Application of Propofol in Oral and Maxillofacial Surgery Anesthesia Based on Smart Medical Blockchain Technology.

The rapid development of intelligent computer technology has promoted the improvement of people's living standards and the application of the entire intelligent system. In modern medicine, doctors use their accumulated experience and medical knowledge to diagnose diseases and draw conclusions. In order to effectively inherit the diagnosis experience accumulated by doctors, R&D personnel put forward the idea of using artificial intelligence technology to develop an intelligent auxiliary medical diagnosis system. Aiming at the abovementioned machine-learning problems in the medical field, this article mainly introduces the application research of propofol in oral and maxillofacial surgery anesthesia based on smart medical blockchain technology. This paper proposes a research method based on smart medical blockchain technology to assist propofol in oral and maxillofacial surgery anesthesia, including support vector machine data classification algorithm, decision tree data classification algorithm, and machine-learning-based LSTM neural network. Research experiments on the application of intelligent medical aid propofol in oral and maxillofacial surgery anesthesia are conducted. The experimental results in this paper show that the response time of the general business logic interface of the application system of propofol in oral and maxillofacial surgery anesthesia based on smart medical blockchain technology is about 41 milliseconds, which can better help doctors in anesthesia and related treatments.

1. Introduction

Blockchain technology is a database that forms a chain structure through the hash index and is operated by multiple parts. The blockchain uses multiple nodes and distributes multiple accesses to the data, thereby reducing the dependence on the central Internet server and avoiding the possibility of the central H server point being destroyed due to data and data loss. Encryption technology is used to ensure its integrity and ensure that the data files stored in the blockchain are not tampered with. Based on the characteristics of blockchain technology and medical systems, this article designs a blockchain-based intelligent medical rescue system based on the limitations of traditional medical information file storage. The system can help doctors diagnose and treat and improve efficiency and accuracy.

During oral and maxillofacial surgery, because the operation area is close to the airway, postoperative bleeding, local swelling, or improper postoperative treatment can easily cause airway obstruction. On the other hand, preventive tracheotomy is not accepted by patients and their families, and the tracheal tube is removed prematurely, which may bring fatal consequences. With the advancement of ICU sedation technology, postoperative tracheal intubation to maintain spontaneous breathing in the ICU has gradually become the mainstream, improving medical safety and speeding up patient recovery. Propofol is a very good transient agonist, and it can exhibit a sedative and anesthetic effect. Therefore, it is of great significance to study the application of auxiliary prevention based on smart medical blockchain technology in oral and orthopedic anesthesia.

Tian found that, with the development of information technology, the concept of smart medicine gradually emerged. Smart healthcare uses new-generation information technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence to comprehensively transform the traditional medical system and make medical treatment more efficient, convenient, and personalized. In order to introduce the concept of smart medical care, Tian first listed the key technologies that support smart medical care and introduced the development status of smart medical care in several important fields, then explained the problems of smart medical care, and proposed corresponding solutions, finally looking forward to the future prospects of smart medicine. This research is theoretical but not scientific enough [1]. Yunfei believes that smart medical care, as a new medical model, can provide patients with more efficient and higher-quality medical services. However, smart medical care requires patients to share their own physiological information for online diagnosis. If the smart medical-data-sharing system lacks an effective security mechanism, this sensitive information may be abused by illegal or malicious users. In addition, smart healthcare needs to face some brand-new challenges, such as resource-constrained Internet of Things, identity theft attacks, and internal attacks. In order to solve these problems, Yunfei proposed a security implementation framework of an SDN-based smart medical-data-sharing system. Under this framework, each patient has a dedicated virtual machine in the data-sharing system and each virtual machine provides a set of data services, which can be released to authorized service consumers or things in the Internet of Things. In addition, the virtual machine is protected by an SDN-based gateway, which provides a firewall mechanism and guarantees that only authorized things can access the patient's virtual machine. In order to verify the validity and feasibility of the framework, Yunfei used a POX controller and a Mininet simulator to implement an experimental system. Experimental results show that the framework is effective in different test scenarios. As the scale of the information flow model increases, the framework can still work well. This study lacks experimental data support and is weak in persuasiveness [2]. Gili found that although anesthetics are routinely used during surgical procedures, experts have not yet reached a consensus on the exact mechanism of hypnotic anesthetics. Molecular, animal, and human studies have shown that thalamic cortical communication disorders are an important part of anesthetics at the level of the brain system. For this reason, Gili used the anesthetic propofol to regulate consciousness and evaluate the differences in the interaction of remote neural networks during the change of consciousness. He used functional magnetic resonance imaging (fMRI) to study the effects of propofol on spontaneous brain activity in 15 healthy volunteers. This dose can produce mild sedation without loss of consciousness and use blood-oxygenation-level-dependent effects. Oscillation (<0.1 Hz) sends signals across functionally connected brain regions, treats data as a graph or a complex network of nodes and links, and uses eigenvector centrality (EC) to characterize brain network properties. The EC mapping of fMRI data of healthy people under mild sedation of propofol showed that the decrease of thalamic center points was related to the increase of brainstem pontine center points, highlighting the important role of these two structures in regulating consciousness. Specifically, the decrease in thalamus centrality is caused by its separation from the extensive cortex and subcortical areas, while the increase in brainstem centrality may be due to its effect on some high central cortical areas under mild sedation. This experiment is a good study of the relevant principles of the action of propofol, but the practicality is not high [3].

