Liver transplantation medication guidance system based on multi-expert diagnosis opinions and intelligent reasoning

Abstract

The invention belongs to the technical field of information processing, and particularly relates to a liver transplantation medication guidance system based on multi-expert diagnosis opinions and intelligent reasoning. The system comprises a data management module, a diagnostic analysis module, a medication suggestion module and a feedback module, the data management module is used for collecting user health index data and diagnosis suggestions of multiple experts and storing medical literature data; the diagnostic analysis module is used for generating diagnostic information of the user according to the information of the data management module; the medication suggestion module is used for generating a medication scheme according to the information of the data management module, the diagnosis information of the user and the feedback information of the feedback module; the feedback module is used for acquiring feedback information which comprises health index data after the user takes medicine and feedback information of an expert; according to the method, the limitation of a traditional medication guidance method is overcome, and more scientific, objective and accurate liver transplantation postoperative medication guidance is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of information processing, and in particular relates to a liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning. Background Art

[0002] In the field of postoperative management of liver transplantation, the user's medication guidance is crucial to the success of the transplant and long-term survival rate. However, existing technologies have multiple shortcomings. Traditionally, doctors mainly rely on personal experience and judgment of individual users' situations to provide medication recommendations. However, due to different professional backgrounds, experience and diagnostic styles of doctors, even when facing the same user, the diagnostic opinions and medication recommendations given by different doctors may vary significantly. This difference not only increases the uncertainty of the user's medication plan, but may also lead to a series of problems such as improper medication, increased drug side effects, and poor treatment effects.

[0003] Current guidance on medication after liver transplantation usually relies on the diagnostic opinion of a single expert, lacking systematic analysis and integration of diagnostic opinions from multiple experts. This guidance model based on single expert opinion has the following major defects: First, due to the lack of comprehensive analysis of multiple expert opinions, the user's medication guidance is easily affected by the limitations of individual expert opinions, and may ignore other important medical insights, resulting in one-sided diagnosis. Secondly, the advice of a single expert is easily affected by personal experience and subjective judgment, lacks sufficient scientificity and objectivity, and is difficult to ensure the accuracy and comprehensiveness of medication guidance. However, existing technologies have failed to make full use of the diversified opinions of multiple experts to formulate individualized medication plans, affecting the accuracy of treatment and the individualized needs of users.

[0004] In summary, a liver transplantation medication guidance system that can combine the opinions of multiple experts is needed 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] In view of the shortcomings of the prior art, the present invention proposes a liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning, the method comprising: 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 that the system comprehensively acquires and effectively manages multi-source data;

[0007] Diagnostic analysis module: used to analyze and integrate the diagnostic opinions of multiple experts, perform logical reasoning based on medical literature, clinical data and user health data, and generate comprehensive diagnostic information for users;

[0008] Medication advice generation module: Generates personalized medication advice based on the comprehensive diagnostic information provided by the diagnostic analysis module and the feedback data provided by the feedback module, and continuously optimizes the medication plan;

[0009] Feedback module: used to collect user feedback information, health data after medication, and expert feedback on medication plans to support the system's dynamic adjustment and optimization of medication recommendations.

[0010] Preferably, the data management module includes:

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

[0012] Expert diagnosis opinion collection unit: used to obtain diagnosis opinions from multiple experts in the field of liver transplantation;

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

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

[0015] Preferably, the diagnostic analysis module includes:

[0016] Disagreement Recognition 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: performs automated logical reasoning based on divergence recognition results, relevant medical literature, clinical data, and user health data to generate comprehensive diagnosis results.

[0018] Furthermore, the process of the reasoning generation unit performing automated logical reasoning based on the divergence identification results, relevant medical literature, clinical data and user health data includes: finding the medical literature most relevant to the home diagnosis opinion from the relevant medical literature; performing automated logical reasoning based on the divergence identification results, clinical data and user health data in combination with the medical literature most relevant to the home diagnosis opinion to generate a comprehensive diagnosis result.

[0019] Preferably, the medication suggestion generating module includes:

[0020] Primary medication plan generation unit: based on the comprehensive diagnostic information provided by the diagnostic analysis module, generates a preliminary medication plan that meets the user's physiological characteristics and disease status;

[0021] Optimization unit: Dynamically optimizes the primary medication regimen based on the feedback data provided by the feedback module to generate personalized optimal medication recommendations.

