Tumor pain management system based on artificial intelligence technology
By combining biosensing and AI analysis modules, real-time monitoring and personalized pain management are achieved, solving the problems of inability to personalize and data security in traditional methods, and realizing efficient and safe pain management.
Patent Information
- Application Number
- CN202511520689.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-10-23
- Publication Date
- 2026-02-27
AI Technical Summary
Traditional methods of cancer pain management lack personalization and cannot monitor patients' physiological and biochemical parameters in real time, resulting in the inability to adjust pain management plans in a timely manner. Furthermore, they rely on a large amount of human and material resources and cannot guarantee data security and privacy.
It employs a biosensor module to monitor physiological and biochemical parameters in real time, combined with an AI analysis module and a personalized pain management module. Data transmission and storage are achieved through an encrypted communication protocol, and interaction is performed using natural language processing and speech recognition technologies to ensure data security and privacy.
It enables personalized pain management, improves the accuracy and timeliness of pain management, reduces doctors' workload, lowers medical costs, and protects patient data security and privacy.
Smart Images
Figure CN121583440A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of medical auxiliary management systems, in particular to a tumor pain management system based on artificial intelligence technology. BACKGROUND
[0002] With the aggravation of population aging, the number of cancer patients is increasing year by year, among which tumor pain is one of the most common symptoms of cancer patients. According to the statistics of the World Health Organization, more than 50% of patients with advanced cancer in the world will experience severe pain, and in developing countries, this proportion is even as high as 80%. Tumor pain seriously affects the quality of life of patients, and even affects the survival period of patients. Traditional tumor pain management methods mainly rely on drug treatment, such as opioid drugs and non-steroidal anti-inflammatory drugs. However, these drugs have large side effects, and long-term use may lead to drug dependence or tolerance. In addition, traditional pain management methods usually lack individualization and cannot develop the most suitable pain management plan according to the specific situation of patients.
[0003] The prior art has the following problems: Problem one, traditional pain management methods usually lack individualization and cannot develop the most suitable pain management plan according to the specific situation of patients. Traditional pain management methods cannot monitor the physiological and biochemical parameters of patients in real time, so they cannot adjust the pain management plan in time. Problem two, traditional pain management methods usually require doctors to spend a lot of time and effort, so they cannot improve the work efficiency of doctors. Traditional pain management methods usually require a lot of manpower and material resources, so they cannot reduce medical costs. Problem three, traditional pain management methods usually cannot guarantee the data security and privacy of patients. SUMMARY
[0004] Technical problems solved
[0005] In view of the deficiencies of the prior art, the present application provides a tumor pain management system based on artificial intelligence technology, which solves the problems of the prior art.
[0006] 1. Traditional pain management methods usually lack individualization and cannot develop the most suitable pain management plan according to the specific situation of patients. Traditional pain management methods cannot monitor the physiological and biochemical parameters of patients in real time, so they cannot adjust the pain management plan in time. 2. Traditional pain management methods usually require doctors to spend a lot of time and effort, so they cannot improve the work efficiency of doctors. Traditional pain management methods usually require a lot of manpower and material resources, so they cannot reduce medical costs. 3. Traditional pain management methods usually cannot guarantee the data security and privacy of patients.
[0007] Technical solution
[0008] To achieve the above object, the present application is implemented by the following technical solution: a tumor pain management system based on artificial intelligence technology, the system comprises: A biosensor module: the biosensor module is used for real-time physiological and biochemical monitoring; An AI analysis module: the AI analysis module is used for analyzing the data of the biosensor module and giving analysis data; A personalized pain management module: the personalized pain management module is used for terminal control of the analysis data of the AI analysis module; A doctor / patient interface: the doctor / patient interface is used for terminal control of the doctor / patient; A data storage module: the data storage module is used for storing the physiological and biochemical parameters, analysis results and pain management plan of the patient; The data transmission and interaction between the biosensor module, AI analysis module, personalized pain management module and doctor / patient interface are through an encrypted communication protocol.
[0009] Beneficial effects
[0010] The present application provides a tumor pain management system based on artificial intelligence technology. It has the following beneficial effects: Through the cooperative work of the AI analysis module and the personalized pain management module, the system can develop a personalized pain management plan according to the physiological and biochemical parameters of each patient. This personalized management method can more effectively relieve the pain of patients and improve the quality of life of patients.
