Blockchain-based Remote Intelligent Disease Diagnosis and Treatment System and Method

Abstract

This invention relates to the field of intelligent diagnosis and treatment technology, and discloses a remote intelligent disease diagnosis and treatment system and method based on blockchain. The method includes collecting multimodal physiological data such as real-time vital signs, historical medical records, and medical images of patients and generating encrypted data packets. This data is then distributed and stored via a blockchain node network, generating data hash values ​​and timestamps. A smart contract is invoked to extract cross-modal features, generating a standardized feature vector set containing multi-feature association identifiers. Based on this, a diagnosis and treatment decision tree that can dynamically adjust branch paths is constructed. The validity of the decision is verified through a consensus mechanism, generating a treatment instruction sequence containing drug preparation, equipment operation, and follow-up cycles. Feedback data is monitored in real time and updated to the blockchain. When deviations exceed a threshold, a smart contract is triggered to generate a correction scheme, writing the discrepancy data as a new transaction record to the blockchain, thus achieving secure multimodal data management and dynamic, precise diagnosis and treatment.

Description

Technical Field

[0001] This invention relates to the field of intelligent diagnosis and treatment technology, specifically to a remote intelligent disease diagnosis and treatment system and method based on blockchain. Background Technology

[0002] With the rapid development of medical informatization, remote disease diagnosis and treatment, by overcoming the limitations of time and space, has become an important way to solve the uneven distribution of medical resources and improve the accessibility of medical services. In the scenario of remote diagnosis and treatment, patients' physiological data are multi-sourced and heterogeneous, covering real-time dynamic vital signs data, structured historical medical record text data, and unstructured medical imaging data, among other multimodal information. This data is the core foundation for achieving accurate diagnosis and treatment.

[0003] However, the current telemedicine system still faces many pressing problems. Regarding data security and management, traditional centralized data storage models pose significant security risks. Multimodal physiological data contains a large amount of patient privacy information, making centralized storage vulnerable to hacker attacks, data tampering, or leakage. Furthermore, inconsistent data formats across different medical institutions create "data silos," hindering cross-institutional data sharing and collaborative diagnosis. At the data processing and feature extraction level, the temporal dynamism, spatial correlation, and semantic complexity of cross-modal data present significant challenges to feature fusion. Existing methods often struggle to effectively uncover the inherent relationships between different types of data, resulting in extracted features lacking standardization and comprehensiveness, thus failing to provide reliable support for diagnostic and treatment decisions.

[0004] In terms of diagnostic and treatment decision-making mechanisms, traditional models often employ fixed-structure decision logic, making it difficult to dynamically adjust decision paths based on changes in real-time patient physiological data. This makes them ill-suited to the dynamic evolution of the disease, easily leading to delays or limitations in treatment plans. Furthermore, the lack of a unified, authoritative mechanism for validating the effectiveness of treatment plans results in varying levels of acceptance among different medical entities, potentially causing irregularities in treatment practices. Simultaneously, the failure to promptly and effectively process and utilize patient feedback data after implementing treatment plans hinders rapid optimization and adjustments, impacting the continuous improvement of treatment outcomes. These issues collectively restrict the accuracy, safety, and efficiency of remote disease diagnosis and treatment, necessitating the development of a novel technological system to overcome these challenges. Summary of the Invention

[0005] The purpose of this invention is to provide a blockchain-based remote intelligent disease diagnosis and treatment system and method to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides a blockchain-based remote intelligent disease diagnosis and treatment method, the method comprising:

[0007] Collect patients' multimodal physiological data and generate encrypted data packets. The multimodal physiological data includes real-time vital signs data, historical medical record text data, and medical image data.

[0008] The encrypted data packets are distributed and stored through a blockchain node network, and data hash values ​​and timestamps are generated on each node.

[0009] The smart contract is invoked to extract cross-modal features from the encrypted data packet and generate a standardized feature vector set, which includes the association identifiers of temporal features, spatial features and semantic features.

[0010] A dynamic diagnosis and treatment decision tree is constructed based on the standardized feature vector set, and the dynamic diagnosis and treatment decision tree dynamically adjusts the branch paths according to the feature weights.

[0011] The validity of the nodes in the dynamic diagnosis and treatment decision tree is verified through a consensus mechanism, and the verified decision paths are marked as executable treatment plans to generate a treatment instruction sequence, which includes drug configuration parameters, device operation steps and follow-up period.

[0012] Real-time monitoring of feedback data from the patient's execution of the treatment instruction sequence, and updating to the blockchain node network;

[0013] When the deviation between the feedback data and the expected therapeutic effect exceeds a threshold, the smart contract is triggered to regenerate a corrected treatment plan.

[0014] The difference between the revised treatment plan and the original treatment plan is written into the blockchain as a new transaction record.

[0015] Preferably, the step of invoking the smart contract to perform cross-modal feature extraction on the encrypted data packet includes:

[0016] The real-time vital signs data in the encrypted data packet are decomposed into equally spaced time segments, and the mean, variance and peak frequency of each segment are calculated.

[0017] Keyword entity recognition and contextual relationship parsing are performed on the historical medical record text data to generate a structured semantic graph;

[0018] Extract the grayscale distribution matrix and edge gradient features of the medical image data to construct a three-dimensional spatial feature vector;

[0019] The temporal segment features, structured semantic graphs, and three-dimensional spatial feature vectors are aligned along the time axis and then fused into a cross-modal correlation matrix.

