is w 1 ,w 2 ,…,w n, weighted average The calculation formula is:

[0060]

[0061] Weighted average method is a data fusion method. In multi-source data fusion, data from different data sources may have different reliability or importance, and this difference is reflected by assigning a weight to each data point.

[0062] 3. Fourier Transform:

[0063] Continuous Fourier transform (FT): For the function f(t) (t is the time variable), its Fourier transform F(ω) is:

[0064]

[0065] Discrete Fourier Transform (DFT): For a discrete sequence f[n] (n=0, 1, ..., N-1), its DFTF[k] is:

[0066]

[0067] Inverse Discrete Fourier Transform (IDFT): If F[k] is known, then f[n] can be obtained through IDFT:

[0068]

[0069] The main purpose of Fourier transform is to convert time domain signals into frequency domain signals. In signal processing, time domain signals reflect how the signal changes over time, while frequency domain signals show the different frequency components and their amplitudes contained in the signal. Through Fourier transform, the frequency characteristics of the signal can be analyzed.

[0070] 4. Wavelet Transform:

[0071] Continuous wavelet transform (CWT): For a function f(t), its continuous wavelet transform W f (a,b) is:

[0072]

[0073] Where a is the scale parameter, b is the translation parameter, ψ(t) is the wavelet function, and ψ * (t) is its conjugate function. Discrete wavelet transform (DVWT): usually adopts binary discrete method, that is, Discrete wavelet transform coefficients c j,k for:

[0074]

[0075] Wavelet transform is a time-frequency analysis method that overcomes the shortcoming of Fourier transform in time domain locality. Wavelet transform analyzes the signal by scaling and translating the wavelet function.

[0076] 5. Support Vector Machine:

[0077] For a linearly separable binary classification problem, assume that the training data set is {(x 1 ,y 1 ),(x 2 ,y 2 ),…,(x n ,y n )}, where x i ∈R d (d is the feature dimension),y i ∈{-1,1}. SVM searches for a hyperplane w T x+b=0(w∈R d ,b∈R), so that the two types of samples can be correctly separated and the classification interval is maximized. The classification decision function is f(x)=sign(w T x+b).

[0078] 6. Long Short Memory Network:

[0079] The main structure of LSTM includes forget gate, input gate and output gate. For time step t, the input is x t , the hidden state at the previous moment is h t-1 , the cell state is c t-1 .

[0080] Forget gate: f t =σ(W f x t +U f h t-1 +b f ), determines which information is discarded from the cell state, where σ is the sigmoid function, W f , U f is the weight matrix, b f is the bias term.

[0081] i t =σ(W i x t +U i h t-1 +b i ), which determines what new information will be stored into the cell state.

[0082] Generate candidate cell states.

[0083] Cell status update:

[0084] Output gate: o t =σ(W o x t +U o h t-1 +b o )

[0085] h t =o t tanh(c t ), output the hidden state at the current moment.

[0086] LSTM is an improved recurrent neural network (RNN) that is mainly used to process sequence data. In drug metabolism monitoring, time series data such as changes in drug concentration over time and dynamic changes in physiological parameters are very common. LSTM solves the gradient vanishing and gradient exploding problems in RNN through its special gate structure, so that it can better capture long-term dependencies. Specific embodiment three:

[0088] like Figure 1-4 As shown, the following is a description of the specific application logic steps of each module and algorithm in the integrated dynamic drug metabolism monitoring and early warning system:

[0089] 1. Data acquisition module

[0090] 1. Sensor selection and configuration

[0091] According to the drug metabolism-related indicators and physiological parameters to be monitored, appropriate optical sensors, chemical sensors and electrophysiological sensors are selected. For example, optical sensors can be used to detect the optical absorption or emission characteristics of certain specific substances to reflect changes in drug concentration or related physiological processes; chemical sensors detect specific chemical components, such as detecting the concentration of drug molecules in the blood; electrophysiological sensors can monitor bioelectric signals, such as electrocardiograms and other parameters that reflect the physiological state of the heart, which may be related to the effects of drug metabolism on body organs.

[0092] Sensors are screened and configured based on their performance in specific biomarker detection to ensure that they have sufficient sensitivity and selectivity to the target signal, can accurately capture subtle changes in related biological signals during drug metabolism, and avoid the influence of other interfering signals.

[0093] 2. Data collection mode setting

[0094] Taking into account individual differences among users (such as differences in drug metabolism due to factors such as age, gender, and health status) and specific needs (different monitoring scenarios or research purposes may require different monitoring frequencies and accuracies), multiple monitoring modes such as static and dynamic are set.