[0011] Through real-time monitoring by the biosensor module and real-time analysis by the AI analysis module, the system can obtain the pain state of the patient in real time and timely adjust the pain management plan. This real-time monitoring and feedback mechanism can make pain management more accurate and timely.
[0012] The present application protects the data security and privacy of patients through an encrypted communication protocol and a secure data storage and access mechanism. This security and privacy protection mechanism can make patients more comfortable using the system.
[0013] The present application enables the doctor / patient interface to realize natural and efficient interaction through natural language processing and speech recognition and synthesis technology. This interaction method can improve the work efficiency of doctors and reduce the work pressure of doctors.
[0014] The application can realize automatic pain management, reduce the workload of doctors and reduce medical costs by using AI technology and wireless communication technology, and the cost reduction effect can enable more patients to receive effective pain management. BRIEF DESCRIPTION OF DRAWINGS
[0015] Fig. 1 The system architecture diagram of the application is shown in the figure. Fig. 2 The method step diagram of the application is shown in the figure. DETAILED DESCRIPTION
[0016] The technical solutions in the embodiments of the application will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the application. Obviously, the described embodiments are only part of the embodiments of the application, not all. Based on the embodiments in the application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the application. Embodiment one: As shown in the figure, a tumor pain management system based on artificial intelligence technology, the system comprises: Figs. 1-2 A biosensor module: the biosensor module is used for real-time physiological and biochemical monitoring; An AI analysis module: the AI analysis module is used for analyzing the data of the biosensor module and giving analysis data; A personalized pain management module: the personalized pain management module is used for terminal control of the analysis data of the AI analysis module; A doctor / patient interface: the doctor / patient interface is used for terminal control of the doctor / patient; A data storage module: the data storage module is used for storing physiological and biochemical parameters, analysis results and pain management plans of the patient; Data transmission and interaction between the biosensor module, the AI analysis module, the personalized pain management module and the doctor / patient interface are through an encrypted communication protocol. The biosensor module comprises a nano biosensor and a wireless data transmission unit, the nano biosensor is made of molecular arrangement nanomaterial, and the nanomaterial has high sensitivity and stability.
[0018] The AI analysis module comprises a neural network and a genetic algorithm, and the neural network is composed of neurons and neuron connections.
[0019] The personalized pain management module adopts a reinforcement learning algorithm.
[0020]
[0021] Preferably, the doctor / patient interface includes a natural language processing part with semantic understanding and sentiment analysis functions, and a speech recognition and synthesis part.
[0022] Preferably, a tumor pain management method based on artificial intelligence technology, the method comprising the following steps: Sp1, real-time monitoring of physiological and biochemical parameters of patients through biosensor module; Sp2, analyzing the monitoring data through AI analysis module; Sp3, according to the analysis result, formulating a personalized pain management plan through the personalized pain management module; Sp4, interacting with doctors and patients through the doctor / patient interface.
[0023] The monitoring data of the biosensor module is encrypted and compressed through the wireless data transmission part, and then transmitted to the AI analysis module.
[0024] The personalized pain management plan adopts reinforcement learning algorithm.
[0025] The doctor / patient interface understands the needs of doctors and patients through the natural language processing part, and interacts with doctors and patients through the speech recognition and synthesis part.
[0026] The data storage module adopts a safe data storage and access mechanism to ensure the security and privacy of data.
[0027] The core of the biosensor module is a gold nanoparticle biosensor based on the principle of surface plasmon resonance, integrated with a micro-needle array, to achieve continuous and quantitative monitoring of multiple biomarkers related to tumor pain. The specific scheme is as follows: The sensor selects spherical gold nanoparticles with a diameter of 50 nm, which are covalently bonded to the sensor chip substrate through a thiol-polyethylene glycol linker. The PEG linker can effectively prevent non-specific protein adsorption and improve the signal-to-noise ratio. At the end of the PEG chain, high-affinity monoclonal antibodies against a set of key biomarkers are fixed, including prostaglandin E2 (PGE2), bradykinin (reflecting inflammatory pain), substance P (reflecting neuropathic pain), and tumor necrosis factor-α and interleukin-6 (reflecting inflammation levels and tissue damage). Through a non-invasive micro-needle array (needle length about 500 microns), the sample is continuously pumped at a rate of 2 microliters per minute from the subcutaneous interstitial fluid of the patient to flow over the sensor surface. When the markers in the sample bind to the corresponding antibodies, it will cause a change in the refractive index of the sensor surface, resulting in a precise shift in the SPR absorption peak wavelength. A high-precision microspectrometer continuously monitors this wavelength shift at a frequency of 1 Hz, and calculates the dynamic concentration of various markers in real time through a pre-established calibration curve (mapping wavelength shift to concentration in ng / mL units). The built-in microcontroller (MCU) of the sensor is responsible for performing preliminary signal processing, including: 1) Signal denoising: A Kalman filter customized for this application is used. The state vector of this filter is defined as x = [c, Ac], where c is the marker concentration and Ac is its rate of change. Its process noise covariance matrix Q and measurement noise covariance matrix R are empirically set through statistical analysis of a large number of offline samples to optimally filter out physiological fluctuations and electronic noise.
