A liver transplantation medicine guiding system based on multi-expert diagnosis opinions and intelligent reasoning

Abstract

The application belongs to the technical field of information processing, and particularly relates to a liver transplantation drug guidance system based on multiple expert diagnosis opinions and intelligent reasoning; the system comprises a data management module, a diagnosis analysis module, a drug suggestion module and a feedback module; the data management module is used for collecting user health index data and multiple expert diagnosis opinions, and storing medical literature; the diagnosis analysis module is used for generating user diagnosis information according to information of the data management module; the drug suggestion module is used for generating a drug scheme according to information of the data management module, user diagnosis information and feedback information of the feedback module; and the feedback module is used for obtaining feedback information, including user health index data after drug taking and expert feedback information; the application overcomes the limitations of traditional drug guidance methods, and realizes more scientific, objective and accurate postoperative drug guidance for liver transplantation.

Description

Technical Field

[0001] This invention belongs to the field of information processing technology, specifically relating to a liver transplant medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning. Background Technology

[0002] In the field of post-liver transplant management, medication guidance for patients is crucial for transplant success and long-term survival. However, current technologies have several shortcomings. Traditionally, physicians rely primarily on personal experience and judgment of individual patient conditions to provide medication recommendations. However, due to differences in physicians' professional backgrounds, experience, and diagnostic styles, even when dealing with the same patient, different physicians may provide significantly different diagnoses and medication recommendations. This variability not only increases the uncertainty of the patient's medication regimen but may also lead to a series of problems such as inappropriate medication, increased drug side effects, and poor treatment outcomes.

[0003] Current post-liver transplant medication guidance typically relies on the diagnosis of a single expert, lacking systematic analysis and integration of diagnoses from multiple experts. This single-expert-based guidance model has the following main drawbacks: First, due to the lack of comprehensive analysis of multiple expert opinions, the user's medication guidance is easily influenced by the limitations of individual expert opinions, potentially overlooking other important medical insights and leading to a one-sided diagnosis. Second, the advice of a single expert is easily influenced by personal experience and subjective judgment, lacking sufficient scientific rigor and objectivity, making it difficult to guarantee the accuracy and comprehensiveness of medication guidance. Furthermore, existing technologies fail to fully utilize the diverse opinions of multiple experts to develop individualized medication plans, affecting the accuracy of treatment and the individualized needs of users.

[0004] In conclusion, we continue to develop a liver transplant medication guidance system that can incorporate the opinions of multiple experts to improve the accuracy of medication guidance, meet the needs of clinical guidance, and assist clinicians in making better clinical medication and treatment decisions. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention proposes a liver transplant medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning. The system includes: a data management module, a diagnostic analysis module, a medication suggestion module, and a feedback module.

[0006] Data management module: used to collect and manage user health data, obtain diagnostic opinions from multiple experts, and store relevant medical literature and clinical data to ensure the system's comprehensive acquisition and effective management of multi-source data;

[0007] Diagnostic Analysis Module: Used to analyze and integrate diagnostic opinions from multiple experts, and to generate comprehensive diagnostic information for users based on logical reasoning from medical literature, clinical data, and user health data.

[0008] Medication suggestion generation module: Based on the comprehensive diagnostic information provided by the diagnostic analysis module and the feedback data provided by the feedback module, it generates personalized medication suggestions and continuously optimizes the medication plan;

[0009] Feedback module: Used to collect user feedback, post-medication health data, and expert feedback on medication plans to support the system in dynamically adjusting and optimizing medication recommendations.

[0010] Preferably, the data management module includes:

[0011] User metrics collection unit: used to collect user health data;

[0012] Expert Diagnostic Opinion Collection Unit: Used to obtain diagnostic opinions from multiple experts in the field of liver transplantation;

[0013] Medical literature storage unit: used to store medical literature and clinical data related to liver transplantation.

[0014] Furthermore, user health data includes: the user's basic physiological data, the user's liver medical images, the user's immune status, other disease history and medication records, dietary habits, and exercise records.

