Intelligent recommendation device for clinical informed consent based on BERT model

Abstract

The present invention relates to an intelligent recommendation device for clinical informed consent based on the BERT model, belonging to the field of digital medicine. It comprises: a data acquisition and processing module for collecting and preprocessing patients' historical medical information, medical knowledge, and clinical informed consent samples; a model construction module for obtaining a trained clinical informed consent generation model; a knowledge base construction module for extracting key information and terms through the trained clinical informed consent generation model and constructing a clinical informed consent template library and a term list library; a clinical informed consent generation module for inputting the data of the patient to be tested into the trained clinical informed consent generation model and calling the clinical informed consent template and term list to generate a personalized clinical informed consent; and a doctor-patient interaction platform for providing a chat window. The device automatically generates personalized clinical informed consent forms for patients, improving work efficiency while enabling patients to understand the content more accurately.

Description

Technical Field

[0001] The present invention relates to the field of digital medical technology, and in particular to an intelligent recommendation device for clinical informed consent based on a BERT model. Background Art

[0002] Informed consent forms play a critical role in clinical care, communicating treatment details and risks to patients to ensure they fully understand their treatment. However, existing informed consent forms often rely on physicians to draft them, which is time-consuming and inefficient. Furthermore, they are not personalized to the patient's specific circumstances, resulting in patients being unable to fully and accurately understand the content, which in turn hinders their ability to make the right treatment choices.

[0003] Therefore, an intelligent recommendation device is needed to achieve personalized customization of clinical informed consent forms, thereby improving doctors' work efficiency and helping patients better understand treatment risks and improve patient satisfaction. Summary of the Invention

[0004] In view of the above analysis, the present invention aims to provide an intelligent recommendation device for clinical informed consent forms based on the BERT model to solve the problem that current clinical informed consent forms rely on doctors to write them on the one hand, and are not personalized according to the actual situation of the patients on the other hand.

[0005] The present invention provides an intelligent recommendation device for clinical informed consent based on the BERT model, which includes the following modules:

[0006] The data collection and processing module is used to collect and preprocess the patient's historical medical information, medical knowledge, and clinical informed consent form samples, and send the preprocessed data to the model building module;

[0007] A model building module is used to build a clinical informed consent generation model based on the BERT model, and pre-train the clinical informed consent generation model based on the preprocessed data to obtain a trained clinical informed consent generation model;

[0008] The knowledge base construction module is used to extract key information and terms from the pre-processed data through the trained clinical informed consent form generation model and label them to build a knowledge base. It also builds a clinical informed consent form template library based on the key information in the knowledge base and a term list library based on the terms in the knowledge base.

[0009] A clinical informed consent form generation module is used to input the pre-processed historical medical record information of the patient to be tested into the trained clinical informed consent form generation model and call the corresponding clinical informed consent form template and term list to generate a personalized clinical informed consent form for the patient to be tested;

[0010] The doctor-patient interaction platform is used to display the clinical informed consent form and provide an interaction window for doctors and patients.

[0011] Furthermore, preprocessing the collected data includes:

[0012] Analyze and clean the collected data;

[0013] The cleaned data is segmented using the WordPiece algorithm;

[0014] Encode the segmented data;

[0015] Each word is encoded according to the segmented data. The encoding includes the token corresponding to the word, the sentence information encoding corresponding to the word, and the absolute position information encoding of the word in the sentence.

[0016] Furthermore, the clinical informed consent form generation model includes an encoder layer and a decoder layer. The encoder layer includes a vocabulary embedding module, a sentence embedding module, a position embedding module and a Tm module. The vocabulary embedding module is used to obtain the token corresponding to each vocabulary, the sentence embedding module is used to obtain the sentence information encoding corresponding to the vocabulary, and the position embedding module is used to obtain the absolute position information encoding of the vocabulary in the sentence. The Tm module includes a multi-head self-attention mechanism and a feedforward neural network. The multi-head self-attention mechanism allows the model to consider all other input vocabulary when processing each vocabulary. The feedforward neural network performs a nonlinear transformation on the output of the multi-head self-attention mechanism and outputs a high-dimensional vector sequence. The decoder layer decodes the high-dimensional vector sequence output by the encoder layer and outputs a target vocabulary sequence.