[0020] Preferably, the step of constructing a dynamic diagnosis and treatment decision tree based on the standardized feature vector set includes:

[0021] The initial decision nodes are determined based on the Euclidean distance between the eigenvectors in the cross-modal correlation matrix.

[0022] Calculate the mutual information entropy between each feature vector and the disease classification label, and dynamically allocate the weight coefficients of the decision branches;

[0023] When new feedback data is added, the splitting priority of the decision node is adjusted based on the weight coefficient.

[0024] Preferably, the step of verifying the node validity of the dynamic diagnosis and treatment decision tree through a consensus mechanism includes:

[0025] Broadcast the node hash value of the decision path to a preset number of verification nodes;

[0026] Each verification node compares the matching degree between the locally stored feature vector and the node hash value;

[0027] When the matching degree exceeds the preset ratio, the node is marked as a valid node and a digital signature is generated.

[0028] Preferably, the generation of the treatment instruction sequence includes:

[0029] Analyze the disease staging markers and complication risk levels in the executable treatment plan;

[0030] Match treatment templates from a pre-defined clinical guideline library to generate a basic instruction set;

[0031] Prioritize and calibrate the basic instruction set based on individual patient differences.

[0032] Preferably, the feedback data from the real-time monitoring of the patient's execution of the treatment instruction sequence includes:

[0033] Collect data on the changes in the concentration of metabolites and the fluctuations in physiological indicators after patients take medication.

[0034] The mechanical parameters and time intervals during the operation of the instrument are recorded using IoT devices;

[0035] The concentration change curves of the metabolites, the fluctuation data of physiological indicators, and the mechanical parameters are packaged into a feedback data block.

[0036] Preferably, triggering the smart contract to regenerate the corrected treatment plan includes:

[0037] Compare the standard deviation and offset direction of the feedback data block with the expected therapeutic effect data;

[0038] When the standard deviation exceeds the threshold, the feature extraction module is invoked to recalculate the cross-modal correlation matrix;

[0039] The branch paths of the diagnosis and treatment decision tree are reconstructed based on the updated cross-modal association matrix.

[0040] Preferably, the step of writing the difference data between the revised treatment plan and the original treatment plan as a new transaction record into the blockchain includes:

[0041] Extract the change instructions and parameter adjustment amounts from the difference data;

[0042] Generate a transaction structure that includes a change timestamp, the executing physician's digital identity, and a differential hash value;

[0043] The transaction structure is written into a new block using a proof-of-work mechanism.

[0044] Preferably, the method further includes:

[0045] Regularly scan the treatment protocols for similar cases stored in the blockchain;

[0046] Cluster analysis was performed to study the distribution patterns of feature vectors in the treatment protocols for similar cases.

[0047] Optimize the feature weight allocation strategy in smart contracts based on distribution patterns.

[0048] Preferably, the present invention also includes a blockchain-based remote intelligent disease diagnosis and treatment system, the system including a processor and a memory, the memory storing a computer program, and the processor executing the computer program to implement the blockchain-based remote intelligent disease diagnosis and treatment method as described above.

[0049] Compared with the prior art, the beneficial effects of the present invention are:

[0050] By collecting multimodal physiological data from patients and generating encrypted data packets, comprehensive integration and secure encapsulation of different types of medical data are achieved, covering key information such as real-time vital signs, historical medical records, and medical images, ensuring the integrity of the data foundation upon which diagnostic and treatment decisions rely. The encrypted data packets are distributed and stored through a blockchain node network, generating data hash values ​​and timestamps. Leveraging the decentralized nature of blockchain, the security risks associated with centralized storage are avoided. Each node retains a copy of the data, preventing data loss or information leakage due to attacks on a single node. Furthermore, the hash values ​​and timestamps enable traceability throughout the entire data lifecycle, ensuring data authenticity and immutability, and providing a robust technical guarantee for patient privacy protection.

[0051] By invoking smart contracts to extract cross-modal features and generate a standardized set of feature vectors, this approach integrates the association identifiers of temporal, spatial, and semantic features. The automated execution of smart contracts ensures the efficiency and consistency of the feature extraction process, effectively overcoming the bottlenecks of inconsistent formats and insufficient association mining in traditional cross-modal data processing. This fully releases the value of different types of data, providing standardized and comprehensive feature support for subsequent diagnostic and treatment decisions. The dynamic diagnostic and treatment decision tree constructed based on this standardized feature vector set can dynamically adjust branch paths according to feature weights, accurately matching the individual physiological characteristics and disease changes of patients. This overcomes the limitations of traditional fixed-structure decision models, making treatment plans more personalized and adaptable, and better able to address the differentiated conditions and dynamic evolution of diseases in different patients.

[0052] The validity of nodes in the dynamic treatment decision tree is verified through a consensus mechanism. Leveraging distributed consensus among blockchain nodes, the authority and reliability of treatment plans are ensured, avoiding potential biases arising from subjective judgment by a single medical entity and making the generated treatment instruction sequence more scientific. The treatment instruction sequence clearly includes specific details such as drug configuration parameters, instrument operation procedures, and follow-up cycles, providing clear guidance for the standardized execution of remote treatment and facilitating accurate implementation of treatment plans by patients or primary healthcare personnel.