[0028] 2) Data compression and transmission: The denoised multi-dimensional time series data is transmitted using the MQTT protocol optimized for the Internet of Things (IoT). Before publishing the message, the data is compressed using differential pulse code modulation (DPCM), only transmitting the difference between the current value and the Kalman filter prediction value, effectively reducing data load.
[0029] The entire communication link is enforced to be encrypted by TLS 1.3 protocol. Finally, the module outputs a JSON-formatted data stream with second-level precision to the AI analysis module, an example of the data packet is as follows: {"timestamp":"2025-05-26T10:00:01Z","device_id":"SN789-A","patient_id":"PID-00123","biomarkers":{"PGE2_ng_mL":1.52,"Bradykinin_ng_mL":0.88,"SubstanceP_ng_mL":2.15,"TNF-alpha_pg_mL":3.4,"IL-6_pg_mL":4.1}}; The AI analysis module employs a spatio-temporal hybrid neural network optimized by genetic algorithm, which integrates one-dimensional convolutional neural network (1D-CNN) and long short-term memory network (LSTM) for deep analysis of multi-dimensional time series data transmitted by the biosensor module. Its goal is to achieve accurate quantification and prediction of pain state. The specific scheme is as follows: the data received by the model is first preprocessed, using a 60-minute sliding window with a step of 1 minute, the continuous data stream is divided into a tensor like (n_samples, 60, 5), where 60 is the time step and 5 is the number of biomarkers, and each biomarker sequence is standardized by Z-score. The tensor is first input to a 1D-CNN layer, which contains 64 convolution kernels with a size of 3 and ReLU activation function, which automatically extracts spatial features such as "synergistic patterns" or "antagonistic patterns" formed by different biomarker combinations at each time point. The feature sequence extracted by CNN is then sent to an LSTM network containing two layers of stacking, each with 128 units. The LSTM network is responsible for learning the evolution law of these feature patterns over time and capturing the long-term dependence of pain development. Finally, a fully connected layer with a 40% Dropout rate maps the output of the LSTM to three targets: 1) Pain level prediction (continuous value from 0 to 10); 2) Probability of acute pain attack within the next 1 hour (probability value from 0 to 1); 3) Pain attribution analysis (a 5-dimensional vector representing the contribution weight of each biomarker to the current pain state).
[0030] To achieve optimal model performance, all hyperparameters of the network (including the number of filters in the CNN, the number of LSTM units, the learning rate, the dropout rate, etc.) are globally optimized using a genetic algorithm. The GA's chromosomes encode this set of hyperparameters, and the fitness function is defined as Fitness = 1 / (w_1*RMSE_level + w_2*BCE_outbreak), where RMSE is the root mean square error of pain level prediction, BCE is the binary cross-entropy loss of pain outbreak prediction, and w_1 and w_2 are weight coefficients. The GA population size is set to 50, with 100 generations of iteration. An elitist retention strategy, simulated binary crossover (SBX), and multinomial mutation are employed to ultimately select the optimal neural network architecture. This module receives a JSON data stream from the biosensing module, processes it, and outputs a JSON object containing deep analysis results, for example: {"timestamp":"2025-05-26T10:01:00Z","analysis_id":"ANA-98765","patient_id":"PID-00123","pain_prediction":{"level":7.8,"level_unit":"NRS-11","outbreak_prob_1hr":0.91},"attribution":{"PGE2":0.45,"Bradykinin":0.10,"SubstanceP":0.35,"TNF-alpha":0.05,"IL-6":0.05}}, This result was distributed to the personalized pain management module and the data storage module; The personalized pain management module employs a Deep Q-Network (DQN) reinforcement learning algorithm to dynamically generate optimal personalized pain management strategies for each patient through continuous online learning. Its core is learning an optimal action-value function Q∗(s,a) to select action a for a given patient state s that maximizes long-term cumulative reward.