The innovations of this article are as follows: (1) support vector machine data classification algorithm and decision tree data classification algorithm for smart medical assistance systems are proposed; (2) machine-learning-based LSTM neural network for smart medical assistance systems is proposed; (3) an application system based on smart medical blockchain technology is designed to assist propofol in oral and maxillofacial surgery anesthesia.

2. Method for the Application of Propofol in Oral and Maxillofacial Surgery Anesthesia Based on Smart Medical Blockchain Technology

In recent decades, the level of medical care has changed and improved with each passing day. A large amount of medical data and information are generated in the diagnosis process. Due to the introduction of a large number of medical machinery and equipment in the diagnosis or treatment process and endless styles of medical equipment, a large number of different types of medical data information are generated [4]. This results in doctors not only having to deal with a large amount of consulting work in their daily work but also analyzing a large amount of medical data without guaranteeing their leisure needs, leading them to work under great pressure. As a result, the chance of misdiagnosis will increase at the time of diagnosis. However, after the introduction of computer processing technology, as the automatic analysis and processing of medical data information accelerates, the demand for useful information is also increasing [5].

With the addition of large-scale medical equipment, a large number of medical images have also been produced. This type of image is different from ordinary pictures. It contains a large amount of data. How to extract important information from these important data has become the key to breaking the disease. At present, doctors in major hospitals have headaches [6]. Nowadays, image data mining technology is commonly used and proven effective. Through the use of special computers to analyze and process medical image information, it can reduce the workload of doctors and increase the accuracy of propofol in oral and maxillofacial surgery anesthesia [7].

2.1. Support Vector Machine Data Classification Algorithm

When the support vector machine model solves the two types of classification problems, it will look for an h − 1-dimensional hyperplane in the h-dimensional sample feature space as the separation plane of the two types of samples. This plane is usually called a linear classifier. When the samples can be distinguished correctly, they are said to be linearly separable [8]. When it is necessary to deal with the case of linear inseparability, SVM will map the sample points to a higher-dimensional or even infinite-dimensional space. At this time, this mapping is nonlinear, so the sample points will become linearly separable in the high-dimensional space [9]. In this case, using the K(x, y) function that satisfies the Mercer condition as the inner product operation of the two sample features is equivalent to mapping the sample from the original feature space to a new feature space [8]. Given a training sample T={(x1, y1), (x2, y2),…, (x, y)},  y ∈ {−1,1} [10, 11]. In the sample space, the classification hyperplane can be expressed by the following formula:

In this formula, x is the feature vector of the i-th sample, y is the sample label, W is the normal vector that determines the direction of the hyperplane, and b is the displacement term that determines the distance between the hyperplane and the origin. To determine a classified hyperplane is to determine W and b [12]. In order for the classification to correctly classify all samples and have the classification interval, the following relationship needs to be satisfied:

At this time, the problem of the optimal classification surface is transformed into the problem of minimizing W · W under constraints. This is a strictly convex programming problem under inequality constraints, and there is a unique solution (W, b) [13, 14]. To solve this problem, the Lagrangian function is usually defined as the objective function, and a is the nonnegative Lagrangian coefficient. The following formula exists:

The first-order partial derivatives of L(W, b, a) with respect to W and b are found and made equal to zero [15]. The Lagrangian coefficient a and the hyperplane normal vector satisfy the following condition:

