Self-adaptive medical oncology medicine monitoring system

Abstract

The invention discloses a self-adaptive medicine monitoring system for medical oncology. The self-adaptive medicine monitoring system comprises the following steps: step 1, designing and implementing a dynamic monitoring module; 2, collecting and transmitting real-time data; 3, developing and realizing a self-adaptive algorithm; 4, fusing the multi-modal data; 5, a feedback and adjustment mechanism; and step 6, long-term follow-up visit and data accumulation. By introducing a microneedle sensor and a real-time data transmission system, the drug concentration and biomarkers of a patient can be continuously and noninvasively monitored dynamically, and the real-time performance ensures that medical staff can obtain the physiological state of the patient in time, quickly recognize potential excessive or insufficient drug conditions, make adjustments more quickly, and improve the medical treatment efficiency. The treatment safety and effectiveness are improved; through a machine learning model constructed based on real-time monitoring data, doctors can obtain accurate drug adjustment suggestions. A noninvasive technology and a real-time feedback mechanism are adopted, and pain and discomfort felt by a patient in the treatment process can be remarkably reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of drug detection, and particularly to an adaptive tumor internal medicine drug monitoring system. Background Art

[0002] Tumor is one of the main diseases causing death globally. With the progress of medical technology, more and more treatment methods have been developed, including chemotherapy, radiotherapy, immunotherapy, etc. However, the individuation of tumor treatment remains a huge challenge. Different patients have very different responses to the same drug, which makes it difficult for physicians to adjust drug doses and treatment plans. With the development of micro sensors and non-invasive monitoring technologies, biosensors have shown broad prospects in medical applications. Microneedle sensors can collect biological fluid samples (such as blood) without damaging the skin and monitor drug concentrations and biomarkers in real time. The introduction of such technologies makes real-time dynamic monitoring possible and provides a new solution for tumor drug management.

[0003] However, there are still significant deficiencies in the existing technologies, such as:

[0004] In the existing technologies: Existing tumor drug monitoring methods often rely on regular blood tests and patient self-reporting, which leads to the lag of information feedback and cannot timely reflect the true state of patients. Since the changes in drug concentrations and biomarkers may be very rapid, delayed monitoring may cause patients to not receive necessary drug adjustments in a timely manner, thus affecting the treatment effect and safety. During the current treatment process, doctors often adjust drug regimens based on experience and historical data, lacking accurate and real-time data support. This results in the lack of individuation of treatment plans for individual patients, making it difficult to meet the physiological characteristics and disease conditions of different patients, and may cause side effects or reduce the treatment effect. Summary of the Invention

[0005] The purpose of the present invention is to provide an adaptive tumor internal medicine drug monitoring system to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: An adaptive tumor internal medicine drug monitoring system, including the following steps:

[0007] Step 1: Design and implementation of a dynamic monitoring module;

[0008] Step 2: Real-time data collection and transmission;

[0009] Step 3: Development and implementation of an adaptive algorithm;

[0010] Step 4: Multimodal data fusion;

[0011] Step 5: Feedback and adjustment mechanism;

[0012] Step Six: Long-term Follow-up and Data Accumulation.

[0013] Preferably, the said Step One: Design and Implementation of the Dynamic Monitoring Module specifically includes:

[0014] Determine the sensor type: Select a microneedle sensor, which can non-invasively monitor the drug concentration and biomarker levels in the blood;

[0015] Installation and calibration: When the patient visits the doctor, professional medical staff implant the microneedle sensor under the skin and calibrate the sensor regularly to ensure the accuracy of the data.

[0016] Preferably, the said Step Two: Real-time Data Collection and Transmission specifically includes:

[0017] Data acquisition device: Construct an integrated data acquisition device to upload the data collected by the microneedle sensor to the central server in real time via Bluetooth or Wi-Fi;

[0018] Data processing platform: Configure a high-performance data processing system on the server side, which can quickly analyze the real-time data to ensure instant update every time data is uploaded.

[0019] Preferably, the said Step Three: Development and Implementation of the Adaptive Algorithm specifically includes:

[0020] Data preprocessing: Collect the patient's historical medical records, monitoring data, and drug response data, and perform data cleaning and annotation;

[0021] Establish a prediction model: Use machine learning algorithms to construct a prediction model, which should be able to predict the best drug adjustment plan based on real-time monitoring data, patient individual differences, and previous treatment responses;

[0022] Model training and verification: Optimize the model parameters through methods such as cross-validation to ensure the generalization ability of the model in different patient groups.