[0031] The specific scheme is as follows: State (s) is defined as a high-dimensional vector that comprehensively describes the patient's current situation, including: all information output by the AI analysis module (predicted pain level, outbreak probability, contribution of each marker), current medication regimen (e.g., current dose of morphine sustained-release tablets, gabapentin dose, time since last administration), and the patient's subjective feelings reported through the interface (e.g., pain type, emotional state). The Action (a) space is a predefined discrete set containing all clinically feasible interventions, encoded as integers, such as 0: remain unchanged, 1: increase morphine sustained-release tablet dose by 5mg, 2: decrease morphine sustained-release tablet dose by 5mg, 3: administer 5mg immediate-release morphine for outbreak pain, 4: suggest 30 minutes of mindfulness meditation, 5: suggest using cold / hot compresses. The Reward (r) function is carefully designed to balance efficacy and safety. ,in It is the predicted pain level for the next state. It is an indicator function that indicates whether breakthrough pain has occurred. This indicates whether an action was taken to increase the dosage of opioids (used to punish overuse). This is an indicator function representing whether pain is well controlled (providing continuous positive feedback), where each w is an adjustable weight coefficient. The Q-network of DQN is a fully connected neural network with three hidden layers (256, 128, and 64 neurons respectively), using the ReLU activation function. The input state s outputs a Q-value estimate for each possible action a. The training process employs empirical replay (with a buffer size of 1,000,000) and a fixed Q-objective (updating the target network every 1000 steps) to ensure learning stability. Action selection follows... Strategies, in which The value decreases linearly from 1.0 to 0.01 to balance exploration and utilization. This module receives JSON analysis results from the AI analysis module, retrieves the current treatment plan from the data storage module to construct the state, and finally outputs a specific, executable JSON-formatted instruction set to the doctor-patient interface, for example: {"action_id":"ACT-12345","patient_id":"PID-00123","recommended_action":{"code":3,"description":"Administered 5mg of immediate-release morphine for breakthrough pain","rationale":"High probability breakthrough pain warning, PGE2 and substance P levels rise sharply"},"urgency":"High"}; Doctor / patient interface: Adopting microservice architecture, the backend core is a service that integrates natural language processing (NLP) and speech synthesis / recognition. The specific solution is as follows: the NLP service is based on a ClinicalBERT model that is pre-trained on a large medical corpus (such as MIMIC-III) and fine-tuned on a tumor pain dialogue dataset. This model can perform two tasks simultaneously: 1) intent recognition, which classifies user input (such as "I have severe abdominal pain, and the medicine seems ineffective") into predefined intents such as "reporting pain exacerbation", "asking about drug side effects", "requesting adjustment plan", etc.; 2) entity extraction, which accurately extracts key entities such as pain location ("abdomen"), pain nature ("severe"), and involved drugs ("medicine"). For voice interaction, the system deeply integrates Google Cloud Speech-to-Text and Text-to-Speech APIs. The front-end application sends the captured microphone audio stream to the Speech-to-Text API for real-time recognition, and the returned text is processed by ClinicalBERT. When the system generates a response, it uses the Text-to-Speech API and **Speech Synthesis Markup Language (SSML)** to control the pitch, speed, and emotion of the output voice. For example, when conveying bad news or warnings, the <prosody rate="slow"> tag is used to slow down the speech speed; when providing encouraging advice, a more positive tone may be chosen. The complete interaction process is as follows: the patient speaks their needs through the App -> the audio stream is converted into text in real-time -> ClinicalBERT processes the text and outputs a JSON object containing intent and entity {'intent':'report_inefficacy', 'entities': {'location': 'abdomen','severity': 'high'}} -> the JSON triggers the backend application logic to query relevant modules -> the system integrates information from various modules to generate a natural language reply (for example: "Received, the system has recorded your severe abdominal pain and analyzed your P substance level anomaly. A revised recommendation has been sent to your attending physician for review, which is 'increase gabapentin dosage by 100mg'. Before the doctor confirms, you can try a 15-minute guided breathing exercise.") -> the text is displayed on the UI and also converted into empathetic voice through TTS API to play to the patient; Data storage module: A high-availability and scalable MongoDB sharded cluster is used as the main database to natively support and efficiently process semi-structured JSON data from various modules. To ensure the highest level of data security and privacy compliance (following regulations such as HIPAA), the system implements client-side field-level encryption and strict access control based on OAuth2.0 / JWT. The specific solutions