[0017] Furthermore, the expression of the input data of the Tm module is:

[0018] a i =[E(x i ), E Ai , E i ],

[0019] Among them, a i Represents x i The encoded data after the vocabulary embedding module, sentence embedding module and position embedding module, x i represents the i-th word in the sentence, E(x i ) represents x i The corresponding token, E Ai Represents x i The corresponding sentence information encoding, E i Represents x i The corresponding absolute position information encoding.

[0020] Furthermore, the content related to the clinical informed consent form keywords is obtained through the tags of key information to construct a clinical informed consent form template;

[0021] Obtaining a surgical risk level according to the type of surgery, wherein the surgical risk level includes low risk, medium risk, and high risk;

[0022] Three templates are constructed for each type of surgery according to the surgical risk level, and the surgical type and surgical risk level corresponding to the template are used as its label. When the surgical risk level is low risk, the corresponding template does not contain medical history keywords and is supplemented with surgical risks and postoperative care content based on actual conditions. When the surgical risk level is medium risk and high risk, the corresponding template contains medical history keywords and is supplemented with surgical risks and postoperative care content based on actual conditions.

[0023] Furthermore, the patient's medical knowledge level is set to professional level, good level or general level according to the degree of his / her understanding of medical terms; alternative words with different levels of understanding difficulty are set for each term according to different medical knowledge levels, and the surgery type label is marked; terms with the same surgery type label constitute a term list.

[0024] Furthermore, the clinical informed consent form generation module includes a risk level identification unit, a knowledge base calling unit and a model running unit. The risk level identification unit is used to obtain the corresponding surgical risk level based on the surgical type of the patient to be tested; the knowledge base calling unit is used to obtain the corresponding clinical informed consent form template and terminology list according to the surgical type and surgical risk level of the patient to be tested; the model running unit is used to run the trained clinical informed consent form generation model, input the pre-processed data of the patient to be tested into the trained clinical informed consent form generation model and call the corresponding informed consent form template and terminology list to generate a personalized clinical informed consent form for the patient to be tested.

[0025] Furthermore, when the historical treatment plan of the patient to be tested includes similar operations, the differences and similarities between this operation and similar operations are added to the corresponding clinical informed consent template for dynamic update; when the patient to be tested has a history of allergies, the drugs and materials used and their potential allergic reaction risks are added to the corresponding clinical informed consent template for dynamic update; when the patient to be tested has a history of chronic diseases, the relevant chronic disease management recommendations and the risks and complications of the relevant chronic diseases during surgery are added to the corresponding clinical informed consent template for dynamic update, and a personalized clinical informed consent for the patient to be tested is generated based on the updated clinical informed consent template.

[0026] Furthermore, the clinical informed consent form generation model is pre-trained based on the pre-processed data to obtain a trained clinical informed consent form generation model including:

[0027] Get the dataset from the preprocessed data and perform data augmentation;

[0028] The data-augmented dataset is used as the input of the clinical informed consent form generation model, and pre-training is performed according to the pre-training task. The trained clinical informed consent form generation model is obtained by updating the parameters in its network structure.

[0029] Furthermore, the doctor-patient interaction platform includes a front-end module, a communication module, a management module, and a back-end module. The front-end module is used to establish a user interface through a component library, the communication module is used to realize communication between the front-end module and the back-end module by establishing a full-duplex communication channel, the management module is used to manage the user status, and the back-end module is used to realize user authentication and authority control, message processing, and data storage.

[0030] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0031] 1. This invention constructs a clinical informed consent form template and a term list library, and automatically generates high-quality clinical informed consent forms based on the BERT model, thereby improving work efficiency and solving the current problem of low efficiency of relying on doctors to write clinical informed consent forms.

[0032] 2. The present invention customizes a personalized clinical informed consent form for the patient based on the patient's medical history information, medical knowledge level, treatment plan and risk level, so that the patient can understand the medical terms, treatment information and treatment risks more comprehensively and accurately, and thus make the right treatment choice.

[0033] 3. The present invention constructs a clinical informed consent form template library and a term list library by collecting a large number of clinical informed consent form samples, medical literature, and medical standards, and continuously updates the clinical informed consent form template library and the term list library by supplementing new medical literature and medical standards to obtain the latest medical knowledge and terminology, thereby improving the accuracy of generating clinical informed consent forms.

[0034] 4. The present invention provides a chat function for patients and doctors through a doctor-patient interaction platform to conduct consultation and feedback related to clinical informed consent, thereby promoting communication between doctors and patients and improving patient satisfaction.