[0015] Preferably, the diagnostic analysis module includes:

[0016] Disagreement identification unit: Used to identify inconsistencies in the diagnostic opinions of multiple experts and determine the impact of these disagreements on the user's treatment decision;

[0017] Reasoning generation unit: Based on the divergence identification results, relevant medical literature, clinical data and user health data, it performs automated logical reasoning to generate comprehensive diagnostic results.

[0018] Furthermore, the process of automated logical reasoning by the reasoning generation unit based on the divergence identification results, relevant medical literature, clinical data, and user health data includes: identifying the medical literature most relevant to the family's diagnostic opinion from the relevant medical literature; and generating a comprehensive diagnostic result by automatically performing logical reasoning based on the divergence identification results, clinical data, and user health data, combined with the medical literature most relevant to the family's diagnostic opinion.

[0019] Preferably, the medication recommendation generation module includes:

[0020] Primary medication regimen generation unit: Based on the comprehensive diagnostic information provided by the diagnostic analysis module, it generates a preliminary medication regimen that conforms to the user's physiological characteristics and disease status;

[0021] Optimization Unit: Dynamically optimizes the primary medication regimen based on feedback data provided by the feedback module, generating personalized optimal medication recommendations.

[0022] Preferably, the feedback module includes:

[0023] User feedback unit: used to collect users' health data and user feedback information after medication use, to ensure that the system has a timely grasp of the user's real situation;

[0024] Expert Feedback Unit: This unit collects expert feedback on medication plans and generates a suggestion feedback report to help the system scientifically adjust and optimize medication recommendations.

[0025] Furthermore, the user's health data after taking the medication includes health data from a period of time in the past to the present moment, as well as health data predicted for a period of time in the future.

[0026] The beneficial effects of this invention are as follows: The medication guidance system constructed in this invention collects multimodal data, including the user's basic physiological data, liver medical images, immune status, other disease history and medication records, dietary habits, and exercise records. By integrating multiple data types, the system can analyze the user's condition more comprehensively and accurately, improving the accuracy and personalization of diagnosis. This invention combines the diagnostic opinions of multiple experts with real-time user data for final decision-making, enabling the system to generate the most objective and reasonable diagnostic results and medication plans based on a comprehensive consideration of multiple opinions, further improving the reliability of diagnosis and treatment. By introducing the dialectical analysis of multi-expert diagnostic opinions and utilizing real-time feedback information from users and experts, this invention overcomes the limitations of traditional medication guidance methods, achieving more scientific, objective, and accurate post-liver transplant medication guidance, and compensating for the shortcomings of existing technologies in personalized treatment. Attached Figure Description

[0027] Figure 1 This is a framework diagram of a liver transplant medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning, as described in this invention.

[0028] Figure 2 Here is a diagram of the BERT model structure;

[0029] Figure 3 Diagram of the multi-head attention mechanism

[0030] Figure 4 Flowchart for the expert diagnostic opinion collection unit in the data management module;

[0031] Figure 5 Flowchart for the divergence identification unit of the diagnostic analysis module;

[0032] Figure 6 This is a flowchart of the user feedback unit in the feedback module. Detailed Implementation

[0033] 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.

[0034] This invention proposes a liver transplant medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning, such as... Figure 1 As shown, the system includes: a data management module, a diagnostic analysis module, a medication recommendation module, and a feedback module.

[0035] Preferably, the main task of the data management module is to collect, store, and manage users' health data and diagnostic opinions from multiple experts in a standardized manner, as well as store relevant medical literature and clinical data. This module processes data through a series of advanced algorithms to ensure the system's comprehensive acquisition and effective management of multi-source data, ensuring the accuracy, completeness, and consistency of data from different sources, thereby providing a reliable source of information for subsequent diagnostic analysis and medication recommendations.

[0036] The data management module includes a user indicator collection unit, an expert diagnostic opinion collection unit, and a medical literature storage unit.