Substituting these two constraints into formula (3), the original linear programming problem is transformed into a relatively simple dual problem [16]. That is, the maximum value of the objective function is solved for a under the following constraint condition:

According to the duality principle, the original problem has an optimal solution (W, b), so its dual problem also has an optimal solution [17]. The solution of this optimization problem must satisfy the following condition:

The model complexity of the support vector machine is determined by the number of support vectors. The optimal classification function obtained after the solution is

Using the function K(x, y) that satisfies the Mercer condition as the inner product operation of two sample features is equivalent to mapping the sample from the original feature space to a new feature space [18]. At this time, the corresponding optimal classification function is defined as

2.2. Decision Tree Data Classification Algorithm

Among classification data mining algorithms, decision tree algorithm is the most commonly used algorithm. The decision tree algorithm combines the classification process of the tree shape and adaptation problem [19]. There is a shared attribute in the specified tree, that is, each layer corresponds to a classification attribute. The nodes in the layer have different attribute values, and the corresponding data in the attribute values are stored in the nodes. Each node stores the probability distribution of different types of label attributes on the branch line [20]. Suppose that the current node is V, the training data set that reaches V is L, there are k different class labels C(i=1,2,…, k), let C be the tuple set of class label C in L, |L| and |C| are the number of tuples, and a certain division L is divided into y subsets {L1, L2,…, L} [21, 22].

2.2.1. Information gain

The information gain on a certain division attribute N is defined as the difference between the amount of information (entropy) needed to identify tuples before division and the amount of information needed to identify tuples after division on attribute N [23]. There is the following relationship:

2.2.2. Gain Rate

The information gain metric tends to use attribute partitioning with more branches, and the gain rate metric is adopted, which uses the split information value to normalize the information gain [24]. The definition formula of split information is as follows:

The gain rate is defined as

2.2.3. Gini Indicator

The Gini index is the measurement criterion used in the CART algorithm. The Gini index measures the impurity of the data partition or the training tuple set L [25, 26]. Its definition formula is

The Gini index considers the binary division of each attribute. Assuming that L is divided into L1 and L1 for a certain binary division of attribute N on L, the Gini index of this division is defined as

The decrease in impurity due to this division is defined as

Each time the attribute that can maximize the reduction of impurity is selected as the splitting attribute; this attribute and its split subset (discrete value attribute) or split point (continuous value attribute) together form the splitting criterion [27].

2.3. LSTM Neural Network Based on Machine Learning

The LSTM neural network is a variant of recurrent neural networks. It mainly adds Forget Gate to the RNN to solve the long-distance dependence of the RNN. For the LSTM model, the hidden layer block unit is the most important, which is constructed with three “gate” designs [28]. The purpose of the hidden layer block is to filter (that is, add or delete) whether the cell can pass through the three kinds of “gates” when the data information passes through. At present, the names of these three useful “gates” are input gate; forget gate; and output gate [29, 30].

The input gate calculation formula is as follows, where i is the number of nodes in the input layer, H is the number of hidden layer nodes, and C is the number of cell nodes:

The calculation formula of the forget gate is as follows, where i is the number of nodes in the input layer, H is the number of hidden layer nodes, and C is the number of cell nodes:

The cell state calculation formula is as follows, where i is the number of nodes in the input layer of t and H is the number of nodes in the hidden layer:

The output gate calculation formula is as follows, where i is the number of nodes in the input layer, H is the number of hidden layer nodes, and C is the number of cell nodes:

The method part of this article uses the abovementioned method to assist the application research of propofol in oral and maxillofacial surgery anesthesia based on smart medical blockchain technology. The specific process is shown in Figure 1.

Figure 1

Part of the technical process of this method.

3. Design of the Application System of Intelligent Medical-Assisted Propofol in Oral and Maxillofacial Surgery Anesthesia

3.1. System Design Principles and Goals

3.1.1. Stability and Reliability

Stability and reliability are the most important quality characteristics of an information system. The application of clinical pathways will effectively reduce patients' medical expenses, reduce patient stays, improve the standardized procedures of hospitals, and provide doctors with diagnosis and treatment assistance. The system should consider the design, architecture, equipment selection, manufacturer's responsiveness and maintenance capabilities, and the ability to provide spare parts to ensure that the possibility of failure of the information system is minimized and the impact is minimized. Also, there are contingency plans for emergency situations.