[0035] In the present invention, the above-mentioned technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of the present invention will be described in the following description, and some advantages will become apparent from the description or be learned through practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] The accompanying drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like parts throughout the drawings.

[0037] Figure 1 This is a block diagram of an intelligent recommendation device for clinical informed consent based on a BERT model according to an embodiment of the present invention;

[0038] Figure 2 A schematic diagram of input data for a clinical informed consent generation model according to an embodiment of the present invention;

[0039] Figure 3 This is a schematic diagram of the model structure for generating a clinical informed consent form according to an embodiment of the present invention. DETAILED DESCRIPTION

[0040] The preferred embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, and are not used to limit the scope of the present invention.

[0041] A specific embodiment of the present invention discloses an intelligent recommendation device for clinical informed consent based on the BERT model. Figure 1 As shown, the device includes:

[0042] The data collection and processing module is used to collect and preprocess the patient's historical medical information, medical knowledge, and clinical informed consent form samples, and send the preprocessed data to the model building module;

[0043] A model building module is used to build a clinical informed consent generation model based on the BERT model, and pre-train the clinical informed consent generation model based on the preprocessed data to obtain a trained clinical informed consent generation model;

[0044] The knowledge base construction module is used to extract key information and terms from the pre-processed data through the trained clinical informed consent form generation model and label them to build a knowledge base. It also builds a clinical informed consent form template library based on the key information in the knowledge base and a term list library based on the terms in the knowledge base.

[0045] A clinical informed consent form generation module is used to input the pre-processed historical medical record information of the patient to be tested into the trained clinical informed consent form generation model and call the corresponding clinical informed consent form template and term list to generate a personalized clinical informed consent form for the patient to be tested;

[0046] The doctor-patient interaction platform is used to display the clinical informed consent form and provide an interaction window for doctors and patients.

[0047] Furthermore, the data acquisition and processing module includes a data acquisition unit and a data processing unit. The data acquisition unit is used to collect the patient's historical medical record information through the electronic medical record system, crawl medical-related knowledge through the Internet, collect clinical informed consent form samples through various medical institutions and send them to the data processing unit; the data processing unit is used to pre-process the received data.

[0048] Specifically, the patient's medical history includes personal information, medical consultation information, examination / imaging information, doctor's orders, diagnosis information, treatment information, medical history information, hospitalization records, and subsequent follow-up information. We crawled a large amount of medical-related knowledge from the internet, including medical terminology, medical standards, disease information, medication information, treatment information, medical literature, medical e-books, and other documents. We also collected a large number of informed consent form samples from various medical institutions, covering a wide range of diseases, treatment options, and medical fields to ensure the comprehensiveness and diversity of the collected data.

[0049] Specifically, disease information includes the definition, symptoms, diagnostic criteria, and treatment methods of the disease; drug information includes the name, purpose, usage, dosage, and side effects of the drug; treatment information includes examinations (such as blood tests, imaging examinations) and their steps and precautions, and treatments (such as surgery, radiotherapy, chemotherapy) and their steps and precautions.

[0050] Furthermore, preprocessing the collected data includes:

[0051] Analyze and clean the collected data;

[0052] The cleaned data is segmented using the WordPiece algorithm;

[0053] Encode the segmented data.

[0054] Specifically, parsing refers to converting data in different formats (such as PDF, Word, and HTML) into a unified data format (e.g., a string). Cleaning involves removing irrelevant symbols, deduplication, standardization, and extraneous information from the parsed data. Removing irrelevant symbols refers to removing symbols that have no semantic impact, such as line breaks, HTML tags, and page numbers. Deduplication refers to removing semantically duplicated parts to avoid redundant information during subsequent searches. Standardization refers to unifying semantically identical terms, such as "ASA" and "aspirin." Removing extraneous information refers to removing information without substantive content, such as headers, footers, and signatures. The WordPiece algorithm is used to segment continuous text (e.g., an examination procedure description) into independent sentences, and sentences into independent words. Each word is encoded based on the segmented data. The encoding includes the token corresponding to the word, the corresponding sentence information, and the absolute position of the word within the sentence. The encoded data serves as the basic unit that the clinical informed consent form generation model subsequently constructed can understand and process.