[0023] Preferably, the said Step Four: Multimodal Data Fusion specifically includes:

[0024] Data fusion platform: Integrate the real-time data from the microneedle sensor, genomic data, imaging data, and clinical data;

[0025] Deep learning analysis: Use deep learning algorithms to comprehensively analyze data from different sources to provide a more comprehensive assessment of the patient's health status.

[0026] Preferably, the said Step Five: Feedback and Adjustment Mechanism specifically includes:

[0027] Real-time Monitoring Dashboard: A real-time monitoring dashboard that displays various monitoring indicators and drug reaction situations;

[0028] Automatic Feedback Mechanism: When the monitoring data reaches the preset threshold, the system automatically generates an alarm and notifies the doctor and the patient via text message or APP;

[0029] Doctor Decision Support: The system provides doctors with drug adjustment suggestions based on model predictions, and doctors can make the final decision according to the actual situation.

[0030] Preferably, Step Six: Long-term Follow-up and Data Accumulation specifically includes:

[0031] Regular Evaluation Mechanism: Establish a regular evaluation mechanism, conduct regular follow-up visits to patients, and collect feedback data to continuously optimize the model and algorithm;

[0032] Data Security and Privacy Protection: Combine data encryption and access control to ensure the privacy and security of patient data.

[0033] Compared with the prior art, the beneficial effects of the present invention are:

[0034] 1. By introducing microneedle sensors and a real-time data transmission system, continuous and non-invasive dynamic monitoring of the patient's drug concentration and biomarkers can be achieved. This real-time nature ensures that medical staff can promptly obtain the patient's physiological status, quickly identify potential drug overdose or insufficiency situations, and thus make adjustments faster, improving the safety and effectiveness of treatment;

[0035] 2. Through a machine learning model built based on real-time monitoring data, doctors can obtain accurate drug adjustment suggestions. This data-driven approach can provide personalized treatment plans according to each patient's unique physiological characteristics and disease changes, thereby improving the treatment effect, reducing side effects, and improving the overall health status of the patient;

[0036] 3. Due to the adoption of non-invasive technology and a real-time feedback mechanism, the pain and discomfort felt by the patient during the treatment process will be significantly reduced. In addition, real-time monitoring enables the patient to better understand their own health status and treatment progress, and also improves the patient's satisfaction and quality of life. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 For the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0038] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0039] Please refer to Figure 1 , the present invention provides a technical solution:

[0040] An adaptive tumor medical drug monitoring system includes the following steps:

[0041] Step 1: Design and implementation of the dynamic monitoring module;

[0042] Step 2: Real-time data collection and transmission;

[0043] Step 3: Development and implementation of the adaptive algorithm;

[0044] Step 4: Multimodal data fusion;

[0045] Step 5: Feedback and adjustment mechanism;

[0046] Step 6: Long-term follow-up and data accumulation.

[0047] Step 1: Design and implementation of the dynamic monitoring module, specifically including:

[0048] Determine the sensor type: Select a microneedle sensor, which can non-invasively monitor the drug concentration and biomarker levels in the blood;

[0049] The micro-sensor in this embodiment is a device well-known to those skilled in the art and will not be elaborated here. By comparing it with other monitoring technologies such as traditional blood tests and skin sensors, the advantages of the microneedle sensor in terms of non-invasiveness, real-time performance, and patient comfort are evaluated. When selecting a microneedle sensor, non-invasive monitoring ability, real-time data acquisition ability, adaptability to specific drugs and biomarkers, and reusability should be followed.

[0050] Installation and calibration: When the patient visits the doctor, a professional medical staff implants the microneedle sensor under the skin and calibrates the sensor regularly to ensure the accuracy of the data.