3.1.2. Scalability

The system should ensure rapid migration and expansion.

3.1.3. Ease of Use

The users of the clinical pathway have many tasks and strong normative guidance. Therefore, the system construction should meet the business needs as the primary goal. Under the premise of practicality and reliability, it should be as convenient as possible for users to operate, save the time of medical staff, and improve the efficiency and ease of use of the system. The interface layout, operating procedures, business logic, and professional language of the clinical route must conform to the doctor's guiding habits, clinical thinking rules, and diagnosis and treatment procedures and be organically integrated into the doctor's workplace. It can effectively improve the effectiveness and quality of clinical diagnosis and treatment and improve the quality and effectiveness of medical care management.

3.1.4. Standard

The standardization of the system is the most basic and most critical link in system construction.

3.1.5. Advancement

The system adopts domestic and foreign advanced computer software and hardware technology, network communication technology, and information processing technology, has a high cost performance, and ensures the efficient operation of the entire information system.

3.1.6. Security

The system should follow information security standards, and the clinical pathway system should have very strong data security (to prevent batch theft), privacy, and statistical security to prevent illegal use and forgery of clinical data. In order to ensure the overall security of the system, we must thoroughly check the security functions of the application software from the aspects of application software, database, operating system, network function, identity verification, etc. to prevent intrusion of illegal users, reduce the possibility of failure of legitimate users, ensure the authenticity, completeness, consistency, and accuracy of the data, and provide a reliable mechanism for migration and data conversion to prevent data loss, duplication, inconsistency, omission, and other serious problems that affect the data quality during the conversion or conversion process.

3.2. Database Management System Design

The database system is the general condition of the database and database management system. The database is a general and comprehensive data collection with a specific model, a minimum remaining amount, and a high degree of independence and is used to provide different users in a specific range for common use. The database management system is an operation and management system for adding, deleting, modifying, and restoring databases and is the software for managing and maintaining databases.

3.2.1. Design of a Data Structure Table of System Knowledge Base

After collating the system knowledge base data, SQL-Server2000 is used to create data tables. Relevant knowledge tables are designed in the system knowledge base; this method is used to establish the corresponding data tables; due to limited space, we will not repeat them here. After creating the data table, you can enter data into the SQL-Server2000 environment. By determining a reasonable data table structure and storing the corresponding knowledge in the table, the knowledge of the system can be stored in the data. Combined with database access technology, through the database management module, functional operations such as the management and maintenance of the knowledge base can be realized.

3.2.2. ADO Database Access Technology

The system stores the knowledge in the knowledge base in the database through the establishment of a data table. The knowledge base is the basis of system reasoning. Therefore, it should be combined with database access technology to realize the access to the database in the program. This system uses ADO database access technology because it not only has high efficiency in accessing data sources but is also easy to use and has certain advanced features.

ADO is an object controller, independent of the language (language independent) provided by Microsoft to access the programming interface. Although oledbapi is used to access low-level data, it only supports C and C++. ADO uses oledbapi to achieve high-level data access. At the same time, it also provides access technologies in multiple languages, such as visualc++, visualbasic, and Delphi. The main advantages of ADO are it is easy to use and has fast speed, low storage cost, and small amount of residual disc.

A typical ADO programming sequence starts with the establishment of a connection with the database. Generally, an ADOConnection object is shared and reused by multiple instances of other ADO objects. The oral and maxillofacial surgery anesthesia diagnosis database is taken as an example to realize the connection of ADO.

3.2.3. Software Design of the Database Management Module

The database management module is an interactive interface between the database and the user, so the design of this module is directly related to the user's use of the database. Programming and accessing SQL-Server2000 database files through the ADO database access technology interface in the Delphi environment can achieve the following functions:

Data maintenance function: users can add, delete, and modify data to the database to achieve continuous improvement of the database.

Data query operation: after the user submits a query request to the system, the database management system will put the data into the database to find the item that meets the conditions of the problem. When querying, the database is only allowed to be opened for reading to ensure that the data in the database does not change.

The data browsing function is used to quickly browse the database.

This part of the experiment proposes that the abovementioned steps are used to assist the application research experiment of propofol in oral and maxillofacial surgery anesthesia based on smart medical blockchain technology. The specific process is shown in Table 1.

Table 1

Experimental steps in this article.