[0055] For example, the preprocessed data is as follows Figure 2 As shown, [CLS] indicates the start position, [SEP] indicates sentence separation, [E A ] represents the sentence information encoding of the first sentence, [E B ] represents the sentence information encoding of the second sentence. According to the position of “disease” in “heart valve disease”, its absolute position information is encoded as [E1].

[0056] Furthermore, the network structure of the clinical informed consent generation model is as follows: Figure 3 As shown, it includes an encoder layer and a decoder layer. The encoder layer is used to convert the preprocessed data into a high-dimensional vector sequence and input it into the decoder layer. The decoder layer is used to generate a target vocabulary sequence from the output of the decoder.

[0057] Specifically, the encoder layer includes a vocabulary embedding module, a sentence embedding module, a position embedding module and a Tm module. The vocabulary embedding module is used to obtain the token corresponding to each vocabulary, such as Figure 3 "E[CLS]", "E[disease]", "E[SEP]" in the sentence embedding module; the sentence embedding module is used to obtain the sentence information encoding corresponding to the vocabulary, such as Figure 3 The “E” A "; The position embedding module is used to obtain the absolute position information encoding of the word in the sentence, such as Figure 3 "E0", "E1", "E N"; The Tm module includes a multi-head self-attention mechanism and a feedforward neural network. The multi-head self-attention mechanism allows the model to consider all other input words when processing each word. The feedforward neural network performs a nonlinear transformation on the output of the multi-head self-attention mechanism and outputs a high-dimensional vector sequence to capture the semantic and grammatical relationships between words, thereby representing the semantic information of the text. The decoder layer decodes the high-dimensional vector sequence output by the encoder layer and outputs a target vocabulary sequence, such as Figure 3 "T0", "T1", "T N ”.

[0058] Specifically, the expression of the input data of the Tm module is:

[0059]

[0060] Among them, a i Represents x i The encoded data after the vocabulary embedding module, sentence embedding module and position embedding module, x i represents the i-th word in the sentence, E(x i ) represents x i The corresponding token, E Ai Represents x i The corresponding sentence information encoding, E i Represents x i The corresponding absolute position information encoding.

[0061] It is understandable that since the encoder of the BERT model does not rely on convolution operations or loop structures, it cannot capture the position information of a single word when processing a word sequence. Therefore, this application encodes the absolute position information of each word during preprocessing, thereby introducing the position encoding of the word in the encoder layer of the clinical informed consent form generation model. By adding the token corresponding to the word, the sentence information encoding and the absolute position information encoding, the clinical informed consent form generation model can capture the position relationship of the word in the sequence, thereby being able to understand the context information more accurately.

[0062] Furthermore, the pre-training of the clinical informed consent form generation model based on the pre-processed data to obtain a trained clinical informed consent form generation model includes:

[0063] Get the dataset from the preprocessed data and perform data augmentation;

[0064] The data-augmented dataset is used as the input of the clinical informed consent form generation model, and pre-training is performed according to the pre-training task. The trained clinical informed consent form generation model is obtained by updating the parameters in its network structure.

[0065] Specifically, data enhancement is performed by randomly deleting words, randomly replacing words, and inserting synonyms or antonyms. The pre-training task includes mask language model (MLM) and next sentence prediction (NSP). Mask training refers to randomly masking certain input words and then predicting the masked words based on the context to represent the current context, thereby improving the model's ability to understand the context; next sentence prediction training refers to determining whether there is a logical association between two sentences, thereby improving the model's ability to understand the context.

[0066] It should be noted that during the training process, taking into account the particularity of medical texts, such as the diversity of terms and abbreviations, differences in text length, and other factors, this application adopts a multi-round training and dynamic masking strategy to improve the generalization ability and robustness of the model.

[0067] As you can understand, pre-training only fine-tunes the model parameters through training tasks, reducing training time. After pre-training is completed, the trained model can be used to extract information and perform semantic analysis on new pre-processed data.

[0068] Furthermore, the key information includes the type of surgery (name of the surgery), potential risks and complications associated with the surgery, patient information, treatment objectives, expected treatment effects, postoperative care instructions and precautions, etc. The terms include medical terms (such as cardiac arrest, cardiac output, atherosclerosis), surgery-related terms (such as incision, suture, depth of anesthesia), etc.

[0069] Furthermore, newly added medical knowledge-related documents are added to the knowledge base in the form of RAG to update it, so as to supplement the latest medical knowledge and improve the basis for improving the accuracy of generating clinical informed consent forms.