[0037] The user indicator acquisition unit uses multiple data acquisition methods to collect various user health data, covering liver imaging data (such as MRI and CT scans), basic physiological data (such as blood indicators), and immune status. In addition, it can also collect user-reported symptoms or health conditions (history of other diseases and medication records, dietary habits, and exercise records), ensuring the system has a comprehensive understanding of the user's physiological state. The system integrates with Hospital Information Management Systems (HIS) and Laboratory Information Management Systems (LIMS) via APIs to achieve automatic acquisition and integration of multimodal data. To address the issue of inconsistent data formats from different sources, the system employs data cleaning and standardization algorithms. Data cleaning uses outlier detection algorithms (such as Z-Score) to identify and remove abnormal data. The standardization part uses the Z-Score standardization algorithm, the formula of which is:

[0038]

[0039] Where X represents the original data, μ is the sample mean, and σ is the standard deviation. This formula is used to transform data from different units into a standard normal distribution to ensure data consistency in subsequent analysis.

[0040] The expert diagnostic opinion acquisition unit is used to obtain diagnostic opinions from multiple experts in the field of liver transplantation, allowing input of expert opinions in text format. This unit employs Natural Language Processing (NLP) technology, especially the BERT (Bidirectional Encoder Representations from Transformers) model, to perform deep semantic understanding of the text content. Figure 2 As shown, BERT is a pre-trained language model based on the Transformer architecture, the core of which lies in capturing contextual information through a bidirectional encoder.

[0041] The key components of the BERT model include the formula for calculating its self-attention mechanism:

[0042]

[0043] Where Q is the query vector, K is the key vector, V is the value vector, and d k This is the dimension of the key vector. Using this formula, the model can capture contextual dependencies within sentences, ensuring that expert diagnoses are accurately understood and translated into standardized medical terminology, while simultaneously matching them with the system's existing medical knowledge graph.

[0044] To enhance the ability to handle complex semantic dependencies, this unit introduces a multi-head self-attention mechanism on top of the BERT (Bidirectional Encoder Representations from Transformers) model, such as... Figure 3 As shown.

[0045] Multi-head self-attention mechanisms can compute different features in parallel across multiple subspaces, capturing deeper semantic relationships. Specifically, the input expert diagnostic opinion text is first mapped into a query (Q), key (K), and value (V) matrix:

[0046] Q = XW Q K = XW K V = XW V

[0047] Where X is the input word embedding matrix, W Q W K W V This is a learnable linear transformation matrix. Subsequently, the attention distribution is computed in parallel by multiple heads:

[0048]

[0049] Each head captures different levels of semantic information, and the outputs of multiple heads are finally concatenated:

[0050] MultiHead(Q,K,V)=Concat(head1,head2,...,head h W O

[0051] Through this multi-head self-attention mechanism, the model can more accurately understand the complex contextual dependencies in expert opinions, ensuring that diagnostic opinions can be accurately converted into standardized medical terminology, while efficiently matching with the system's existing medical knowledge graph to provide more reliable diagnostic support.

[0052] The process for collecting expert opinions in the expert diagnostic opinion collection unit is as follows: Figure 4 As shown.

[0053] The medical literature storage unit is responsible for storing medical literature and clinical data related to liver transplantation, including medical literature and clinical data related to liver transplantation and its related complications, as an important basis for subsequent reasoning and analysis.

[0054] Preferably, the diagnostic analysis module includes a divergence identification unit and a reasoning generation unit.

[0055] The core task of the diagnostic analysis module is to integrate the diagnostic opinions of multiple experts, identify discrepancies, and use logical reasoning based on relevant medical literature to generate comprehensive diagnostic information. This module employs an intelligent reasoning mechanism to ensure that the generated diagnostic results are scientifically based and logically coherent, thereby enhancing the accuracy and interpretability of the diagnosis.

[0056] The disagreement identification unit is responsible for processing differing opinions in expert diagnoses and conducting in-depth analysis of these opinions using a knowledge graph-based disagreement identification algorithm to determine their potential impact on the user's treatment decisions. The system maps expert opinions to corresponding medical knowledge nodes using a pre-constructed liver transplant disease knowledge graph, forming semantically related graph-structured data. Then, the system comprehensively employs Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) to calculate semantic similarity and analyze the importance of expert opinions, ensuring the accuracy of disagreement identification.

[0057] Specifically, such as Figure 5 As shown, the semantic similarity of different expert opinions is first calculated using GCN to capture potential connections and differences among expert opinions. GCN propagates semantic features between nodes by aggregating neighbor information of nodes in the graph. Its core formula is:

[0058]

[0059] Wherein: H (l)Let W(l) be the node representation matrix of the l-th layer, and W(l) be the weight matrix of the l-th layer. It is the normalized adjacency matrix. Let be the degree matrix, and o be the nonlinear activation function.