[0053] Real-time monitoring of patient feedback data on the execution of treatment instructions and updating it to the blockchain node network creates a closed-loop management system for the treatment process. When the deviation between the feedback data and the expected therapeutic effect exceeds a threshold, the smart contract is triggered to regenerate a revised treatment plan, achieving dynamic optimization of the plan. This allows for timely responses to changes in the patient's condition, preventing poor treatment outcomes due to outdated plans. The difference between the revised and original treatment plans is written into the blockchain as a new transaction record, further improving the traceability chain of the treatment process. This provides complete data for subsequent medical quality assessments and summaries of treatment experience, and lays the data foundation for cross-institutional medical collaboration and academic exchange, promoting the development of the telemedicine system towards greater standardization, precision, and efficiency. Simultaneously, the decentralized nature of blockchain provides a foundation of trust for collaborative treatment between different medical institutions, breaking down data silos, promoting the efficient flow and sharing of medical resources, and improving the overall quality and accessibility of telemedicine services. Attached Figure Description

[0054] Figure 1 This is a schematic diagram illustrating the working principle of the blockchain-based remote intelligent disease diagnosis and treatment method described in this invention.

[0055] Figure 2 A flowchart for calling a smart contract to perform cross-modal feature extraction;

[0056] Figure 3 A flowchart for real-time monitoring of patient feedback data. Detailed Implementation

[0057] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0058] Please see Figure 1 This invention provides a blockchain-based remote intelligent disease diagnosis and treatment method. The method includes: achieving secure and reliable remote diagnosis and treatment by integrating multimodal data acquisition, blockchain storage, smart contract analysis, and dynamic decision optimization. The method begins with the acquisition of multimodal physiological data from the patient, including real-time vital signs data, historical medical record text data, and medical imaging data. Acquisition devices, such as wearable sensors, electronic health record systems, and medical imaging instruments, work synchronously to encrypt the raw data and package it into an encrypted data packet. The encryption process uses an asymmetric encryption algorithm to ensure data privacy. The encrypted data packet is then uploaded to a blockchain node network, which consists of multiple distributed nodes. Each node independently stores a copy of the encrypted data packet and calculates a unique data hash value, while also attaching an immutable timestamp. The smart contract is automatically invoked to decrypt the encrypted data packet and extract cross-modal features, outputting a standardized feature vector set. This standardized feature vector set contains association identifiers for temporal, spatial, and semantic features. Based on the standardized feature vector set, a dynamic diagnosis and treatment decision tree is constructed. The branch paths of the decision tree are dynamically adjusted according to the weights of the feature vectors to reflect changes in the patient's state. The consensus mechanism verifies the validity of each node in the dynamic treatment decision tree. Verified decision paths are marked as executable treatment plans, and a treatment instruction sequence is generated, including drug configuration parameters, device operation steps, and follow-up periods. Feedback data from patients executing the treatment instruction sequence is monitored in real time, including changes in physiological indicators and operation records. This data is updated to the blockchain node network. When the deviation between the feedback data and the expected therapeutic effect exceeds a preset threshold, the smart contract triggers the regeneration of a revised treatment plan. The difference between the revised treatment plan and the original treatment plan is written to the blockchain as a new transaction record, ensuring the transparency and traceability of the treatment process.

[0059] Example 1: See Figure 2Real-time vital sign data in encrypted data packets is decomposed into equally spaced time segments. The division of these segments is based on the inherent sampling frequency of the data acquisition equipment; for example, millisecond-level continuous waveform data from an electrocardiogram monitor is divided into segments of fixed duration for analysis. Each time segment requires calculation of several statistical characteristics, including its mean, variance, and peak frequency. The mean reflects the baseline level of the physiological signal within that time period, the variance characterizes the signal's fluctuation range and stability, and the peak frequency identifies the dominant rhythm or periodic pattern from a frequency domain perspective using Fast Fourier Transform. These features collectively constitute a quantitative description of the dynamic changes in vital signs. Historical medical record text data undergoes keyword entity recognition and contextual relationship parsing. The entity recognition process utilizes a pre-trained medical named entity recognition model to extract key medical terms from unstructured text. These terms include disease names, drug names, clinical symptoms, and examination indicator values. Contextual relationship parsing establishes semantic associations between extracted entities using dependency parsing techniques. For example, it determines the therapeutic relationship between a drug and a specific symptom, or the supporting relationship between a test result and a diagnosis, thereby generating a structured semantic graph with entities as nodes and semantic relationships as edges. Medical image data processing focuses on extracting its grayscale distribution matrix and edge gradient features. The grayscale distribution matrix is ​​obtained by normalizing the intensity values ​​of image pixels and then re-gridning them, reflecting the density differences of tissues or organs. Edge gradient features are calculated by applying edge detection operators such as Sobel or Canny to convolve the images, outlining the contours and boundaries of anatomical structures. Based on these fundamental features, a feature vector that expresses the three-dimensional spatial relationships of regions of interest in the image can be further constructed.