Design of the application system of intelligent medical-assisted propofol in oral and maxillofacial surgery anesthesia	3.1	System design, principles, and goals	1	Stability and reliability
			2	Scalability
			3	Ease of use
			4	Standard
			5	Advanced
			6	Safety
	3.2	Database management and system design	1	Design of a data structure table of system knowledge base
			2	ADO database access technology
			3	Software design of the database management module

4. Application of Propofol in Oral and Maxillofacial Surgery Anesthesia Based on Smart Medical Blockchain Technology

4.1. Auxiliary System Performance Analysis

The test data set is divided into three different groups; first, the original training set is tested, the training set of the system is directly used as the test data for testing, and the final result is obtained; the other group is to select part of the data from the test set as the test set. It is run directly to analyze the results; the last set of data makes the test data set and the training set independent of each other, without any overlap of data. The training set is the medical record data in all cases. After randomly sampling 412 cases, it is found that the test data volume accounts for 93% of the total data volume, and the rest of the data is used as the test set. Each group was tested 15 times, and the results are averaged and stored in Table 2.

Table 2

Generalization ability of the model.

	Test set = training set (%)	Training set ∈ test set	Two episodes independent (%)
Decision tree	95.47	90.635	89.72
SVM	97.12	92.34%	90.61
LSTM network model	99.07	93.26%	92.68

Considering that some small hospitals have not received many patients for diagnosis and treatment, the impact of the amount of data on the performance of the auxiliary diagnosis model should be considered. The training set is randomly selected for each test. According to the abovementioned analysis method, the specific operation is handled as follows: data are selected from the training set according to a certain proportion for testing. In our experiment, we test three situations separately: the amount of data contained accounts for 90%, 80%, and 70% of the total training set data. The accuracy of the auxiliary diagnosis model when matching different numbers of data instances is judged, each case is tested 15 times, and the final results are averaged and filled in the tables . Because the application of the auxiliary diagnosis model is sometimes in small township hospitals, these hospitals are small in scale, resulting in a small amount of data that can be used for testing, so the amount of data may affect the performance of the system. In our experiment, we successively reduce the number of instance data and finally analyze the size of the impact based on the experimental results. The specific effects are shown in Table 3 and Figure 2.

Table 3

The impact of training set data volume on classification results.

	Training set = 90 (%) total	Training set = 80 (%) total	Training set = 70 (%) total
Decision tree	96.25	90.52	75.31
SVM	95.50	93.36	72.09
LSTM network model	99.41	89.16	77.23

Figure 2

The impact of training set data volume on classification results.

The test results show that the amount of training set data has a relatively large impact on the neural network diagnosis model. The results obtained when the LSTM network is running have a great relationship with the training data. When the test data are more different from the original training data when they are large, the final effect changes greatly.

The accuracy of the algorithm is tested for load prediction under a certain random load pressure. In the test, the query rate per second is selected as the pressure indicator. A random number of requests are sent to the system within 30 minutes, and the load value predicted by the algorithm and the actual load value are recorded. The specific results are shown in Table 4 and Figure 3.

Table 4

Algorithm test.

Time (unit: minutes)	Predicted load	Actual load
0	354	411
5	981	1106
10	743	792
15	624	674
20	276	257
25	709	841
30	254	246

Figure 3

Algorithm test.

Therefore, it can be seen from the figure that the predicted load value basically conforms to the actual load value curve, so the load algorithm based on multiple indicators can meet the requirements of the predicted load in the service automatic scaling.

Taking the system response time as the evaluation index, the response of the system under three different pressures is given. The operating conditions of the system under different pressures are shown in Table 5 and Figure 4.

Table 5

Stress test.

QPS	Duration (min)	Average response time (ms)	Percentage (%)
500	15	2481	25.55
1500	15	3014	31.03
3000	15	4217	43.42

Figure 4

Stress test.

It can be seen from the chart that the smart medical assistance system works well under three different stress conditions in the test environment.

The system business interface is tested, and the specific conditions are drawn into a chart, as shown in Table 6 and Figure 5.

Table 6

Interface performance test.

Interface	Response time (ms)
	Decision tree	SVM	LSTM network model
1	21	24	19
2	34	31	26
3	26	46	24
4	29	27	25
5	42	38	31
6	51	46	37
7	47	57	41

Figure 5

Interface performance test.