[0051] Evaluate at the patient's visit, including taking a medical history and performing a physical examination, to confirm that the patient is suitable for using the microneedle sensor. In a sterile environment, a professional medical staff uses a non-invasive tool to implant the microneedle sensor under the patient's skin, select a suitable location such as the wrist or abdomen, ensure good contact between the sensor and the skin, and record the implantation site and time. Immediately perform a preliminary calibration on the sensor after implantation to ensure that it can accurately measure drug concentrations and biomarkers. Conduct a calibration test using a standard solution or a drug with a known concentration to ensure that the sensor readings are within an acceptable range. Establish a regular calibration system, such as once a month, to evaluate the accuracy and stability of the sensor. At subsequent visits, collect sensor data for comparative analysis and perform recalibration when deviations are found. The selection and installation steps of the microneedle sensor not only have significant improvements in technology but also can greatly enhance the patient's treatment experience and efficacy in practical applications, laying a solid foundation for the entire adaptive oncology medical drug monitoring system.

[0052] Step 2: Real-time data collection and transmission, specifically including:

[0053] Data acquisition device: Construct an integrated data acquisition device to upload the data collected by the microneedle sensor to the central server in real time via Bluetooth or Wi-Fi. The real-time data upload and processing enable doctors to immediately understand the patient's drug response and adjust the treatment plan in a timely manner, enhancing the effect of personalized medicine. Patients can view their real-time monitoring data through an application.

[0054] If a Bluetooth module is selected for data transmission, choose a low-power Bluetooth (such as BLE) module for short-distance data transmission, which is suitable for use in a short-distance environment such as a hospital. If Wi-Fi is selected for transmission, choose a Wi-Fi-enabled module to achieve longer-distance data transmission and ensure that the data can be uploaded to the remote server in a timely manner. Regularly read the output data of the microneedle sensor and set the sampling frequency.

[0055] Data processing platform: Configure a high-performance data processing system on the server side to quickly analyze the real-time data and ensure that it is updated immediately every time the data is uploaded.

[0056] Select an appropriate server architecture (such as a cloud computing platform or a local server) according to the data volume and processing requirements to ensure its high-performance computing capabilities. Select a high-performance database (such as MySQL, PostgreSQL, or NoSQL databases), establish a data storage solution to facilitate the rapid query and update of real-time data, analyze the real-time received and uploaded data, quickly calculate the change trends of drug concentrations and biomarkers, and generate real-time reports. Compared with traditional regular blood tests, real-time data collection and upload significantly improve the timeliness of data. Doctors can respond quickly. With the help of a high-performance data processing platform, the speed of real-time data analysis is significantly increased, meeting the needs of dynamic monitoring.

[0057] Step 3: Development and implementation of an adaptive algorithm, specifically including:

[0058] Data preprocessing: Collect the patient's historical medical records, monitoring data, and drug reaction data, and perform data cleaning and annotation;

[0059] Collect the patient's historical medical records, including medical history, previous treatments, reaction records, genetic information, etc., integrate real-time monitoring data, including drug concentrations and biomarker levels provided by microneedle sensors, collect drug reaction data, including adverse reactions, efficacy evaluations, and patients' self-reports, fill in or delete missing data to ensure the integrity of the dataset, and annotate the data according to drug reactions and treatment results, such as annotating efficacy, no obvious efficacy, or adverse reactions, etc. Divide the historical data into a training set and a test set to prepare for subsequent model training and verification.

[0060] Build a prediction model: Use machine learning algorithms to build a prediction model that can predict the best drug adjustment plan based on real-time monitoring data, patient individual differences, and previous treatment reactions;

[0061] According to the data characteristics and the nature of the problem, select an appropriate machine learning algorithm (such as random forest, support vector machine (SVM), neural network, etc.). Consider the interpretability and complexity of the model. Especially in a clinical environment, selecting a more transparent model (such as a decision tree) may be more in line with actual needs. Use the selected machine learning algorithm to build a prediction model based on the training set. The goal of the model is to predict the best drug adjustment plan based on real-time monitoring data, including drug dosage, adjustment frequency, etc.

[0062] Model training and verification: Optimize the model parameters through methods such as cross-validation to ensure the generalization ability of the model in different patient groups;

[0063] Train the model using the training set, adjust the model parameters (such as learning rate, tree depth, etc.) to ensure that the model can well adapt to the training data, implement hyperparameter tuning, adopt methods such as grid search or random search to find the optimal parameter combination, adopt the cross-validation method, divide the data into multiple subsets, and conduct multiple rounds of training and validation to evaluate the stability and generalization ability of the model, calculate evaluation metrics (such as accuracy, recall, F1 score, etc.), evaluate the performance of the model in different patient groups, based on the validation results, further optimize the model structure and parameters to improve the applicability of the model in the real clinical environment, continuously monitor the model performance, and regularly update and retrain the model to adapt to new data and patient characteristics;

[0064] Traditional medication regimens are often experience-based, while adaptive algorithms can accurately predict the best drug adjustment plan according to the patient's real-time data and historical responses to achieve personalized treatment. Compared with static regimens, adaptive algorithms can analyze and monitor data in real time, dynamically adjust the treatment plan, and effectively respond to changes in the patient's condition.