are as follows: In the database schema, a core patients collection stores all patient information. Within each patient document, sensitive data such as name, contact information, and all nested physiological and biochemical parameters, analysis results, and treatment plan details are automatically encrypted on the application server before being written to the database. This is achieved through the MongoDB driver, which interacts with a separate key management service (KMS) such as AWS KMS or HashiCorp Vault. The application layer never touches the master key and only requests a data encryption key (DEK) for specific data from the KMS, using the AES-256-GCM algorithm to complete the encryption of specific fields. The database itself only stores ciphertext and encryption metadata, so even if the database is compromised, the data itself is secure. In terms of access control, all requests to the backend API must pass through an API gateway. The doctor or patient client first requests a JSON Web Token (JWT) from an independent authentication server through two-factor authentication (2FA). The payload of this JWT contains detailed claims, such as: {'sub':'doctor_id_123','iss':'auth.pain_system.com','exp':1748239482,'scope':'read:patient_PID-00123write:patient_PID-00123_interventions'}, which precisely defines the identity of the token holder, the scope of permissions (which data of a specific patient can be read and written), and the validity period. The API gateway verifies the signature and claims of each incoming request's JWT, and only legitimate requests are routed to the corresponding backend microservices. All data access, including successful reads and failed attempts, is recorded in a dedicated, tamper-proof audit log (such as writing to an AWS SQL DB) for security auditing and tracking; The biosensor module first monitors the patient's physiological and biochemical parameters in real time, and then transmits the monitoring data to the AI analysis module through the wireless data transmission part; The AI analysis module receives the data, analyzes it through a neural network, and then optimizes the neural network parameters using a genetic algorithm to obtain the analysis results; The personalized pain management module formulates a personalized pain management plan using reinforcement learning algorithm according to the analysis result of the AI analysis module; The doctor / patient interface displays the personalized pain management plan to the doctor and patient in the form of natural language and / or voice; The data storage module stores the physiological and biochemical parameters of the patient, the analysis result and the pain management plan, so as to facilitate subsequent data analysis and research. Specific embodiment two: Further disclosed technical solutions: The biosensor module uses a new type of biosensor that utilizes the properties of nanomaterials, such as high surface activity and specific biomarker binding ability, to achieve real-time monitoring of biomarkers. This sensor achieves specific detection of different biomarkers by changing the type and structure of nanomaterials. Gold nanoparticles are used as nanomaterials, and by changing the shape and size of gold nanoparticles, specific detection of different biomarkers is achieved. The wireless data transmission part uses the latest wireless communication technology, such as 5G or 6G, to achieve high-speed and low-latency data transmission. The AI analysis module uses neural networks and genetic algorithms, two of the latest artificial intelligence technologies. Neural networks simulate the way the human brain works by learning patterns and rules in data through training. Deep learning networks such as convolutional neural networks (CNN) or recurrent neural networks (RNN) are used to analyze complex biological data. Genetic algorithms simulate the process of natural selection and inheritance by optimizing neural network parameters to improve neural network performance. Genetic algorithms are used to optimize neural network weights and biases to optimize neural networks. The personalized pain management module uses reinforcement learning algorithms, a new type of machine learning algorithm that learns the optimal strategy through interaction with the environment. In this module, the environment is the patient's body state, and the strategy is the pain management plan. Reinforcement learning algorithms such as Q-learning or Deep Q Network (DQN) are used to learn the most suitable pain management plan for the patient through continuous trial and error. The doctor / patient interface module uses natural language processing and speech recognition and synthesis technologies, two of the latest artificial intelligence interaction technologies. Natural language processing technology understands and generates natural language to achieve natural language interaction with users. Models such as BERT or GPT are used to understand and generate natural language. Speech recognition and synthesis technology converts speech into text or text into speech to achieve voice interaction with users. Google's speech recognition and synthesis API is used to achieve high-quality voice interaction. The data storage module uses a new type of data storage and access mechanism that ensures data security and privacy through data encryption and access control. Encryption algorithms