[0070] Furthermore, the content related to the clinical informed consent form keywords is obtained through the tags of key information to construct a clinical informed consent form template.

[0071] Specifically, the keywords of the clinical informed consent form include the patient's name, gender, age, hospitalization number, department, bed number, operation type, purpose of operation, preoperative diagnosis, operation risk, postoperative care, and medical history. The operation risk level is obtained according to the operation type, and the operation risk level includes low risk, medium risk, and high risk. A clinical informed consent form template is constructed based on the above keywords and their related content, wherein three templates are constructed for each operation type according to the operation risk level, and the operation type and operation risk level corresponding to the template are used as its label. When the operation risk level is low risk, the corresponding template does not contain the medical history keyword and is supplemented with the content of operation risk and postoperative care according to the actual situation. When the operation risk level is medium risk and high risk, the corresponding template contains the medical history keyword and is supplemented with the content of operation risk and postoperative care according to the actual situation.

[0072] It should be noted that all clinical informed consent form templates constitute a clinical informed consent form template library, and the corresponding templates can be called through the surgery type and surgery risk level labels.

[0073] Specifically, the patient's medical knowledge level is set as either expert, good, or average based on their understanding of medical terminology. For each term, alternative terms with varying levels of difficulty are assigned based on their medical knowledge level, and the procedure type is labeled. Terms with the same procedure type label form a term list, and all term lists form a term list library.

[0074] It should be noted that when setting replacement terms, it is necessary to ensure that the replacement terms are semantically consistent with the original terms and meet the needs of patients with different levels of medical knowledge.

[0075] For example, the term list is as follows:

[0076]

[0077] It can be understood that the present invention constructs a clinical informed consent form template library and a term list library by collecting a large number of clinical informed consent form samples, medical literature, and medical standards, and continuously updates the clinical informed consent form template library and the term list library by supplementing new medical literature and medical standards to obtain the latest medical knowledge and terminology, thereby improving the accuracy of generating clinical informed consent forms.

[0078] Furthermore, the clinical informed consent form generation module includes a risk level identification unit, a knowledge base calling unit and a model running unit. The risk level identification unit is used to obtain the corresponding surgical risk level based on the surgical type of the patient to be tested; the knowledge base calling unit is used to obtain the corresponding clinical informed consent form template and terminology list according to the surgical type and surgical risk level of the patient to be tested; the model running unit is used to run the trained clinical informed consent form generation model, input the pre-processed data of the patient to be tested into the trained clinical informed consent form generation model and call the corresponding informed consent form template and terminology list to generate a personalized clinical informed consent form for the patient to be tested.

[0079] Specifically, the data acquisition and processing module collects the historical medical information of the patient to be tested and performs preprocessing to obtain the preprocessed data of the patient to be tested, and the preprocessed data of the patient to be tested includes his / her name, age, gender, surgery type, medical history information, drug use information, diagnosis information, disease type, treatment information, and medical knowledge level. The risk level identification unit searches for the corresponding level in the surgery classification catalog (national standard) according to the surgery type of the patient to be tested. The levels include level one to level four, and level one or level two is set to low risk, level three is set to medium risk, and level four is set to high risk. The knowledge base calling unit retrieves the clinical informed consent form template with corresponding labels in the clinical informed consent form template library according to the surgery type and surgery risk level of the patient to be tested, and retrieves the term list with corresponding labels in the term list library according to the surgery type of the patient to be tested. The trained clinical informed consent form generation model identifies relevant terms, disease type, medical history information and treatment plan based on the input name, age, gender, surgery type, medical history information, medication information, diagnosis information and treatment information of the patient to be tested, outputs the content corresponding to each keyword in the clinical informed consent form template, searches for each term in the term list in the output content, and if found, replaces it with the term corresponding to the medical knowledge level column, thereby generating a personalized clinical informed consent form for the patient to be tested.

[0080] Specifically, when the historical treatment plan of the patient to be tested includes similar operations, the differences and similarities between the current operation and similar operations will be added to the corresponding clinical informed consent template for dynamic update; when the patient to be tested has a history of allergies, the drugs and materials used and their potential allergic reaction risks will be added to the corresponding clinical informed consent template for dynamic update; when the patient to be tested has a history of chronic diseases, the relevant chronic disease management recommendations and the risks and complications of the relevant chronic diseases during surgery will be added to the corresponding clinical informed consent template for dynamic update, and a personalized clinical informed consent for the patient to be tested will be generated based on the updated clinical informed consent template.