[0060] Through the multi-layered propagation mechanism of GCN, the system can effectively capture the inherent semantic relationships between expert diagnostic opinions, thereby marking inconsistencies in the diagnostic opinions.

[0061] To further improve the accuracy of disagreement identification, the system introduces a Graph Attention Network (GAT). GAT allows the model to adaptively assign different attention weights to expert opinion nodes based on their importance, thus highlighting the role of key opinions. The core formula of GAT is:

[0062]

[0063] in, Here, attention weight represents the influence of node j on node i, and it is calculated as follows:

[0064]

[0065] Through this mechanism, the system can more accurately identify and highlight key points of disagreement in different expert opinions. Furthermore, the system employs a multi-head attention mechanism, using multiple parallel attention heads to capture different feature dimensions in expert opinions, thereby enhancing the model's ability to represent opinion diversity.

[0066] After identifying points of disagreement, the system will further evaluate these points of disagreement, analyze their potential impact on diagnostic decisions, and provide a more comprehensive and accurate integrated analysis of diagnostic opinions to help users make more rational treatment decisions.

[0067] For identified disagreements, the reasoning generation unit employs an evidence-based reasoning system combined with a knowledge graph for reasoning analysis. This unit extracts supporting evidence from relevant medical literature, clinical data, and user health data, and comprehensively evaluates different diagnostic opinions through the evidence-based reasoning system.

[0068] First, to ensure efficient literature retrieval, the system uses the BERT algorithm to recommend medical literature most relevant to expert diagnostic opinions. Specifically:

[0069] 1. Perform lexicalization on each document and generate a global semantic vector V for each document using the BERT model. doc (Vectors based on [CLS] tags).

[0070] Formula: V doc=BERT([CLS],t1,t2,...,t n Where t1, t2, ..., t n A sequence of terms representing a document.

[0071] 2. The system converts the expert's diagnostic opinions into a semantic vector V. CLS And by calculating the semantic vector V of the document doc The similarity between the diagnostic opinion vector and the reference vector determines the recommended literature.

[0072] Similarity calculation formula (using cosine similarity):

[0073]

[0074] Using this formula, the system can recommend medical literature most relevant to expert diagnostic opinions based on semantic similarity.

[0075] To further improve the accuracy of document recommendations, the system also integrates the Doc2Vec model. This model maps documents to a low-dimensional vector space and can recommend documents based on similarity. Its objective function is:

[0076]

[0077] Here, P(w|d) represents the probability of predicting word w given document d. This model dynamically recommends relevant literature for the current diagnosis to the system by capturing the latent semantic relationships among documents.

[0078] The inference system uses a Bayesian inference model to calculate the probability of each diagnostic path. The basic formula for Bayesian inference is:

[0079]

[0080] Here, P(H|E) represents the posterior probability that hypothesis H is true when evidence E appears, P(E|H) represents the probability that evidence E appears given hypothesis H, P(H) is the prior probability of hypothesis H, and P(E) is the marginal probability of evidence E. Through this reasoning model, the system evaluates the probabilities of different diagnostic paths and, combined with medical evidence from the literature, selects the most supportive path as the final comprehensive diagnostic result. This reasoning process ensures that the diagnostic result not only has logical coherence but also receives sufficient support from medical literature, enhancing the scientific rigor and credibility of the diagnosis.

[0081] Preferably, the medication recommendation generation module includes a primary medication plan generation unit and an optimization unit.

[0082] The medication recommendation generation module generates preliminary medication recommendations based on the comprehensive diagnostic information provided by the diagnostic analysis module, combined with the user's health status and feedback, and continuously optimizes the medication plan through a dynamic feedback mechanism.