such as AES or RSA are used to encrypt data, and access control mechanisms such as OAuth or JWT are used to securely access data. Specific embodiment three: Further disclosed technical solutions: The biological sensing module first monitors the patient's physiological and biochemical parameters in real time through the nano-biosensor, which may include pain-related biomarkers such as inflammatory factors and neurotransmitters. The monitoring data is encrypted and compressed by the wireless data transmission part and transmitted to the AI analysis module. The AI analysis module receives the data from the biological sensing module and starts data analysis. First, the neural network learns the biological data deeply and extracts the patterns and rules in the data. Then, the genetic algorithm optimizes the parameters of the neural network to improve the performance of the neural network. Finally, the AI analysis module transmits the analysis results to the individualized pain management module. The individualized pain management module receives the analysis results from the AI analysis module and starts to develop individualized pain management plans. This module uses reinforcement learning algorithms to learn the optimal strategy through interaction with the environment. In this process, the environment is the patient's body state, and the strategy is the pain management plan. The individualized pain management module transmits the developed pain management plan to the doctor / patient interface. The doctor / patient interface receives the pain management plan from the individualized pain management module and starts to interact with the doctor and patient. This interface uses natural language processing technology to understand the needs of the doctor and patient, and then uses voice recognition and synthesis technology to interact with the doctor and patient. The doctor and patient can view the pain management plan through this interface and also make suggestions for modification. The data storage module is responsible for storing all data throughout the process, including the patient's physiological and biochemical parameters, the AI analysis module's analysis results, and the individualized pain management module's pain management plan. This module uses secure data storage and access mechanisms to ensure data security and privacy.
[0034] Hardware and software of the individualized pain management module, Hardware components: Computing device: This is the core part of the individualized pain management module, which needs to have enough computing power to run complex reinforcement learning algorithms. This device is a high-performance server or a workstation equipped with professional AI chips. It uses a server equipped with NVIDIA's Tesla or Ampere series GPU, or a workstation equipped with Google's TPU.
[0035] Control device: This device is used to execute the pain management plan developed by the computing device. It is a smart drug pump or a smart electric stimulation device. It uses a smart drug pump equipped with a wireless communication module or a smart electric stimulation device equipped with a wireless communication module.
[0036] Communication device: This device is used to realize the communication between the computing device and the control device, which is a wireless router and a mobile communication base station, using a wireless router supporting 5G or 6G, or using a mobile communication base station supporting 5G or 6G.
[0037] Hardware flow: After the computing device receives the analysis results of the AI analysis module, it starts to run the reinforcement learning algorithm and formulates a personalized pain management plan.
[0038] The computing device sends the formulated pain management plan to the control device through the communication device.
[0039] After the control device receives the pain management plan, it starts to execute the plan. If the plan is to manage pain through medication, the intelligent drug pump will adjust the dosage and injection time of the drug according to the plan; if the plan is to manage pain through electrical stimulation, the intelligent electrical stimulation device will adjust the intensity and frequency of electrical stimulation according to the plan.
[0040] Further scheme of the control device: The control device plays a crucial role in the personalized pain management system, and it is responsible for executing the pain management plan formulated by the AI analysis module and the personalized pain management module. The following is a detailed technical exposition of the control device: Intelligent drug pump: The intelligent drug pump is a device that can accurately control the delivery of drugs. It can accurately control the dosage and injection time of drugs according to the instructions of the pain management plan. The intelligent drug pump usually includes a drug storage part, a pump part, and a control part. The drug storage part is used to store drugs, the pump part is used to deliver drugs from the storage part to the patient's body, and the control part is used to control the work of the pump part. The control part usually includes a microprocessor and a wireless communication module. The microprocessor is used to process instructions from the personalized pain management module, and the wireless communication module is used to receive and send data.