It can be seen from the chart that the response time of a general business logic interface is about 41 milliseconds, while some interfaces have a longer response time. Analyzing the business logic processed by the interface and the length of the JSON data returned by the interface shows that when the business logic processed by the interface is more complex, you need to query the database multiple times and perform data integration, which takes a long time; Also, when there are many fields, the response time of the interface is mainly consumed in data transmission. Therefore, the main factors that affect the length of the interface response time are the complexity of the business logic and the length of the returned data. Therefore, we consider caching hot data with frequent queries and complex query logic in Redis to reduce the number of database visits and the response time of system requests.

4.2. Application Analysis of Propofol in Oral and Maxillofacial Surgery Anesthesia Based on Smart Medical Blockchain Technology

The intelligent medical assistance system designed in this article is based on the intelligent medical blockchain technology to assist the application of propofol in oral and maxillofacial surgery anesthesia, three sets of applications and analysis, and observe the use of propofol to anesthetize patients with the aid of the system The related adverse reactions are sorted out, and the specific conditions are shown in Table 7 and Figure 6.

Table 7

Adverse reactions in the three groups of patients.

Observation index	Label	First group (%)	Second group (%)	Third group (%)
Hypotension	1	11.2	6.3	8.2
Bradycardia	2	6.3	4.2	5.6
Respiratory depression	3	2.2	4.5	5.3
Tongue fall	4	3.1	3.7	4.7
Injection pain	5	8.7	9.4	7.6
Myoclonus	6	10.2	8.6	9.2
Know during surgery	7	6.4	5.7	4.8
Postoperative irritability	8	2.1	3.2	2.6
Postoperative nausea and vomiting	9	2.4	2.6	1.9

Figure 6

Adverse reactions in the three groups of patients.

Propofol is a hypnotic intravenous anesthetic that is widely used in painless diagnosis and treatment. Propofol is a derivative of alkylphenol, which has high fat solubility and is not easily soluble in water. The preparation currently used clinically is an emulsion. The anesthetic mechanism of propofol has not been fully elucidated. It is currently recognized that it binds to the β subunit of the GABAA receptor to produce anesthesia. If propofol is combined with opioid anesthesia induction, the incidence of apnea will be greatly increased, and the duration of apnea can be as long as 60 s, but normal breathing can be restored soon. This study also found that the respiratory rate and pulse oxygen saturation of the propofol group decreased significantly after induction of anesthesia than the other two groups, and the incidence of respiratory depression was significantly higher than that of the other two groups. However, when the patient suffers from respiratory depression, he can resume breathing about 1 min after raising his jaw and pressurizing the mask, maintaining SpO2 above 96%. Although the respiratory depression caused by propofol anesthesia can quickly return to normal breathing after the mask is pressurized and oxygenated, it may have little impact on young people with unimpaired respiratory function, but for patients with respiratory insufficiency or decreased oxygen storage capacity, it may increase the incidence of pulmonary complications. Therefore, patients with respiratory diseases need to be cautious when applying propofol sedation.

5. Conclusions

Smart healthcare is the general term for new medical and health services such as artificial intelligence, Internet of Things, cloud computing, big data, and mobile Internet. The new generation of information technology such as sensor technology is closely related to traditional medical and health services. We used advanced Internet technology, Internet technology, and mobile communication technology to realize the interconnection of medical and health and health and services and realized an effective interaction between personnel and resources through intelligent means to ensure that people receive preventive and therapeutic medical services in a timely manner; smart medical policies are in place. They play an important role in improving the quality of urban life.

Blockchain technology is based on the characteristics of decentralized services of peer-to-peer networks, distributed storage, and collective maintenance of individual nodes in the network. It improves the security of data and can also bring reliable transactions to users and realize medical data sharing and medical interaction.

With the advancement of medical informatization, it is more and more important to establish a complete medical auxiliary system. This article studies how to build a simple medical auxiliary diagnosis system, which aims to reduce the work pressure of physicians, improve the accuracy of diagnosis, and promote the improvement of the overall level of the hospital. The auxiliary medical intelligent diagnosis system is based on the medical data information knowledge base, combined with the doctor's medical experience, as well as the patient's specific disease conditions to be examined, and uses advanced computer processing and analysis technology to help doctors accurately diagnose. It not only solves the problems for patients but also provides a good learning and working environment for doctors who have just participated in the internship, promotes the development of their medical business capabilities, and contributes to the development of the national medical and health industry.