[0083] The initial medication regimen generation unit employs a decision tree algorithm. This unit inputs comprehensive diagnostic information into the decision tree model, combining it with the user's individual physiological data (such as age, weight, and liver function status) to generate a personalized initial medication regimen that aligns with the user's physiological characteristics and disease state. The decision tree model recursively partitions the dataset, performing multi-level classifications of the user's individual information to generate medication recommendations tailored to the user's individual needs. For immunosuppressants that may be used after liver transplantation, the decision tree model generates personalized dosage recommendations based on the user's immune status and drug metabolism. The splitting of the decision tree model follows the principle of maximizing information gain, and its information gain calculation formula is as follows:

[0084]

[0085] In this formula, IG(T,A) represents the information gain of feature A on dataset T; H(T) is the entropy of dataset T; and Tv is a subset of features A taking different values. By calculating the information gain, the decision tree selects the optimal feature for node splitting until the final personalized medication recommendation is generated.

[0086] The optimization unit employs the Deep Q-Networks (DQN) algorithm from reinforcement learning. It dynamically adjusts the medication regimen by scoring user feedback (user feedback information, post-medication health data, and expert feedback on the medication plan, etc.). The DQN algorithm uses a deep neural network to estimate the Q-value under different states and optimizes medication decisions based on user health feedback. The formula for Q-value update is as follows:

[0087]

[0088] In this formula, Q(s,a) represents the Q-value of taking action a in state s; r is the immediate reward (e.g., the improvement in the user's physiological indicators after medication); γ is a discount factor used to balance the weights of current and future rewards; and α is the learning rate, controlling the update step size. The system adjusts the medication regimen based on feedback signals after each medication administration. For example, when the user's blood indicators improve, the system updates the Q-value accordingly and continuously optimizes future medication decisions through a deep reinforcement learning model. Through the feedback mechanism of reinforcement learning, the system can ensure that it provides the optimal medication regimen at each treatment stage.

[0089] Preferably, the feedback module includes a user feedback unit and an expert feedback unit.

[0090] The feedback module is responsible for collecting users' health data and feedback information after medication use, as well as expert feedback on the medication plan. Through this module, the system can dynamically adjust and optimize the medication plan to adapt to the user's real-time health status, ensuring that the medication plan at each stage can best meet the individual needs of the user.

[0091] like Figure 6 As shown, the user feedback unit monitors users' health data (such as blood sugar and liver function indicators) and user feedback information (such as medication effects) in real time after medication use. The user's health data after medication use includes health data from a period in the past to the present, as well as health data predicted for a future period. To more accurately predict users' health trends and identify potential health risks, the system employs a Bidirectional Long Short-Term Memory (BiLSTM) network combined with an attention mechanism to enhance the analysis of health data and the ability to identify feedback.

[0092] 1. Bidirectional Long Short-Term Memory (BiLSTM) model

[0093] BiLSTM is an improved recurrent neural network that captures dependencies between past and future time points by performing forward and backward processing on time series data. This model can comprehensively consider complex dependencies in time series data, enhancing its ability to capture health trends.

[0094] 1.1 Calculation formula for forward LSTM

[0095] The forward LSTM uses the current input data x t The hidden state of the previous time step Calculate the hidden state at the current time step:

[0096] h t =LSTM(x t ,h t-1 )

[0097] Where, x t It is the input health data. This represents the hidden state of the forward LSTM at time step t-1.

[0098] 1.2 Calculation formula for backward LSTM

[0099] Backward LSTM uses the current input data x t and the hidden state of the next time step Calculate the hidden state at the current time step:

[0100]

[0101] in, This represents the hidden state of the backward LSTM at time step t+1.

[0102] 1.3 Output of Bidirectional LSTM

[0103] The outputs of the forward and backward LSTMs are concatenated to form the final output of the bidirectional LSTM:

[0104]

[0105] Through this bidirectional processing, BiLSTM can capture the complex time dependencies in user health data, focusing not only on past information but also gaining insights from future data, thereby improving the accuracy of predicting health trends.

[0106] 2. Introduction of attention mechanisms

[0107] To further enhance the model's ability to capture key information from health data, an attention mechanism is introduced, enabling the model to adaptively focus on health data at different time points and dynamically adjust weights based on their importance. In this way, the system not only relies on the bidirectional processing capabilities of BiLSTM but can also more accurately identify key health changes in user feedback through the attention mechanism, especially in long-term series data.