[0081] It should be noted that the similar surgeries refer to surgeries that are classified into one category according to the ICD10 surgical codes.

[0082] For example, it is assumed that the medical history information of the patient to be tested is hypertension and heart valve disease, the type of surgery is heart bypass surgery, and the patient's medical knowledge level is general. According to the heart bypass surgery, the surgical risk type is high risk. According to the heart bypass surgery and high risk, a clinical informed consent template with the heart bypass surgery and high risk labels is retrieved in the clinical informed consent template library. According to the heart bypass surgery, a term list with the heart bypass surgery label is retrieved in the term list library. The trained clinical informed consent generation model generates a clinical informed consent for heart bypass surgery based on the information of the patient to be tested. Since the patient has a history of hypertension and heart valve disease, the content corresponding to the keywords of preoperative and postoperative precautions will provide detailed information on the antihypertensive drugs that the patient needs to take according to the "drug use information", and the content corresponding to the surgical risk keyword will provide a detailed description of the risks of hypertension during surgery and related complications; and each term in the term list is searched in the output content. If found, it is replaced with the term corresponding to the term in the general level column to make it easier for the patient to be tested to understand.

[0083] It should be noted that clinical informed consent forms usually include surgical informed consent forms, special examination informed consent forms and special treatment informed consent forms. Among them, there are more types of surgical informed consent forms and the content contained is more complex. Therefore, the technical solution of this application is explained using the surgical informed consent form as an example. Other types of clinical informed consent forms have corresponding templates and labels, which are generated by calling the corresponding templates through the labels using the above method.

[0084] As can be understood, this invention improves work efficiency by constructing a clinical informed consent form template and term list library, automatically generating high-quality clinical informed consent forms based on the BERT model, and addressing the current inefficiency of relying on physicians to write clinical informed consent forms. This invention customizes a clinical informed consent form based on the patient's medical history, medical knowledge level, treatment plan, and risk level, enabling patients to more comprehensively and accurately understand the medical terminology, treatment information, and treatment risks, thereby making the right treatment choices.

[0085] Furthermore, the doctor-patient interaction platform includes a front-end module, a communication module, a management module, and a back-end module. The front-end module is used to establish a user interface through a component library, the communication module is used to realize communication between the front-end module and the back-end module by establishing a full-duplex communication channel, the management module is used to manage the user status, and the back-end module is used to realize user authentication and authority control, message processing, and data storage.

[0086] It should be noted that the user interface includes a chat main interface, a message input box, a user list, and a message list, and the user interface can adapt to screens of different sizes and different types of devices. The user status includes the user's connection status (including user login and logout) and online status. The message processing includes parsing the message content. The doctor-patient interaction platform provides a chat function so that patients and doctors can communicate at any time and place, so that patients and doctors can consult and provide feedback related to the clinical informed consent form, thereby promptly answering patients' concerns about the clinical informed consent form.

[0087] Exemplarily, the front-end module uses the Vue3 framework, developing the components required for the user interface through the Composition API. The communication module uses WebSocket, enabling communication between the front-end and back-end modules by establishing a full-duplex connection between them. The management module uses the Vuex state management library to manage user login, message sending and receiving, online, and logout states. The back-end module uses the Express framework to build a Node.js server, integrating Socket.IO to monitor user connections and message sending and receiving events, enabling message processing.

[0088] It can be understood that the present invention provides a chat function for patients and doctors through the doctor-patient interaction platform to conduct consultation and feedback related to clinical informed consent, thereby promoting communication between doctors and patients and improving patient satisfaction.

[0089] Compared with the existing technology, the beneficial effects of the clinical informed consent intelligent recommendation device based on the BERT model provided by the present invention are as follows:

[0090] 1. This invention constructs a clinical informed consent form template and a term list library, and automatically generates high-quality clinical informed consent forms based on the BERT model, thereby improving work efficiency and solving the current problem of low efficiency of relying on doctors to write clinical informed consent forms.

[0091] 2. The present invention customizes a personalized clinical informed consent form for the patient based on the patient's medical history information, medical knowledge level, treatment plan and risk level, so that the patient can understand the medical terms, treatment information and treatment risks more comprehensively and accurately, and thus make the right treatment choice.