[0022] Preferably, the feedback module includes:

[0023] User feedback unit: used to collect the user's health data and user feedback information after taking the medicine, to ensure that the system can grasp the user's real situation in a timely manner;

[0024] Expert feedback unit: used to collect experts' feedback on medication plans and generate recommendation feedback reports to help the system scientifically adjust and optimize medication recommendations.

[0025] Furthermore, the health data of the user after taking the medicine includes the health data from the past period of time after the user took the medicine to the current moment and the health data predicted for a period of time in the future.

[0026] The beneficial effects of the present invention are as follows: the data collected in the medication guidance system constructed by the present invention includes multimodal data, including the user's basic physiological data, the user's liver medical images, the user's immune status, other disease history and medication records, eating habits and exercise records, and by integrating multiple data types, the system can analyze the user's situation more comprehensively and accurately, improving the accuracy and personalization of the diagnosis. The present invention combines the diagnostic opinions of multiple experts and the user's real-time data to make the final decision, so that the system can generate the most objective and reasonable diagnostic results and medication plans based on the comprehensive opinions of multiple parties, further improving the reliability of diagnosis and treatment; the present invention overcomes the limitations of traditional medication guidance methods by introducing dialectical analysis of multiple expert diagnostic opinions and real-time feedback information from users and experts, and realizes more scientific, objective and accurate medication guidance after liver transplantation, making up for the shortcomings of existing technologies in personalized treatment. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 This is a framework diagram of a liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning in the present invention.

[0028] Figure 2 This is the BERT model structure diagram;

[0029] Figure 3 This is the structure diagram of the multi-head attention mechanism

[0030] Figure 4 This is the flow chart of the expert diagnosis opinion collection unit of the data management module;

[0031] Figure 5 Flow chart of the divergence identification unit of the diagnostic analysis module;

[0032] Figure 6 This is the flow chart of the user feedback unit of the feedback module. DETAILED DESCRIPTION

[0033] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0034] The present invention proposes a liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning. Figure 1 As shown, the system includes: a data management module, a diagnosis and analysis module, a medication suggestion module and a feedback module.

[0035] Preferably, the main task of the data management module is to collect, store and manage the user's health data and the diagnostic opinions of multiple experts in a standardized way, and store relevant medical literature and clinical data. This module processes data through a series of advanced algorithms to ensure that the system fully acquires and effectively manages multi-source data, and ensures 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 diagnosis opinion collection unit and a medical literature storage unit.

[0037] The user indicator collection unit uses a variety of data collection methods to collect a variety of user health data, covering liver imaging data (such as MRI, CT scans), basic physiological data (such as blood indicators, etc.), immune status, and in addition, it can also collect user self-reported symptoms or health conditions (other disease history and medication records, eating habits and exercise records) to ensure that the system has a comprehensive grasp of the user's physiological status. The system connects with the hospital information management system (HIS) and laboratory information management system (LIMS) through an integrated API to achieve automatic collection and integration of multimodal data. In order to deal with the problem of inconsistent data formats from different sources, the system uses 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, and its formula is:

[0038]

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

[0040] The expert diagnosis opinion collection unit is used to obtain the diagnosis opinions of multiple experts in the field of liver transplantation. It allows the expert diagnosis opinions to be input in text form. This unit uses natural language processing (NLP) technology, especially the BERT (Bidirectional Encoder Representations from Transformers) model, to perform deep semantic understanding of text content. Figure 2 As shown in the figure, BERT is a pre-trained language model based on the Transformer architecture, the core of which is to capture contextual information through a bidirectional encoder.

[0041] The key components of the BERT model include the calculation formula of 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 is the dimension of the key vector. Through this formula, the model can capture the contextual dependencies in the sentence, ensuring that the expert diagnosis opinions can be accurately understood and converted into standardized medical terms, while matching them with the medical knowledge graph already in the system.

[0044] In order to improve the ability to handle complex semantic dependencies, this unit introduces a multi-head self-attention mechanism based on the BERT (Bidirectional Encoder Representations from Transformers) model. Figure 3 shown.

[0045] The multi-head self-attention mechanism can calculate different features in multiple subspaces in parallel and capture deeper semantic associations. Specifically, the input expert diagnosis 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] Among them, X is the input word embedding matrix, W Q , W K , W V is a learnable linear transformation matrix. Subsequently, multiple heads calculate the attention distribution in parallel:

[0048]

[0049] Each head captures different levels of semantic information, and finally the outputs of multiple heads are 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, ensure that diagnostic opinions can be accurately converted into standardized medical terms, and efficiently match them with the system's existing medical knowledge graph to provide more reliable diagnostic support.