[0041] Intelligent electrical stimulation device: The intelligent electrical stimulation device is a device that can accurately control electrical stimulation. It can accurately control the intensity and frequency of electrical stimulation according to the instructions of the pain management plan. The intelligent electrical stimulation device usually includes a power supply part, a stimulation part, and a control part. The power supply part is used to provide electrical energy, the stimulation part is used to convert electrical energy into electrical stimulation, and the control part is used to control the work of the stimulation part. The control part usually includes a microprocessor and a wireless communication module. The microprocessor is used to process instructions from the personalized pain management module, and the wireless communication module is used to receive and send data. Specific embodiment four: Further disclosed technical scheme: The encryption communication protocol uses the TLS (Transport Layer Security) protocol, which is a secure protocol that encrypts network connections at the transport layer to ensure the security and integrity of data during transmission. TLS supports multiple encryption algorithms, such as RSA, AES, and 3DES, and selects the appropriate encryption algorithm based on the needs. The data transmission method uses wireless data transmission, specifically, the biosensor module sends the monitored physiological and biochemical parameters to the AI analysis module through a wireless network, and the AI analysis module analyzes these parameters and sends the analysis results to the individualized pain management module and the doctor / patient interface. Wireless data transmission enables real-time and fast data transmission, improving the efficiency of the system. Data interaction uses RESTful API, which is an API design style based on HTTP protocol that supports multiple data formats, such as JSON and XML, to achieve efficient data interaction between modules. Specifically, the biosensor module, AI analysis module, individualized pain management module, and doctor / patient interface will provide a set of RESTful APIs, and other modules can access or modify data by calling these APIs. Data security uses multiple security measures to ensure data security, such as using TLS protocol to encrypt data to prevent data from being stolen or tampered during transmission, desensitizing sensitive data to prevent privacy issues caused by data leakage, and using secure data storage and access mechanisms, such as database encryption and access control, to ensure data security during storage and access.
[0043] It should be noted that, in this text, relational terms such as first and second are only used to distinguish one entity or operation from another, and do not necessarily require or imply any actual relationship or order between the entities or operations. Moreover, the term "includes", "contains" or any other variant thereof is intended to cover non-exclusive inclusion, so that the process, method, article or device including a series of elements not only includes those elements, but also includes other elements not explicitly listed or inherent to such process, method, article or device. Without more limitations, the element defined by the statement "includes a reference structure" does not exclude the presence of other identical elements in the process, method, article or device including the element.
[0044] Although embodiments of the present application have been shown and described, those of ordinary skill in the art will understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.
Claims
1. An artificial intelligence technology-based tumor pain management system, characterized by, Comprise: a biosensing module for real-time physiological and biochemical monitoring; an AI analysis module for analyzing data from the biosensing module and providing analysis data; a personalized pain management module for terminal control of the analysis data from the AI analysis module; a doctor / patient interface for terminal control by doctors and patients; a data storage module for storing physiological and biochemical parameters, analysis results, and pain management plans of patients; Data transmission and interaction between the biosensing module, AI analysis module, personalized pain management module, and doctor / patient interface are through an encrypted communication protocol.
2. The artificial intelligence technology-based tumor pain management system according to claim 1, characterized in that, The biosensing module includes a nano biosensor and a wireless data transmission unit, and the nano biosensor uses molecular arrangement nanomaterials.
3. The artificial intelligence technology-based tumor pain management system according to claim 1, characterized in that, The AI analysis module includes a neural network and a genetic algorithm, and the neural network is composed of neurons and neuron connections.
4. The artificial intelligence technology-based tumor pain management system according to claim 1, characterized in that, The personalized pain management module uses a reinforcement learning algorithm.
5. The artificial intelligence technology-based tumor pain management system according to claim 1, characterized in that, The doctor / patient interface includes a natural language processing part and a speech recognition and synthesis part, and the natural language processing part includes semantic understanding and sentiment analysis.
6. The system of claim 1, wherein, The data storage module uses an encrypted data storage and access mechanism. 7.A method for tumor pain management based on artificial intelligence technology, characterized in that, The method comprises the following steps: Sp1, real-time monitoring of physiological and biochemical parameters of patients through the biosensing module; Sp2, analyzing monitoring data through the AI analysis module; Sp3, developing a personalized pain management plan through the personalized pain management module based on the analysis results; Sp4, interacting with doctors and patients through the doctor / patient interface.
8. The artificial intelligence technology-based tumor pain management method according to claim 7, characterized in that, The monitoring data of the biosensing module is encrypted and compressed through the wireless data transmission part before being transmitted to the AI analysis module.
9. The artificial intelligence technology-based tumor pain management method according to claim 7, characterized in that, The personalized pain management plan uses a reinforcement learning algorithm.
10. The tumor pain management method based on artificial intelligence technology according to claim 7, characterized by, The doctor / patient interface understands the needs of doctors and patients through the natural language processing part and interacts with them through the speech recognition and synthesis part.