[0065] Step Four: Multimodal data fusion, specifically including:

[0066] Data fusion platform: Integrate real-time data from microneedle sensors, genomic data, imaging data, and clinical data;

[0067] The real-time data comes from the drug concentration and biomarker data of microneedle sensors, the genomic data comes from the patient's genetic information, including genomic sequencing results, the imaging data comes from imaging examination results such as CT and MRI, and the clinical data mainly comes from medical records, treatment records, laboratory test results, etc. Select a suitable database management system such as a NoSQL database, establish a data storage plan to support the storage of multiple data formats, design a data architecture to ensure that various types of data can be seamlessly integrated and efficiently queried, preprocess different data sources, including data cleaning, standardization, and format conversion, to ensure the consistency and availability of the data, perform image preprocessing on the imaging data to improve the image quality and analyzability, design a data fusion mechanism to ensure that multiple data sources can be integrated on a unified platform, realize data identification and association, and ensure that data from different sources can be associated through identifiers such as patient ID;

[0068] Deep learning analysis: Use deep learning algorithms to comprehensively analyze data from different sources to provide a more comprehensive assessment of the patient's health status;

[0069] Select a suitable deep learning framework such as TensorFlow or PyTorch for model development, ensuring that it supports multi-modal data processing. Use convolutional neural networks to process image data, combined with recurrent neural networks or fully connected neural networks to process other types of data. Train the model using the integrated multi-modal data, adjust the hyperparameters, and optimize the model performance. Adopt ensemble learning methods to improve the prediction accuracy through the combination of different models;

[0070] Existing technologies often rely on a single data source for patient assessment, while multi-modal data fusion can provide a more comprehensive health status assessment, considering the multi-dimensional impacts of genetics, real-time monitoring, imaging, and clinical information. The powerful features of deep learning algorithms can learn deeper patterns from complex and correlated data, improving the accuracy of assessment results and helping doctors formulate more scientific treatment plans.

[0071] Step Five: Feedback and adjustment mechanism, specifically including:

[0072] Real-time monitoring dashboard: A real-time monitoring dashboard that displays various monitoring indicators and drug reaction situations;

[0073] The monitoring dashboard mainly displays key monitoring indicators such as drug concentration, biomarker levels, imaging assessment results, etc. Set the data refresh frequency to ensure that the dashboard can reflect the latest monitoring data in real time.

[0074] Automatic feedback mechanism: When the monitoring data reaches the preset threshold, the system automatically generates an alarm and notifies the doctor and patient via text message or APP;

[0075] Determine the preset thresholds for each monitoring indicator, set the alarm criteria based on clinical experience and previous studies. For each indicator, set the normal range, critical value, and danger threshold to implement a real-time monitoring system. Once the monitoring data reaches or exceeds the preset threshold, an alarm is automatically generated and sent to the doctor and patient via text message or mobile application (APP) to ensure that important information can be quickly transmitted.

[0076] Doctor decision support: The system provides drug adjustment suggestions based on model predictions, and the doctor can make the final decision according to the actual situation.

[0077] Based on the results of multi-modal data fusion and deep learning analysis, generate personalized drug adjustment suggestions for doctors, including specific suggestions such as adjusting the dosage, changing the medication plan, or monitoring frequency. Integrate an intelligent decision support system to provide auxiliary information such as clinical guidelines and drug interaction alerts, and provide simulation results to show the impact of different adjustment plans on the patient's long-term prognosis. Based on the suggestions generated by the system, the doctor combines clinical experience, patient feedback, and specific circumstances to make the final decision;

[0078] Existing technologies often rely on periodic reports and monitoring. In contrast, this solution ensures the immediacy and responsiveness of information through real-time monitoring dashboards and automatic feedback mechanisms. Traditional systems usually lack automatic decision support, while this solution combines deep learning and multimodal data analysis to provide data-based intelligent adjustment suggestions, enhancing the scientific nature of clinical decision-making. Through real-time monitoring and alert mechanisms, patients can participate more actively in their treatment processes.