[0052] The process of collecting expert opinions in the expert diagnosis opinion collection unit is as follows: Figure 4 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, which serves as an important basis for subsequent reasoning and analysis.

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

[0055] The core task of the diagnostic analysis module is to integrate the diagnostic opinions of multiple experts, identify the differences, and combine relevant medical literature for logical reasoning to generate comprehensive diagnostic information. This module uses an intelligent reasoning mechanism to ensure that the generated diagnostic results have scientific basis and logical coherence, and enhance the accuracy and explainability of the diagnosis.

[0056] The disagreement identification unit is responsible for processing different opinions in expert diagnostic opinions, and conducts in-depth analysis of these opinions through a knowledge graph-based disagreement identification algorithm to determine the potential impact of these disagreements on user treatment decisions. The system maps expert opinions to corresponding medical knowledge nodes through a pre-built liver transplant disease knowledge graph to form semantically associated graph structure data. Then, the system uses a combination of graph convolutional networks (GCN) and graph attention networks (GAT) to calculate semantic similarity and importance of expert opinions to ensure the accuracy of disagreement identification.

[0057] Specifically, if Figure 5 As shown in Figure 1, we first calculate the semantic similarity of different expert opinions through GCN to capture the potential associations and differences in expert opinions. GCN propagates the semantic features between nodes by aggregating the neighbor information of nodes in the graph. Its core formula is:

[0058]

[0059] Where: H (l)represents the node representation matrix of the lth layer, W(l) is the weight matrix of the lth layer, is the normalized adjacency matrix, is the degree matrix, and o is the nonlinear activation function.

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

[0061] In order to further improve the accuracy of disagreement identification, the system introduces the Graph Attention Network (GAT). GAT allows the model to adaptively assign different attention weights to different expert opinion nodes according to their importance, so as to highlight the role of key opinions. The core formula of GAT is:

[0062]

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

[0064]

[0065] Through this mechanism, the system can more accurately identify and highlight the key divergent information in different expert opinions. In addition, the system also adopts a multi-head attention mechanism to capture the different feature dimensions of expert opinions through multiple parallel attention heads, thereby improving the model's ability to express the diversity of opinions.

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

[0067] The reasoning generation unit uses an evidence-based reasoning system combined with a knowledge graph to perform reasoning analysis on the identified disagreements. The unit extracts supporting evidence from relevant medical literature, clinical data, and user health data, and conducts a comprehensive evaluation of different diagnostic opinions through an evidence-based reasoning system.

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

[0069] 1. Meta-gram each document and generate a global semantic vector V for each document through the BERT model doc (Based on the [CLS] tagged vector).

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

[0071] 2. The system converts the expert’s diagnostic opinion into a semantic vector V CLS , and by calculating the document semantic vector V doc The similarity between the vector and the diagnostic opinion vector is used to determine the recommended literature.

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

[0073]

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

[0075] In order to further improve the accuracy of document recommendation, the system also integrates the Doc2Vec model. This model can recommend documents based on similarity by mapping documents into a low-dimensional vector space. Its objective function is:

[0076]

[0077] Among them, P(w|d) is the probability of predicting word w given document d. This model captures the latent semantic relationship of documents and dynamically recommends documents related to the current diagnosis for system reference.

[0078] The reasoning system uses the Bayesian reasoning model to calculate the possibility of each diagnostic path. The basic formula of Bayesian reasoning is:

[0079]

[0080] Among them, P(H|E) represents the posterior probability that hypothesis H is true when evidence E appears, P(E|H) represents the possibility of evidence E appearing when hypothesis H is established, 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 selects the most supportive path in combination with medical evidence in the literature as the final comprehensive diagnosis result. This reasoning process ensures that the diagnosis result is not only logically coherent, but also can be supported by sufficient medical literature, enhancing the scientificity and credibility of the diagnosis.

[0081] Preferably, the medication suggestion generation module includes a primary medication regimen 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 and the user's health status and feedback, and continuously optimizes the medication plan through a dynamic feedback mechanism.