[0095] Static monitoring mode can be used to obtain baseline values ​​of drug concentrations and physiological parameters at a specific time point or in a relatively stable state, such as performing a comprehensive initial data collection before a patient takes a medication.

[0096] The dynamic monitoring mode continuously monitors the dynamic changes of relevant parameters during drug metabolism in the body in real time, such as the concentration changes at different time points after the drug enters the body and the real-time fluctuations of corresponding physiological parameters (such as heart rate, blood pressure, etc.), so as to have a more comprehensive understanding of the dynamic process of drug metabolism.

[0097] 3. Sensor collaboration and data transmission

[0098] Advanced signal processing technology is used to enable different types of sensors to collaborate with each other. For example, different types of biological signals collected by optical sensors, chemical sensors, and electrophysiological sensors are integrated through data fusion algorithms to obtain more comprehensive and accurate information. For example, drug concentration data is combined with electrophysiological signals reflecting organ function to comprehensively judge the impact of drug metabolism on the body. The sensor transmits the collected data to the central processing unit in real time through a variety of connection methods (such as Bluetooth, Wi-Fi, wired connection, etc.). During the transmission process, it is necessary to ensure the integrity and timeliness of the data, avoid data loss or delay, and provide sufficient information support for subsequent data processing and analysis.

[0099] 2. Data Processing Module

[0100] 1. Data cleaning and standardization

[0101] Data cleaning: Quality control of drug metabolism data received from the data acquisition module. Through the outlier detection algorithm, data points that are significantly deviated from the normal range are identified, which may be caused by sensor failure, measurement error or other abnormal conditions. For example, for drug concentration data, if a measurement value is far beyond the expected range of normal drug concentration, it may be judged as an outlier. At the same time, the missing value filling method is used to reasonably supplement the missing data caused by various reasons (such as temporary sensor failure, data transmission interruption, etc.) to ensure the integrity of the data.

[0102] Standardization: Use advanced algorithms to convert data from different sources, units and scales into a unified standard form so that they can be compared and analyzed on the same scale. For example, drug concentration data collected by different sensors can be uniformly converted into international standard units for subsequent comprehensive analysis and modeling. This can eliminate dimensional differences between data and improve the accuracy and reliability of data analysis.

[0103] 2. Frequency domain analysis and feature extraction

[0104] Fourier transform is used to convert drug metabolism data in the time domain into frequency domain signals to reveal the frequency components in the signals. By analyzing the frequency characteristics, it is possible to understand whether there are periodic changes in the drug metabolism process. For example, the periodic fluctuations of certain physiological parameters over time may be related to the drug metabolism cycle.

[0105] Wavelet transform is used to analyze non-stationary drug metabolism data. Its multi-resolution characteristics enable it to capture dynamic changes in drug metabolism at different scales. For example, in the stage where drug concentration rises or falls rapidly, wavelet transform can extract the characteristics of these changes in more detail, including mutation points, trend changes and other time domain and frequency domain characteristics.

[0106] The extracted feature information is integrated to form a comprehensive data representation based on time and frequency domains, providing richer and more representative data features for subsequent model construction and optimization, so that the model can better learn and understand the complex patterns of drug metabolism.

[0107] Model building module

[0108] Basic model selection and individualized data integration

[0109] Based on the classic pharmacokinetic model, this model describes the general laws of drug absorption, distribution, metabolism and excretion in the body. The model is initially customized based on the user's physiological characteristics (such as age, weight, gender, liver and kidney function, etc.) and individual differences (such as the effects of genetic factors, disease history, etc. on drug metabolism).

[0110] Incorporate the user-specific data obtained from the data acquisition and processing module into the model building process. For example, the real-time drug concentration monitoring data, physiological parameters, and related personal health information of a specific user are combined with the pharmacokinetic model so that the model can more accurately reflect the drug metabolism characteristics of the user.

[0111] Algorithm application and model training

[0112] In the process of model building, advanced algorithms such as support vector machines and long short-term memory networks are used to process data. Support vector machines classify different drug metabolism states or user categories by finding an optimal classification hyperplane, such as distinguishing normal drug metabolism from abnormal metabolism, or classifying drug metabolism patterns of users according to their individual characteristics.

[0113] Long short-term memory networks are mainly used to process time series data, and can better capture the time-related dynamic changes and long-term dependencies in drug metabolism. For example, based on the trend of drug concentration changes and the dynamic changes of corresponding physiological parameters over a period of time, the future direction of drug metabolism can be predicted.