[0079] Step Six: Long-term Follow-up and Data Accumulation, specifically including:

[0080] Regular Evaluation Mechanism: Establish a regular evaluation mechanism to conduct regular follow-up visits to patients, collect feedback data to continuously optimize the model and algorithms;

[0081] Formulate a schedule for regular follow-up visits, determine the evaluation content, including drug reactions, changes in health status, quality of life, and patient satisfaction, etc. Integrate the data collected during the follow-up visits and conduct comparative analysis with the previous data to identify changes in the patient's health status and drug reactions. Based on the feedback information, evaluate the effectiveness of the treatment plan and the patient experience, adjust the follow-up strategy, incorporate patient feedback and new monitoring data into model training to optimize the existing algorithms, improve the accuracy and adaptability of the model, regularly review the analysis process, and update the model in a timely manner to reflect the latest clinical practices and research progress.

[0082] Data Security and Privacy Protection: Combine data encryption and access control to ensure the privacy and security of patient data;

[0083] All patient data is protected using strong encryption technology during storage and transmission to ensure data security. Encryption technology is well-known to those skilled in the art of this technology and will not be elaborated here.

[0084] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An adaptive oncology drug monitoring system, characterized in that: The steps include: Step 1: Design and implementation of dynamic monitoring module; Step 2: Real-time data collection and transmission; Step 3: Development and implementation of adaptive algorithms; Step 4: Multimodal data fusion; Step 5: Feedback and adjustment mechanism; Step 6: Long-term follow-up and data accumulation.

2. The adaptive oncology drug monitoring system according to claim 1, characterized in that: The step 1: design and implementation of the dynamic monitoring module specifically includes: Determine the sensor type: Choose microneedle sensors, which can non-invasively monitor drug concentrations and biomarker levels in the blood; Installation and calibration: When the patient visits the doctor, a professional medical staff will implant the microneedle sensor under the skin and calibrate the sensor regularly to ensure the accuracy of the data.

3. The adaptive oncology drug monitoring system according to claim 2, characterized in that: The step 2: real-time data collection and transmission, specifically includes: Data collection device: Build an integrated data collection device to upload the data collected by the microneedle sensor to the central server in real time via Bluetooth or Wi-Fi; Data processing platform: The server side is equipped with a high-performance data processing system that can quickly analyze real-time data and ensure that it can be updated instantly every time the data is uploaded.

4. The adaptive oncology drug monitoring system according to claim 3, characterized in that: The step three: development and implementation of the adaptive algorithm, specifically includes: Data preprocessing: Collect patients’ medical history, monitoring data, and drug reaction data, and perform data cleaning and labeling; Build a predictive model: Use machine learning algorithms to build a predictive model that can predict the best medication adjustment plan based on real-time monitoring data, individual patient differences, and previous treatment responses; Model training and validation: Optimize model parameters through methods such as cross-validation to ensure the generalization ability of the model in different patient groups.

5. The adaptive oncology drug monitoring system according to claim 1, characterized in that: The step 4: multimodal data fusion, specifically includes: Data fusion platform: Integrate real-time data from microneedle sensors, genomic data, imaging data, and clinical data; Deep learning analysis: Use deep learning algorithms to conduct comprehensive analysis of data from different sources to provide a more comprehensive assessment of patient health status.

6. The adaptive oncology drug monitoring system according to claim 1, characterized in that: Step 5: Feedback and adjustment mechanism, specifically including: Real-time monitoring dashboard: A real-time monitoring dashboard that displays various monitoring indicators and drug reactions; Automatic feedback mechanism: When the monitoring data reaches the preset threshold, the system automatically generates an alarm and notifies doctors and patients via SMS or APP; Doctor decision support: The system provides doctors with medication adjustment recommendations based on model predictions, and doctors can make final decisions based on actual conditions.

7. The adaptive oncology drug monitoring system according to claim 1, characterized in that: The step six: long-term follow-up and data accumulation, specifically includes: Regular evaluation mechanism: Establish a regular evaluation mechanism, regularly visit patients, and collect feedback data to continuously optimize models and algorithms; Data security and privacy protection: Combine data encryption and access control to ensure the privacy and security of patient data.

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