[0060] Feature sets from different modalities, including temporal segment features, structured semantic graphs, and 3D spatial feature vectors, need to be aligned along the time axis. This alignment process addresses the differences in timestamp precision and acquisition intervals between data from different sources, employing linear interpolation or nearest neighbor interpolation algorithms to unify non-uniformly sampled data sequences to a consistent time point. The aligned multimodal data is then fused into a cross-modal association matrix. This fusion process involves complex tensor operations, such as performing an outer product operation on temporal feature vectors, semantic graph embedding vectors, and spatial feature vectors to form a high-order tensor. Tensor decomposition or expansion techniques are then used to reduce the dimensionality to a comprehensive two-dimensional matrix. The rows of this cross-modal association matrix typically correspond to different feature dimensions, while the columns correspond to the unified time points. Each element in the matrix not only contains feature information from a single modality but also encodes the association strength between features from different modalities at the same time. The cross-modal association matrix constitutes the core data structure of the standardized feature vector set, which facilitates efficient processing by subsequent machine learning models.

[0061] The entire feature extraction process is encoded within the logic of a smart contract. When the smart contract is triggered and executed on a blockchain node, it invokes the computing resources deployed on the node to complete the complex feature engineering tasks described above. The feature extraction process strictly adheres to internationally recognized medical data standards, such as the Health Rank 7 standard and medical digital imaging and communication standards, to ensure the interoperability of the generated feature vectors across different systems. The original encrypted data packets used for feature extraction are temporarily decrypted within the smart contract. After the feature extraction task is completed, the generated intermediate data and final feature vectors are encrypted again, just like the original data. The encrypted data packets are then rewritten into the blockchain's distributed storage to prevent any unauthorized data access. The cross-modal association matrix is ​​not a static data structure. When new patient data is added to the system through the blockchain node network, the cross-modal association matrix is ​​updated using an incremental learning algorithm. This algorithm only calculates feature changes based on the new data and adjusts the corresponding parts of the matrix, avoiding the resource consumption of recalculating the entire historical dataset. This mechanism maintains the consistency of feature representations when faced with new information. The execution of the smart contract is atomic. The success or failure of the feature extraction process is recorded as a complete transaction on the blockchain, ensuring the reliability and auditability of the data processing process. The quality of feature vector generation directly affects the accuracy of subsequent diagnostic and treatment decisions. Therefore, a feature quality verification module is integrated into the smart contract. The feature quality verification module checks the valid range of feature values, whether there are a large number of missing values, and whether the correlation between features is reasonable. If the feature generation is unqualified, a recalculation process will be triggered.

[0062] Example 2: The construction of a dynamic diagnostic decision tree begins with a deep analysis of the cross-modal correlation matrix in the standardized feature vector set. This matrix contains the complex intrinsic relationships between the patient's multimodal physiological data. The Euclidean distance between the feature vectors is precisely calculated. Euclidean distance measures the relative positional relationship of different feature points in multidimensional space; feature points with smaller distance values ​​are considered to have higher physiological similarity. The initial decision node division is completed based on a preset distance threshold, which is obtained through statistical analysis of the distance between the feature cluster centers of historical healthy and diseased populations. Each initial decision node represents a preliminary disease classification judgment or health status interval. The mutual information entropy between each feature vector and the known disease classification label is evaluated. Mutual information entropy quantifies the contribution of a single feature or feature combination to reducing diagnostic uncertainty; features with high mutual information entropy values ​​are considered to have stronger classification and discriminative abilities. The weight coefficients for dynamically allocating decision branches are based on the calculation results of mutual information entropy, and these weight coefficients directly determine the influence of features in the selection of decision tree branch paths. New feedback data flows into the system in real time from the blockchain node network, containing the patient's physiological response information to previous treatment instructions. The weight adjustment mechanism is activated, dynamically updating the weight coefficients based on the fit between the new data and the original feature vectors. Features with high weight coefficients that do not perform consistently with the new data will have their coefficients lowered, while features highly correlated with the new data will have their weights increased. The splitting priority of decision nodes is reordered based on the updated weight coefficients. The splitting priority determines the order in which features are selected during the decision tree growth process, with higher-priority branches representing the most likely treatment direction. The node hash values ​​of the decision paths are broadcast to a preset number of verification nodes. The node hash value is a unique digital fingerprint obtained by encrypting the feature vector data, weight coefficients, and node generation timestamp contained in the node using the SHA-256 algorithm.

[0063] Upon receiving the broadcast node hash value, each verification node initiates a local comparison process. The verification node retrieves its own stored, consensus-confirmed feature vector dataset and calculates the matching degree between its local feature vector and the feature vector described in the broadcast information. The matching degree calculation uses a cosine similarity algorithm, which focuses on measuring the directional differences between feature vectors, effectively avoiding interference from the absolute magnitude of the numerical values ​​in the similarity judgment. When the matching degree calculation result exceeds a preset percentage threshold (e.g., 80%), it means that most verification nodes agree on the validity of the decision node. This decision node is marked as a valid node, and valid nodes are the basic units constituting the final executable treatment plan. The digital signature generation process then begins. The verification node uses its private key to encrypt and sign the node hash value and the timestamp of successful verification, generating a unique digital signature that is attached to the valid node.