[0114] In order to improve the accuracy and adaptability of the model, a reasonable parameter selection and optimization strategy is designed. By constantly adjusting the parameters of the algorithm (such as the penalty parameter and kernel function parameter in the support vector machine, the number of neurons, the number of layers, and the learning rate in the long short-term memory network, etc.), a large amount of historical drug metabolism data is used for training and verification during the model training process to prevent overfitting (the model is too complex and only fits the training data well, but has poor prediction ability for new data) and underfitting (the model is too simple and cannot capture the laws and characteristics in the data well), maintain the generalization ability of the model, and enable it to be effectively applied in different scenarios and users.

[0115] The model building module can learn the user's drug metabolism data in real time. As new data is continuously generated, the historical data is analyzed and mined through an efficient learning mechanism to dynamically adjust the model's structure and parameter settings. For example, if the user's physiological state is found to have changed (such as disease recovery, weight change, and other factors that affect drug metabolism), the model will adjust the relevant parameters in a timely manner based on the new data to ensure that it always reflects the user's latest physiological state.

[0116] The online learning method is adopted to enable the model to quickly respond to the physiological changes of users. When new real-time data is received, the model is immediately updated and optimized, and the drug metabolism prediction results are updated in a timely manner. For example, at different time points after the user takes the medicine, according to the real-time monitored changes in drug concentration and physiological parameters, the model quickly adjusts the predicted drug metabolism curve to provide users with more accurate personalized drug metabolism information, thereby enhancing the real-time and accuracy of the system and improving the effectiveness of user health management.

[0117] Early warning mechanism module

[0118] Threshold setting and personalization adjustment

[0119] Through in-depth analysis of the user's historical monitoring data, we can understand the characteristics of the user's drug concentration range, physiological index change range, etc. during normal drug metabolism. Based on these data analyses, we can reasonably set personalized drug concentration and physiological index thresholds. For example, for a specific user, based on the previous drug metabolism data statistics, we can determine the upper and lower thresholds of drug concentration under normal circumstances, as well as the normal fluctuation range thresholds of related physiological parameters (such as heart rate, blood pressure, etc.).

[0120] Considering the individual differences and physiological characteristics of users, such as the effects of age, gender, underlying diseases and other factors on drug metabolism and physical reactions, the thresholds are adjusted individually. For example, the elderly may metabolize drugs more slowly due to decreased physical function, and their drug concentration thresholds may need to be adjusted accordingly; for users with heart disease, the thresholds of physiological indicators such as heart rate may need to be set more strictly to ensure the sensitivity and specificity of the warning, that is, to accurately detect potential adverse drug reactions or abnormal drug metabolism, while avoiding too many false alarms.

[0121] When the real-time monitoring data exceeds the set threshold, the system automatically triggers an alarm. For example, if the user's drug concentration is monitored to exceed the pre-set safety range, or if the physiological indicators fluctuate abnormally and exceed the corresponding threshold, the system immediately activates the early warning mechanism.

[0122] Provide multiple notification methods, such as SMS, application push notifications, sound alarms, etc., to ensure that users can receive relevant information in a timely manner. At the same time, the notification should contain concise and clear warning content, such as "Abnormal drug concentration, please pay attention" or "Heart rate exceeds the normal range, please consult a doctor in time", so that users can quickly understand the warning situation and take corresponding measures.

[0123] The early warning mechanism module has the function of recording the user's historical early warning information, including the warning time, the type of threshold triggered, the drug metabolism data and physiological parameters at that time, and other detailed information. By analyzing these historical early warning information, we can understand the frequency, trend, and possible related factors of abnormal drug metabolism in users.

[0124] Based on the results of historical data analysis, the early warning strategy is continuously optimized. For example, if it is found that a certain user often triggers an early warning at a specific time period or a specific drug dose, it may be necessary to further adjust the threshold setting or optimize the monitoring plan for the user. At the same time, by analyzing the user's feedback on the early warning (such as whether the user has taken the correct measures according to the early warning prompts, and subsequent changes in health status, etc.), the accuracy and effectiveness of the early warning mechanism are continuously improved, and the user's medication safety and health management level are improved. Specific embodiment four:

[0126] like Figure 1-4 As shown, the following is a detailed hardware composition and hardware description of each module in Example 1:

[0127] 1. Data acquisition module

[0128] Optical Sensors

[0129] Components and key points:

[0130] Light source system: light emitting diode or laser diode, etc., select a specific wavelength light source according to the detection target to stimulate the relevant optical properties of drug molecules.

[0131] Photodetector: photodiode or photomultiplier tube, etc., responsible for receiving light and converting it into electrical signals. Its performance affects detection sensitivity and response speed.