[0108] 2.1 The core formula of the attention mechanism

[0109] Based on the output of the BiLSTM, the attention weight α at each time step is calculated. t :

[0110]

[0111] Among them, e t Based on BiLSTM hidden state h t and attention weight matrix W a Calculated attention energy:

[0112] e t =tanh(W a h t )

[0113] Through this attention mechanism, the model is able to identify the time steps that are most important for predicting health trends and assign higher weights to these time steps.

[0114] 2.2 Weighted BiLSTM Output

[0115] Based on attention weight α t The BiLSTM outputs at all time steps are weighted and summed to generate the final weighted output representation:

[0116]

[0117] The weighted representation can more accurately reflect the key information in users' health data, enabling the model to focus more on the time points that have the greatest impact on users' health status when processing long-term series data, thereby improving the accuracy of risk identification.

[0118] 3. Personalized optimization based on user feedback

[0119] By combining BiLSTM with an attention mechanism, the system can not only identify a user's current health status but also provide personalized health trend predictions based on individual long-term data. This prediction includes a dynamic assessment of future health status; the system automatically updates weights based on the user's historical health data and feedback, thereby continuously optimizing treatment recommendations for the user.

[0120] The expert feedback unit is responsible for periodically inviting experts involved in the diagnosis to review the current medication regimen. It then analyzes the experts' feedback using an automated text processing model and generates a feedback report with recommendations. To extract key information from the expert feedback, the system employs the BERT model. The BERT model measures the importance of a term within a set of texts, thereby automatically identifying expert recommendations regarding the medication regimen. The BERT calculation process is as follows:

[0121] 1. The text feedback from experts is first input into the BERT model for processing. The text input can be paragraphs, sentences, or words.

[0122] The input text is segmented into tokens, and special classification tags [CLS] and separator tags [SEP] are added. For each token ti, BERT generates a corresponding vector representation V(t). i This vector combines the semantic information of the word in the current context.

[0123] 2. Each layer of BERT consists of a Transformer encoder. The vector representation of each word is processed through multiple encoders to capture contextual information. The formula is as follows:

[0124] V′(t i =Transformer(V(t) i ),Q,K,V)

[0125] Where: V(t) i ) is the initial vector representation of the input tokens. Q, K, and V are the query vector, key vector, and value vector, respectively, calculated through a self-attention mechanism. The Transformer encodes the contextual association of each token through a multi-layer self-attention mechanism, generating a more semantically expressive vector V′(t)i ).

[0126] 3. BERT adds a special classification tag [CLS] at the beginning of each input text. The vector representation of this tag can serve as a global semantic representation of the entire text. The output vector V, obtained by extracting the [CLS] tag, is then used. CLS This yielded expert feedback on the overall semantic representation of the text.

[0127] V CLS =BERT([CLS],t1,t2,…,t n )

[0128] 4. To extract key information from expert feedback, the similarity between \(V_{CLS}\) and the vector representations of predefined drug regimen-related terms or concepts can be calculated. Commonly used similarity measurement formulas are as follows:

[0129]

[0130] Among them, V concept It is a vector representation of predefined concepts or drug terms, and calculates the cosine similarity between the semantic representations fed back by experts and the terms to identify the most relevant drug regimen recommendations.

[0131] 5. Based on the similarity calculation results, the system can extract key information related to the medication plan from expert feedback and generate a structured suggestion feedback report for subsequent decision-making.

[0132] This model allows the system to automatically extract expert opinions on medication regimen adjustments, such as suggestions for adjusting drug dosage or frequency. This extracted key information is then incorporated into the optimization unit for continuous adjustment and optimization of the medication regimen, ensuring its effectiveness and personalization.

[0133] Through the detailed design and implementation described above, the system can effectively improve the quality of medication decisions for liver transplant patients, optimize treatment outcomes, and reduce the risks arising from disagreements in diagnostic opinions.

[0134] The medication guidance system designed in this invention can be integrated with the hospital's internal electronic medical record system and telemedicine platform to manage the user's postoperative recovery in real time and provide customized diagnosis and medication guidance plans, which greatly improves the efficiency of medical services and the user's postoperative quality of life.