[0064] The binding information of digital signatures and valid nodes is broadcast back to the blockchain network for other nodes to record and verify. Decision nodes that fail to meet the preset matching ratio threshold are marked as invalid nodes. Invalid nodes will not enter the subsequent treatment plan construction process and trigger a partial reconstruction mechanism of the decision tree. The consensus mechanism uses a practical Byzantine fault-tolerant algorithm to coordinate the states between various verification nodes. The practical Byzantine fault-tolerant algorithm can tolerate a certain number of node failures or malicious behavior, still ensuring that the network reaches a reliable consensus on the validity of decision nodes. The update and verification process of the dynamic treatment decision tree forms a closed loop. New feedback data is continuously input, the decision tree structure is continuously fine-tuned, and node validity is verified cyclically, keeping the entire system dynamically evolving. The generation process of node hash values ​​includes an incrementing version number. The version number ensures that even if the feature vector data is the same but the generation time is different, the node hash value is unique, effectively preventing the reuse attack of old decision nodes. All digital signatures generated during the verification process are stored together in a chain. The digital signature chain clearly records the complete audit trajectory of each valid node from generation to verification, providing an immutable and traceable basis for treatment decisions. Invalid nodes and their corresponding feature vector data are temporarily isolated and archived. This isolated and archived data can be used for subsequent analysis of the reasons for disagreements in the decision-making model, providing a data foundation for optimizing feature extraction and decision-making algorithms. The verification status of the entire decision tree is recorded as a transaction in a new block of the blockchain. The immutability of the blockchain ensures the historical traceability of the diagnostic decision-making logic.

[0065] Example 3: See Figure 3The system parses the disease staging identifiers and complication risk levels in the executable treatment plan. The disease staging identifiers are machine-readable, strictly adhering to the coding system in the 11th revision of the International Classification of Diseases, dividing the patient's disease progression into discrete and clearly defined stages. The complication risk level is calculated using a multivariate assessment model, whose input parameters include patient age, genetic markers, organ function indicators, and key events in past medical history. A pre-defined clinical guideline library is queried by the system. This library is a read-only knowledge graph stored on the blockchain, containing evidence-based medicine guidelines published by authoritative medical associations. The process of matching treatment templates and generating a basic instruction set is a rule-based automated reasoning process. The system maps the parsed precise disease staging identifiers and quantified complication risk levels to diagnostic nodes and conditional paths in the knowledge graph to find the most suitable standardized treatment plan template. The basic instruction set includes the chemical drug name, administration method, initial dose range, physical therapy parameters, surgical procedure sequence, and a matrix of planned follow-up time intervals. Patient-specific parameters are integrated into the calibration algorithm. These parameters include real-time measured body surface area, creatinine clearance, drug-metabolizing enzyme genotypes, serum albumin levels, and patient-reported quality of life scores. Prioritizing and dose calibration of the basic instruction set requires the application of clinical decision support logic. Prioritization is based on a quantitative assessment of the potential benefit-risk ratio of treatment measures, while dose calibration relies on pharmacokinetic models to simulate in vivo exposure at different doses. The final generation of the treatment instruction sequence transforms abstract medical knowledge into a series of precise, executable machine instructions. The treatment instruction sequence is encoded using the HL7FHIR standard resource format to ensure seamless parsing and execution of instructions in heterogeneous hospital information systems. Drug configuration parameters specify the total amount of drug, single dose, administration rate, infusion carrier, and dosing interval. Device operation steps are decomposed into atomic operation units with strict order. The follow-up period defines the assessment time points, the types of biological samples to be collected, and the required imaging examinations.

[0066] Feedback data from the patient's execution of treatment instructions is collected via a heterogeneous Internet of Things (IoT) network. Metabolic product concentration changes after medication administration are continuously recorded by minimally invasive or non-invasive biosensors. These sensors package the detected specific metabolite concentration values ​​along with high-precision timestamps into data packets and send them to the gateway device. Fluctuations in physiological indicators, such as continuous arterial pressure, EEG spectrum, and respiratory waveforms, are recorded by bedside monitoring devices using high-frequency sampling. The built-in algorithms of these monitoring devices calculate the statistical characteristics of each indicator in real time. IoT devices synchronously record the mechanical parameters and time intervals during device operation. Mechanical parameters include the torque applied by the rehabilitation robot, the base rate and amplitude of the insulin pump, and the inspiratory-to-expiratory ratio setting of the ventilator. Time intervals precisely record the start time, duration, and stop time of each intervention. Metabolic product concentration changes, physiological indicator fluctuation data, and mechanical parameters are aggregated into a structured feedback data block. This feedback data block is encapsulated in a format conforming to the IEEE 11073 standard, balancing data exchange efficiency with semantic clarity. Before being transmitted over the network, feedback data blocks are appended with a digital signature based on an elliptic curve digital signature algorithm. This signature is generated using the private key of the data acquisition device to verify that the data has not been tampered with since acquisition and that its source is reliable. Feedback data blocks are transmitted in real-time to the blockchain node network via a dedicated medical IoT network with quality of service assurance. The transmission protocol prioritizes the Advanced Message Queuing Protocol, which ensures reliable message delivery. The monitoring process is designed for continuous operation. The execution status of the feedback data stream and the treatment instruction sequence is associated with a globally unique treatment event identifier. This treatment event identifier must be included in the feedback data block for accurate efficacy attribution analysis. An anomaly detection module is activated when data flows into the processing pipeline. This module uses a Z-score-based statistical process control chart method to dynamically calculate individualized normal ranges for each patient's physiological parameters. Any data point exceeding the control limits triggers an immediate alarm. The mapping relationship between feedback data and the treatment instruction sequence is recorded in a distributed mapping table, stored as key-value pairs on the blockchain's distributed ledger, providing irrefutable evidence of the association. Data packets remain encrypted throughout transmission. Application-layer encryption utilizes the public key of the target blockchain node to ensure data confidentiality across the transmission chain. The entire feedback data collection and transmission system is designed according to privacy principles, anonymizing data at its source to minimize the risk of patient privacy breaches. The successful reception and recording of a feedback data block on the blockchain network triggers a pre-defined state machine transition in the smart contract, updating the corresponding treatment instruction's status to "Executing - Data Returned," thus creating the necessary conditions for subsequent efficacy evaluation and treatment plan adjustments.The continuously accumulated feedback data forms a high-resolution health status trajectory curve over time. This health status trajectory curve is a valuable data asset for assessing the long-term effectiveness of treatment plans, identifying adverse drug reactions, and studying the natural history of diseases.