[0132] Optical filter: selects light of a specific wavelength to pass through, improves detection specificity, and excludes interfering light. Sample pool or flow cell: places biological samples, and the design should consider sample volume, fluidity, and coupling efficiency with the optical system.

[0133] Chemical Sensors:

[0134] Components and key points:

[0135] Sensitive membrane: The core part, made of materials that selectively respond to specific drugs, such as membranes containing specific enzymes used to detect related substances. Its performance determines the selectivity and sensitivity of the sensor.

[0136] Electrode system: includes working electrode (coated with sensitive membrane), reference electrode and counter electrode. The electrode material and geometry affect the performance. The working electrode undergoes chemical reaction to generate electrical signals.

[0137] Electrolyte solution: Required by some sensors, its composition and concentration need to be optimized according to the sensor type and detection target to promote ion transport and chemical reactions.

[0138] Electrophysiological Sensors

[0139] Components and key points:

[0140] Electrode: Surface electrodes (such as silver / silver chloride) are used for skin surface detection, and implantable electrodes are used for more accurate detection in the body. The shape, size and material of the electrode affect the signal quality and acquisition effect.

[0141] Signal amplifier: To amplify weak bioelectric signals, it must have the characteristics of high gain, low noise, and wide dynamic range.

[0142] Filter: Remove noise and interference components from electrophysiological signals and adjust parameters according to the signal frequency characteristics.

[0143] Analog-to-digital converter (ADC): Converts analog electrophysiological signals into digital signals. Resolution and sampling rate affect signal accuracy and capture capabilities.

[0144] 2. Data Processing Module

[0145] Microprocessor or Digital Signal Processor (DSP)

[0146] Components and key points:

[0147] Central Processing Unit (CPU): includes arithmetic logic unit, register group, control unit, etc., executes instructions and data processing operations, and its performance determines the processing speed and efficiency.

[0148] Memory: RAM is used to temporarily store data and instructions, ROM stores solidified program codes, etc. The memory capacity affects the amount of data that the system can process simultaneously.

[0149] Peripheral interface: such as USB, Ethernet, Bluetooth and other interfaces, used to communicate with other modules. The type and number of interfaces determine the connection flexibility and data transmission speed.

[0150] Data storage devices

[0151] Components and key points:

[0152] Solid-state drive (SSD) or hard disk drive (HDD): SSD has fast read and write speed, low energy consumption, good shock resistance but high cost, while HDD has large storage capacity and low cost. Choose according to the data volume and read and write speed requirements.

[0153] Storage controller: manages and controls the read and write operations of data in storage devices, and must have good error correction and data cache management functions.

[0154] 3. Model building module:

[0155] High-performance computing chips (such as GPU)

[0156] Components and key points: GPU core: Contains a large number of computing cores, works in parallel, accelerates model training and optimization, and is suitable for processing complex data and algorithms in drug metabolism monitoring.

[0157] Video memory: stores computing data and model parameters. Its capacity and bandwidth determine the scale and speed of data processing by the GPU.

[0158] Controller: coordinates data transmission and task scheduling between the GPU core and video memory to improve GPU work efficiency.

[0159] Memory module (cache) components and key points:

[0160] SRAM or DRAM chips are used as cache to store frequently accessed data and instructions. In a multi-level cache structure, SRAM is often used as a first-level or second-level cache, close to the CPU, to increase data reading speed and support fast data processing during model building.

[0161] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the statement "comprising a reference structure" do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0162] 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. Integrated dynamic drug metabolism monitoring and early warning system, characterized by: It includes a data acquisition module, a data processing module, a model building module, an early warning mechanism module and a user interaction module. The data acquisition module is composed of optical sensors, chemical sensors and electrophysiological sensors to monitor drug concentrations and related physiological parameters in real time and capture different types of biological signals. The data acquisition module design takes into account sensor sensitivity and selectivity to ensure stability and accuracy under different physiological states. The sensor is selected based on its performance in specific biomarker detection. The data acquisition module has multiple static and dynamic monitoring modes to adapt to individual differences and specific needs. The sensors collaborate through signal processing technology to form an efficient data acquisition network to achieve real-time continuous data monitoring.

2. The integrated dynamic drug metabolism monitoring and early warning system according to claim 1, characterized in that: The data acquisition module adopts a multi-source data fusion algorithm, including Kalman filtering and weighted averaging to integrate multi-source data from biosensors and laboratory testing equipment. The data fusion process optimizes data quality by weighted processing through credibility assessment of different data sources. The data acquisition module also includes a data optimization strategy combined with signal processing technology to smooth and denoise the original data to improve the accuracy and effectiveness of subsequent analysis. The formulas for Kalman filtering and weighted averaging are as follows: Kalman filter algorithm Core Mathematical Formulas Prediction equation: Update equation: Weighted average algorithm: For n data points x1,x2,…,x n , whose corresponding weights are w1,w2,…,w n , weighted average The calculation formula is: Kalman filtering and weighted averaging are used to fuse the data.