[0135] In summary, this invention integrates multimodal data, advanced algorithms, and real-time computing technology to construct a comprehensive intelligent liver transplant medication guidance system. In practical clinical applications, this system has successfully provided personalized postoperative medication guidance for different users, effectively improving postoperative recovery outcomes and reducing the risk of complications, demonstrating strong clinical application value and broad prospects for wider application.

[0136] The above-described embodiments further illustrate the purpose, technical solution, and advantages of the present invention. It should be understood that the above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made to the present invention within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A liver transplantation drug guide system based on multi-specialist diagnosis opinions and intelligent reasoning, characterized by, Comprise the following modules: Data management module: for collecting and managing user health data, obtaining multiple expert diagnosis opinions, and storing relevant medical literature and clinical data, ensuring comprehensive acquisition and effective management of multi-source data by the system; Diagnosis analysis module: for analyzing and integrating multiple expert diagnosis opinions, mapping multiple expert opinions to a liver transplantation knowledge graph, using graph convolution network and graph attention network to identify differences and evaluate influence; based on medical literature, clinical data and user health data, logical reasoning is performed, semantic literature retrieval is executed, and the posterior probability of multiple candidate paths is calculated by Bayesian inference to generate comprehensive diagnosis information for the user; The diagnosis analysis module comprises: Discrepancy identification unit: for identifying inconsistencies in multiple expert diagnosis opinions and determining the impact of these discrepancies on user treatment decisions; it uses graph convolution network and graph attention network to perform semantic similarity calculation and importance analysis on expert opinions to ensure the accuracy of discrepancy identification; Inference generation unit: uses BERT model to find the most relevant medical literature from relevant medical literature based on expert diagnosis opinions; based on the discrepancy identification results, clinical data and user health data, combined with the most relevant medical literature to expert diagnosis opinions, automatic logical reasoning is performed, and Bayesian inference model is used to calculate the possibility of each diagnosis path to generate comprehensive diagnosis results; Drug recommendation generation module: based on the comprehensive diagnosis information provided by the diagnosis analysis module, combined with the feedback data provided by the feedback module to generate personalized drug recommendations, and using DQN algorithm to iteratively optimize the dosage strategy until it converges to an individualized optimal drug regimen; Feedback module: for collecting user feedback information, health data after medication, and expert feedback information on drug regimen to support dynamic adjustment and optimization of drug recommendations by the system.

2. The drug guidance system for liver transplantation based on multi-specialist diagnostic opinions and intelligent reasoning according to claim 1, characterized in that, The data management module comprises: User index collection unit: for collecting user health data; Expert diagnosis opinion collection unit: for obtaining diagnosis opinions from multiple experts in the field of liver transplantation; Medical literature storage unit: for storing medical literature and clinical data related to liver transplantation. 3.The liver transplantation medication guidance system based on multi-specialist diagnosis opinions and intelligent reasoning of claim 2, characterized in that, User health data includes: user's basic physiological data, user's liver medical images, user's immune status, other disease history and medication records, dietary habits and exercise records.

4. The drug guide system for liver transplantation based on multi-specialist diagnosis opinions and intelligent reasoning according to claim 1, characterized in that, The drug recommendation generation module comprises: Primary drug regimen generation unit: based on the comprehensive diagnosis information provided by the diagnosis analysis module, generate a preliminary drug regimen that meets the user's physiological characteristics and disease status; Optimization unit: using DQN algorithm to dynamically optimize the primary drug regimen based on the feedback data provided by the feedback module to generate personalized optimal drug recommendations.

5. The drug guide system for liver transplantation based on multi-specialist diagnosis opinion and intelligent reasoning according to claim 1, characterized in that, The feedback module comprises: User feedback unit: for collecting user health data and user feedback information after medication to ensure that the system can grasp the user's real situation in a timely manner; Expert feedback unit: for collecting expert feedback information on drug regimen and generating a feedback report to help the system make scientific adjustments and optimizations to drug recommendations.

6. The drug guidance system for liver transplantation based on multi-specialist diagnostic opinions and intelligent reasoning according to claim 5, characterized in that, The health data of the user after taking the medicine includes health data of a past period of time to a current time after the user takes the medicine and health data of a future period of time predicted by using a bidirectional long short-term memory network combined with an attention mechanism.

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