[0067] The drug dosage calibration process requires adjustments based on a specific pharmacokinetic model, the core formula of which is as follows:

[0068] ;

[0069] in: This represents the calculated calibrated drug dose. This represents the baseline drug dosage recommended by the baseline instruction set matched from a pre-defined clinical guideline library. This represents the target blood drug concentration that is expected to be achieved during treatment. This represents the blood drug concentration at the current dose predicted using a pharmacokinetic model based on individual patient differences. This represents the dose adjustment factor based on the risk level of complications. Its value is determined by a risk level lookup table, for example, 1.0 for low risk, 0.7 for medium risk, and 0.5 for high risk.

[0070] Example 4: The system compares the standard deviation and offset direction of the feedback data block with the expected efficacy data. The feedback data block includes metabolite concentration change curves, physiological index fluctuation data, and mechanical parameters collected from IoT devices. The expected efficacy data comes from an efficacy model library established based on large-scale clinical studies, which sets the expected physiological index change trajectory for each standard treatment regimen. The standard deviation is calculated using a sliding window method, with the sliding window covering data points within the most recent continuous monitoring period, calculating the dispersion of the difference between the feedback data and the expected data at each time point. Offset direction analysis determines the overall trend of the feedback data deviating from the expected trajectory. Offset directions are divided into positive offset, negative offset, and volatility offset. Positive offset indicates that the actual efficacy is better than expected, negative offset indicates that the efficacy is not as expected, and volatility offset indicates that the condition is unstable. When the calculated standard deviation exceeds a preset threshold, the preset threshold is dynamically adjusted according to the disease type and treatment stage, triggering the scheme correction logic in the smart contract. The smart contract calls the feature extraction module to recalculate the cross-modal correlation matrix. The feature extraction module accesses the latest encrypted data packet stored on the blockchain, decrypts it, and executes the same feature engineering process as the initial analysis. The recalculation process prioritizes feature dimensions that show the largest deviation in the standard deviation analysis. For example, if the standard deviation of blood pressure fluctuation data significantly exceeds the standard, the time-series feature extraction will focus on a more refined analysis of the blood pressure signal. The updated cross-modal correlation matrix incorporates the latest feedback information, reflecting the patient's real-time response to the initial treatment plan. Based on the updated cross-modal correlation matrix, the branch paths of the treatment decision tree are reconstructed. The reconstruction process involves re-dividing decision nodes and redistributing branch weights. Feature paths highly correlated with negative offsets are pruned, and new decision paths based on the latest data are generated.

[0071] The differences between the revised treatment plan and the original plan are extracted. These differences include changed drug names, adjusted dosages, modified device operating parameters, and updated follow-up frequencies. Change instructions and parameter adjustments are clearly identified. Change instructions refer to the specific treatment measures added, deleted, or replaced, while parameter adjustments refer to the numerical changes made from the original values. A transaction structure is generated, containing a change timestamp, the executing physician's digital identity, and a difference hash value. The change timestamp records the precise moment the plan revision occurred. The executing physician's digital identity is a CA-certified digital certificate used to identify the responsible medical individual initiating the revision. The difference hash value is a unique digest obtained by performing a SHA-256 hash on the entire difference data block. A proof-of-work mechanism is used to write the transaction structure into a new block. Nodes in the network compete for the right to write the transaction by solving a complex mathematical problem. The node that successfully finds the solution appends a new block containing the transaction structure to the end of the blockchain. The revised treatment plan is generated and stored in parallel on the blockchain with the original plan. Each plan has an independent version number and a hash pointer to its parent version, forming a complete version history chain. The difference hash value is not only used to identify transactions, but it is also incorporated into the Merkle root calculation of subsequent blocks. This ensures that any tampering with the difference data will result in a change in the Merkle root value, which will then be detected by the system. Writing a new transaction record updates the smart contract's state variable, which records the version number of the currently effective treatment plan, ensuring the system always executes the latest treatment plan. The entire correction process, from deviation detection to the on-chain update of the new plan, is automatically executed by the smart contract, minimizing delays caused by human intervention and enabling rapid response to patient conditions. The difference data written to the blockchain serves as an audit trail for medical decisions, providing an immutable data foundation for medical quality assessment and liability determination. See Table 1 for an example of the structure of the difference data transaction:

[0072] Table 1: Example of Differential Data Transaction Structure

[0073]

[0074] Nodes in the blockchain network verify the received transaction structure, including the validity of the digital signature, the correctness of the differential hash value, and the standardization of the transaction format. The proof-of-work mechanism ensures that the network reaches consensus on the content of the new block in a distributed environment, preventing malicious nodes from tampering with medical records. Once the transaction is confirmed and written into the new block, the revised treatment plan officially takes effect, and the smart contract automatically sends the new treatment instruction sequence to the patient's execution device. The system continuously monitors new feedback data after the revised plan is executed, initiating a new round of evaluation-revision cycle to achieve closed-loop management and continuous optimization of the treatment process. This automatic correction mechanism based on blockchain and smart contracts enhances the adaptability and reliability of the remote medical system, providing patients with dynamic and personalized health management services.