[0083] The primary medication plan generation unit uses a decision tree algorithm. The unit inputs comprehensive diagnostic information into the decision tree model, and combines the user's individual physiological data (such as age, weight, liver function status, etc.) to generate a personalized preliminary medication plan that meets the user's physiological characteristics and disease status. The decision tree model recursively divides the data set and classifies the user's individual information into multiple levels to generate drug recommendations that suit the user's individual needs. For immunosuppressants that may be used after liver transplantation, the decision tree model will generate personalized dosage recommendations based on the user's immune status, drug metabolism, etc. The splitting of the decision tree model is based on the principle of maximizing information gain, and the information gain calculation formula is as follows:

[0084]

[0085] In this formula, IG(T,A) represents the information gain of feature A on data set T; H(T) is the entropy value of data set T, and Tv is the subset when feature A takes different values. By calculating the information gain, the decision tree selects the optimal feature for node division until the final personalized medication recommendation is generated.

[0086] The optimization unit uses the Deep Q-Networks (DQN) algorithm in reinforcement learning to dynamically adjust the medication plan by scoring the user's medication feedback (user feedback information, health data after medication, 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 optimize medication decisions based on the user's health feedback. The formula for updating the Q value 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 (such as the improvement of the user's physiological indicators after taking the medicine), γ is the discount factor, which is used to balance the weights of current and future rewards, and α is the learning rate, which controls the update step size. The system adjusts the medication regimen based on the feedback signal after each medication. For example, when the user's blood indicators improve, the system will update the Q value accordingly and continuously optimize future medication decisions through the deep reinforcement learning model. Through the feedback mechanism of reinforcement learning, the system can ensure that the best medication regimen is provided 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 the user's health data and feedback information after taking the medication, as well as the expert's 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 meet the individual needs of the user to the greatest extent possible.

[0091] like Figure 6 As shown, the user feedback unit monitors the user's health data after taking the medicine (such as blood sugar, liver function indicators, etc.) and user feedback information (such as the effect of the medicine) in real time. The user's health data after taking the medicine includes the health data from the past period of time after the user took the medicine to the current moment, as well as the health data predicted for a period of time in the future. In order to more accurately predict the user's health change trend and identify potential health risks, the system uses a bidirectional long short-term memory network (BiLSTM) combined with an attention mechanism to enhance the analysis and feedback recognition capabilities of health data.

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

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

[0094] 1.1 Calculation formula of forward LSTM

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

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

[0097] Among them, x t is the input health data, is the hidden state of the forward LSTM at time step t-1.

[0098] 1.2 Calculation formula of backward LSTM

[0099] The backward LSTM passes through the current input data x t and the hidden state at the next time step Calculate the hidden state at the current time step:

[0100]

[0101] in, is 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 is able to capture the complex temporal dependencies in user health data, not only focusing on past information but also gaining insights from future data, thereby improving the accuracy of predictions on health change trends.

[0106] 2. Introduction of Attention Mechanism

[0107] In order to further improve the model's ability to capture key information in health data, the system introduces an attention mechanism that enables 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 time series data.

[0108] 2.1 Core formula of attention mechanism

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

[0110]

[0111] Among them, e t is based on the BiLSTM hidden state h t and the attention weight matrix W a The calculated attention energy:

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

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

[0114] 2.2 Weighted BiLSTM Output

[0115] Based on the attention weight α t , perform weighted summation on the BiLSTM outputs of all time steps to generate the final weighted output representation:

[0116]

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

[0118] 3. Personalized optimization of user feedback

[0119] By combining BiLSTM with the attention mechanism, the system can not only identify the user's current health status, but also provide personalized health trend predictions based on the individual's long-term data. This prediction includes a dynamic assessment of future health status, and the system automatically updates the 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 regularly inviting experts involved in the diagnosis to review the current medication regimen, and analyzes the expert feedback on the medication regimen through an automated text processing model and generates a recommendation feedback report. In order to extract key information from expert feedback, the system uses the BERT model. The BERT model is used to measure the importance of a term in a set of texts, thereby automatically identifying the expert's recommendations on medication regimens. The calculation process of BERT is as follows:

[0121] 1. The text of expert feedback 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 separation tags [SEP] are added. For each token ti, BERT will generate a corresponding vector representation V(t i ), which combines the semantic information of the word in the current context.

[0123] 2. Each layer of BERT is composed of Transformer encoders, and the vector representation of each word is processed through multiple layers of encoders to capture the context 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 word. Q, K, and V are the query vector, key vector, and value vector, respectively, which are calculated through the self-attention mechanism. Transformer encodes the contextual association of each word through a multi-layer self-attention mechanism to generate a more semantically expressive vector V′(ti ).

[0126] 3. BERT adds a special classification tag [CLS] at the beginning of each input text. The vector representation of this tag can be used as the global semantic representation of the entire text. By taking the output vector V of the [CLS] tag CLS , and obtain the overall semantic representation of the expert feedback text.