3. The integrated dynamic drug metabolism monitoring and early warning system according to claims 1-4, characterized in that: The data processing module uses a statistical analysis algorithm to clean and standardize the collected drug metabolism data. Data cleaning performs quality control on the collected data by detecting outliers and filling missing values ​​to ensure data integrity and consistency. The algorithm selection in data processing considers the applicability and efficiency of processing complex data sets. The data processing module also integrates frequency domain analysis data processing technology to extract data feature information using Fourier transform and wavelet transform to form a comprehensive data representation based on time and frequency domains to facilitate subsequent model construction and optimization. The calculation formulas for Fourier transform and wavelet transform are: Continuous Fourier transform (FT): For the function f(t) (t is the time variable), its Fourier transform F(ω) is: Discrete Fourier Transform (DFT): For a discrete sequence f[n] (n=0, 1, ..., N-1), its DFTF[k] is: where k = 0, 1, ..., N-1 Inverse Discrete Fourier Transform (IDFT): If F[k] is known, then f[n] can be obtained through IDFT: Continuous wavelet transform (CWT): For a function f(t), its continuous wavelet transform W f (a,b) is: Fourier transform and wavelet transform are used to extract data feature information.

4. The integrated dynamic drug metabolism monitoring and early warning system according to claims 1-3, characterized in that: The data processing module uses Fourier transform and wavelet transform to extract frequency domain features. Fourier transform converts time domain signals into frequency domain signals to reveal frequency components and provide information for subsequent analysis. Wavelet transform can analyze non-stationary signals and its multi-resolution characteristics enable the module to capture dynamic changes in drug metabolism at different scales to extract time domain and frequency domain features.

5. The integrated dynamic drug metabolism monitoring and early warning system according to claims 1-4, characterized in that: The model building module uses a machine learning algorithm to achieve dynamic adjustment based on the classical pharmacokinetic model combined with the user's physiological characteristics and individual differences, and takes the user's individualized data into consideration to establish a drug metabolism model that adapts to the specific physiological state. The model building process uses advanced algorithms such as support vector machines and long short-term memory networks to process data to improve accuracy and adaptability.

6. The integrated dynamic drug metabolism monitoring and early warning system according to claims 1-5, characterized in that: The model building module learns the user's drug metabolism data in real time and continuously updates the model parameters to adapt to the user's physiological changes. The module is designed with an efficient learning mechanism that dynamically adjusts the model structure and parameter settings through historical data analysis to ensure that the user's latest physiological state is reflected. An online learning method is used to quickly respond to the user's physiological changes and timely update the drug metabolism prediction results to enhance the real-time performance of the system. Through real-time data monitoring, the user's change trend is automatically identified to adjust the model to improve the prediction accuracy and user satisfaction.

7. The integrated dynamic drug metabolism monitoring and early warning system according to claims 1-6, characterized in that: The early warning mechanism module sets personalized drug concentration and physiological indicator thresholds. When the monitoring data exceeds the threshold, the system automatically generates an alarm and notifies the user. The module reasonably sets the threshold by analyzing the user's historical monitoring data to ensure the sensitivity and specificity of the early warning, taking into account the individual differences and physiological characteristics of the users so that the early warning mechanism can adapt to different users. When the early warning is triggered, a variety of notification methods are provided to ensure that the user receives information in a timely manner. The module also has the function of recording historical early warning information for subsequent analysis and improvement. The design goal of the module is to help users monitor their health status, prevent potential adverse drug reactions, and ensure medication safety through a scientific early warning mechanism.

8. The integrated dynamic drug metabolism monitoring and early warning system according to claims 1-7, characterized in that: The early warning mechanism module continuously optimizes the early warning strategy through the reinforcement learning algorithm and can dynamically adjust the threshold according to historical data and user feedback. The built-in algorithm analyzes the historical effects of early warning and identifies the optimal early warning strategy through data mining to ensure flexibility and accuracy.

9. The integrated dynamic drug metabolism monitoring and early warning system according to claims 1-8, characterized in that: The user interaction module provides a user-friendly interface to intuitively display drug metabolism status, historical data and warning information so that users can easily understand their health status. The user interface supports a custom display function and users can select data types and formats according to personal needs to enhance the experience.

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