[0075] Example 5: Treatment plans for similar cases stored in the blockchain node network are periodically scanned. Scanning tasks are automatically initiated by smart contracts according to preset time cycles, such as a global scan every 24 hours. The scanning process generates a query request based on the disease diagnosis code, the patient's basic demographics, and the core feature vectors of the treatment plan. This query request is sent to every complete node in the blockchain network. Each complete node searches for matching case data in its local index and returns the matching treatment plans for similar cases and their corresponding standardized feature vector sets to the aggregation node. The number of treatment plans for similar cases increases with the growth of the blockchain, forming a dynamically expanding clinical knowledge base. Cluster analysis identifies the distribution patterns of the feature vectors of the collected treatment plans for similar cases. The cluster analysis uses a density-based spatial clustering algorithm, which can discover clusters of arbitrary shapes and effectively handle noise points. The distribution patterns of feature vectors are reflected in the density and clustering patterns of data points in a multi-dimensional feature space. For example, in the clustering of treatment plans for patients with type 2 diabetes, feature vectors may form different clusters around several core dimensions such as "insulin sensitivity," "degree of obesity," and "number of complications." Each cluster represents a subgroup of patients with significantly shared clinical characteristics, and cases within the same cluster exhibit high similarity in treatment protocols regarding drug selection and dosage adjustment strategies. The discovery of these distribution patterns provides data-driven insights into the complex relationship between disease presentation and treatment response.

[0076] The feature weighting strategy in the smart contract is optimized based on the distribution patterns revealed by cluster analysis. The optimization process is a dynamic adjustment mechanism based on reinforcement learning. The smart contract maintains a feature weight matrix, which defines the importance score of each feature in constructing a dynamic treatment decision tree. The reinforcement learning algorithm uses the clustering results as environmental feedback. If a feature shows high discriminative power in distinguishing different clinical subgroups, its weight score in the feature weight matrix will be increased. Conversely, features with weak correlation to treatment outcomes will have their weights decreased. For example, cluster analysis might reveal that for a certain subgroup of heart disease, the "late ventricular potential" feature is far more important than the "resting heart rate" feature in predicting treatment outcomes. In this case, the smart contract will assign a higher weight to the "late ventricular potential" feature when constructing a decision tree for new patients belonging to this subgroup. The optimized feature weighting strategy is immediately updated in the smart contract's state variables, and the new strategy will be applied to the next process of generating a treatment plan for a patient. The entire optimization loop is closed and automated, requiring no manual intervention from data scanning and cluster analysis to strategy updates. The results of cluster analysis, including cluster center coordinates, the number of cases within each cluster, and other statistical information, are written back to the blockchain as a new transaction in an anonymous, aggregated form. This transaction provides auditable traceability for adjusting feature weights, allowing any participant to verify the data patterns underlying the weight changes. The frequency of periodic scans can be flexibly adjusted based on network conditions and clinical needs. During disease outbreaks or periods of data surge following the launch of new drugs, the scan frequency can be increased to once every twelve hours to more quickly capture new treatment patterns. The algorithm parameters used in cluster analysis, such as neighborhood size and minimum number of points, can also be fine-tuned according to the characteristics of the disease type to ensure the clinical significance of the clustering results. Optimizing the feature weight allocation strategy is a continuous learning process. By constantly absorbing new case data, the system's decision-making logic adapts to the evolution of medical knowledge and changes in the disease spectrum. This self-optimization capability based on real-world evidence makes the blockchain-based remote intelligent disease diagnosis and treatment system not merely an execution tool, but a learning system capable of accumulating and elevating collective clinical wisdom.

[0077] To illustrate this, consider a case study on hypertension management. A blockchain node network periodically scans all treatment protocols for cases diagnosed with "essential hypertension." Cluster analysis may reveal three main patient subgroups: one characterized by "high salt sensitivity" and "low renin activity," whose treatment regimens respond well to diuretics; a second characterized by "high sympathetic excitability" and "rapid heart rate," whose feature vector distribution shows significant effectiveness of beta-blockers; and a third characterized by "high angiotensin II levels," whose distribution indicates that angiotensin-converting enzyme inhibitors are preferred. Based on these cluster analysis results, the feature weighting strategy in the smart contract is optimized, significantly increasing the weight of features such as "salt sensitivity test results," "plasma renin activity," and "resting sympathetic tone index" in the decision tree. When a new patient's hypertension data is uploaded, the system will match its feature vector with these clusters. If its feature vector is closest to the "high sympathetic nerve excitability" cluster, then when generating a dynamic diagnosis and treatment decision tree for it, the system will give priority to treatment pathways that can effectively reduce sympathetic nerve activity and assign higher monitoring weights to relevant physiological indicators.