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

[0128] 4. In order to extract key information from expert feedback, the similarity between \(V_{CLS}\) and the vector representation of predefined drug regimen-related terms or concepts can be calculated. The commonly used similarity measurement formula is as follows:

[0129]

[0130] Among them, V concept is a vector representation of predefined concepts or drug terms, and the cosine similarity between the semantic representations of expert feedback and the terms is calculated to identify the most relevant drug regimen suggestions.

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

[0132] Through this model, the system can automatically extract the opinions of experts on the adjustment of medication regimens, such as suggestions for adjusting drug dosage or frequency of use. These extracted key information will be incorporated into the optimization unit to continuously adjust and optimize the medication regimen to ensure the effectiveness and personalization of the regimen.

[0133] Through the above detailed design and implementation, the system can effectively improve the quality of medication decisions for liver transplant users, optimize treatment outcomes, and reduce the risks caused by differences in diagnostic opinions.

[0134] The medication guidance system designed by the present invention can be connected 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, greatly improving the efficiency of medical services and the user's postoperative quality of life.

[0135] In summary, the present invention has built a comprehensive intelligent liver transplant medication guidance system by integrating multimodal data, advanced algorithms and real-time computing technology. In actual clinical applications, the system can successfully provide personalized postoperative medication guidance for different users, effectively improve postoperative recovery effects, and reduce the risk of complications, showing strong clinical application value and broad promotion prospects.

[0136] The above embodiments further illustrate the purpose, technical solutions and advantages of the present invention in detail. It should be understood that the above embodiments are only preferred implementation modes 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 in the protection scope of the present invention.

Claims

1. A liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning, characterized in that: Contains the following modules: 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 that the system comprehensively acquires and effectively manages multi-source data; Diagnostic analysis module: used to analyze and integrate the diagnostic opinions of multiple experts, perform logical reasoning based on medical literature, clinical data and user health data, and generate comprehensive diagnostic information for users; Medication advice generation module: Generates personalized medication advice based on the comprehensive diagnostic information provided by the diagnostic analysis module and the feedback data provided by the feedback module, and continuously optimizes the medication plan; Feedback module: used to collect user feedback information, health data after medication, and expert feedback on medication plans to support the system's dynamic adjustment and optimization of medication recommendations.

2. According to claim 1, a liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning is characterized in that: The data management module comprises: User indicator collection unit: used to collect user health data; Expert diagnosis opinion collection unit: used to obtain diagnosis opinions from multiple experts in the field of liver transplantation; Medical document storage unit: used to store medical documents and clinical data related to liver transplantation.

3. A liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning according to 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, eating habits and exercise records.

4. The liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning according to claim 1 is characterized in that: The diagnostic analysis module comprises: Disagreement Recognition 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; Reasoning generation unit: performs automated logical reasoning based on divergence recognition results, relevant medical literature, clinical data, and user health data to generate comprehensive diagnosis results.

5. A liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning according to claim 4, characterized in that: The process of the reasoning generation unit performing automated logical reasoning based on the divergence identification results, relevant medical literature, clinical data and user health data includes: finding the medical literature most relevant to the home diagnosis opinion from the relevant medical literature; performing automated logical reasoning based on the divergence identification results, clinical data and user health data in combination with the medical literature most relevant to the home diagnosis opinion to generate a comprehensive diagnosis result.

6. The liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning according to claim 1 is characterized in that: The medication suggestion generating module comprises: Primary medication plan generation unit: based on the comprehensive diagnostic information provided by the diagnostic analysis module, generates a preliminary medication plan that meets the user's physiological characteristics and disease status; Optimization unit: Dynamically optimizes the primary medication regimen based on the feedback data provided by the feedback module to generate personalized optimal medication recommendations.

7. The liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning according to claim 1 is characterized in that: The feedback module comprises: User feedback unit: used to collect the user's health data and user feedback information after taking the medicine, to ensure that the system can grasp the user's real situation in a timely manner; Expert feedback unit: used to collect experts' feedback on medication plans and generate recommendation feedback reports to help the system scientifically adjust and optimize medication recommendations.

8. The liver transplantation medication guidance system based on multi-expert diagnostic opinions and intelligent reasoning according to claim 7 is characterized in that: The user's health data after taking the medicine includes the user's health data from the past period after taking the medicine to the current moment and the health data predicted for a period of time in the future.

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