[0078] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0079] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1.A blockchain-based remote disease intelligent diagnosis and treatment method, characterized in that, The method comprises the following steps: Collecting multi-modal physiological data of a patient and generating an encrypted data packet, the multi-modal physiological data including real-time vital sign data, historical medical record text data, and medical image data; Distributing the encrypted data packet through a blockchain node network for distributed storage, and generating data hash values and timestamps at each node; Calling an intelligent contract to perform cross-modal feature extraction on the encrypted data packet, generating a standardized feature vector set, and the standardized feature vector set containing the association identifiers of time-series features, spatial features, and semantic features; Constructing a dynamic diagnosis and treatment decision tree based on the standardized feature vector set, and the dynamic diagnosis and treatment decision tree dynamically adjusting branch paths according to feature weights; Verifying the node validity of the dynamic diagnosis and treatment decision tree through a consensus mechanism, marking the decision path that passes the verification as an executable diagnosis and treatment scheme, generating a treatment instruction sequence, and the treatment instruction sequence including drug configuration parameters, instrument operation steps, and follow-up cycles; Real-time monitoring of feedback data of the patient executing the treatment instruction sequence and updating to the blockchain node network; When the deviation between the feedback data and the expected therapeutic effect exceeds a threshold value, triggering the intelligent contract to regenerate a revised diagnosis and treatment scheme; Writing the difference data between the revised diagnosis and treatment scheme and the original diagnosis and treatment scheme as a new transaction record into the blockchain; The real-time monitoring of feedback data of the patient executing the treatment instruction sequence comprises: Collecting the concentration change curve of metabolic products after the patient takes medicine and physiological index fluctuation data; Recording mechanical parameters and time intervals during instrument operation through an Internet of Things device; Packing the concentration change curve of metabolic products, physiological index fluctuation data, and mechanical parameters into a feedback data block; The triggering of the intelligent contract to regenerate a revised diagnosis and treatment scheme comprises: Comparing the standard deviation and deviation direction of the feedback data block and the expected therapeutic effect data; When the standard deviation exceeds a threshold value, calling a feature extraction module to recalculate a cross-modal association matrix; Reconstructing the branch paths of the diagnosis and treatment decision tree based on the updated cross-modal association matrix. 2.The blockchain-based remote disease intelligent diagnosis and treatment method according to claim 1, characterized in that, The calling of the intelligent contract to perform cross-modal feature extraction on the encrypted data packet comprises: Decomposing the real-time vital sign data in the encrypted data packet into equally spaced time-series segments, and calculating the mean, variance, and peak frequency of each segment; Performing keyword entity recognition and context relationship analysis on the historical medical record text data to generate a structured semantic graph; Extracting the gray scale distribution matrix and edge gradient features of the medical image data to construct a three-dimensional spatial feature vector; Aligning the time-series segment features, structured semantic graph, and three-dimensional spatial feature vector along the time axis, and then fusing them into a cross-modal association matrix. 3.The blockchain-based remote disease intelligent diagnosis and treatment method according to claim 2, characterized in that, The construction of a dynamic diagnosis and treatment decision tree based on the standardized feature vector set comprises: Dividing the initial decision nodes according to the Euclidean distance of the feature vectors in the cross-modal association matrix; Calculating the mutual information entropy of each feature vector and disease classification label to dynamically assign weight coefficients to decision branches; When new feedback data is added, adjusting the splitting priority of the decision nodes based on the weight coefficients. 4.The blockchain-based remote disease intelligent diagnosis and treatment method according to claim 3, characterized in that, The verification of the node validity of the dynamic diagnosis and treatment decision tree through a consensus mechanism comprises: Broadcasting the node hash values of the decision path to a preset number of verification nodes; Each verification node compares the matching degree of the locally stored feature vector and the node hash value; When the matching degree exceeds the preset proportion, mark the node as a valid node and generate a digital signature. 5.The blockchain-based remote disease intelligent diagnosis and treatment method according to claim 4, characterized in that, The generation of the treatment instruction sequence includes: Parsing the disease stage identification and complication risk level in the executable diagnosis and treatment scheme; Matching the treatment template in the preset clinical guideline library to generate a basic instruction set; According to the individual difference parameters of the patient, the basic instruction set is prioritized and dosed. 6.The blockchain-based remote disease intelligent diagnosis and treatment method according to claim 1, characterized in that, The difference data between the modified diagnosis and treatment scheme and the original diagnosis and treatment scheme is written into the blockchain as a new transaction record, including: Extracting the change instruction item and parameter adjustment amount in the difference data; Generating a transaction structure containing the change timestamp, the digital identity of the executing doctor, and the difference hash value; Through the proof of work mechanism, the transaction structure is written into a new block. 7.The blockchain-based remote disease intelligent diagnosis and treatment method according to claim 6, characterized in that, Also includes: Periodically scanning the diagnosis and treatment schemes of similar cases stored in the blockchain; Cluster analysis of the feature vector distribution rule of the diagnosis and treatment scheme of the similar cases; According to the distribution rule, optimize the feature weight distribution strategy in the smart contract. 8.A blockchain-based remote disease intelligent diagnosis and treatment system, characterized in that, A processor and a memory, the memory stores a computer program, and the processor executes the computer program to realize the blockchain-based remote disease intelligent diagnosis and treatment method of any one of claims 1-7.

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