[0092] 3. The present invention constructs a clinical informed consent form template library and a term list library by collecting a large number of clinical informed consent form samples, medical literature, and medical standards, and continuously updates the clinical informed consent form template library and the term list library by supplementing new medical literature and medical standards to obtain the latest medical knowledge and terminology, thereby improving the accuracy of generating clinical informed consent forms.

[0093] 4. The present invention provides a chat function for patients and doctors through a doctor-patient interaction platform to conduct consultation and feedback related to clinical informed consent, thereby promoting communication between doctors and patients and improving patient satisfaction.

[0094] Those skilled in the art will appreciate that all or part of the process steps of the above-described embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, such as a magnetic disk, an optical disk, a read-only memory, or a random access memory.

[0095] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by any technician familiar with this technical field within the technical scope disclosed by the present invention should be covered by the scope of protection of the present invention.

Claims

1. The clinical informed consent intelligent recommendation device based on the BERT model is characterized by: The device comprises: The data collection and processing module is used to collect and preprocess the patient's historical medical information, medical knowledge, and clinical informed consent form samples, and send the preprocessed data to the model building module; A model building module is used to build a clinical informed consent generation model based on the BERT model, and pre-train the clinical informed consent generation model based on the preprocessed data to obtain a trained clinical informed consent generation model; The knowledge base construction module is used to extract key information and terms from the preprocessed data using the trained clinical informed consent form generation model and label them to build a knowledge base. Based on the key information in the knowledge base, three templates are constructed for each surgical type according to the surgical risk level. The surgical type and surgical risk level corresponding to the template are used as its label. The content related to the clinical informed consent form keywords is obtained through the labels of the key information to construct a clinical informed consent form template library. A term list library is constructed based on the terms in the knowledge base. A clinical informed consent form generation module is used to input the pre-processed historical medical record information of the patient to be tested into the trained clinical informed consent form generation model, and call a clinical informed consent form template with corresponding tags in the clinical informed consent form template library according to the surgery type and surgery risk level of the patient to be tested, call a term list with corresponding tags in the term list library according to the surgery type of the patient to be tested, output the content corresponding to each keyword in the clinical informed consent form template, search for each term in the term list in the output content, and replace the term with the term corresponding to the medical knowledge level column if found, thereby generating a personalized clinical informed consent form for the patient to be tested; The doctor-patient interaction platform is used to display the clinical informed consent form and provide an interaction window for doctors and patients.

2. The clinical informed consent intelligent recommendation device based on the BERT model according to claim 1 is characterized in that: Preprocessing of the collected data includes: Analyze and clean the collected data; The cleaned data is segmented using the WordPiece algorithm; Encode the segmented data; Each word is encoded according to the segmented data. The encoding includes the token corresponding to the word, the sentence information encoding corresponding to the word, and the absolute position information encoding of the word in the sentence.

3. The clinical informed consent intelligent recommendation device based on the BERT model according to claim 1, characterized in that: The clinical informed consent form generation model includes an encoder layer and a decoder layer. The encoder layer includes a vocabulary embedding module, a sentence embedding module, a position embedding module and a Tm module. The vocabulary embedding module is used to obtain the token corresponding to each vocabulary, the sentence embedding module is used to obtain the sentence information encoding corresponding to the vocabulary, and the position embedding module is used to obtain the absolute position information encoding of the vocabulary in the sentence. The Tm module includes a multi-head self-attention mechanism and a feedforward neural network. The multi-head self-attention mechanism allows the model to consider all other input vocabulary when processing each vocabulary. The feedforward neural network performs a nonlinear transformation on the output of the multi-head self-attention mechanism and outputs a high-dimensional vector sequence. The decoder layer decodes the high-dimensional vector sequence output by the encoder layer and outputs a target vocabulary sequence.

4. The clinical informed consent intelligent recommendation device based on the BERT model according to claim 2, characterized in that: The expression of the input data of the Tm module is: to i =[E(x i ),AND Ai ,AND i ], Among them, a i Represents x i The encoded data after the vocabulary embedding module, sentence embedding module and position embedding module, x i represents the i-th word in the sentence, E(x i ) represents x i Corresponding Token, E Ai Represents x i The corresponding sentence information encoding, E i Represents x i The corresponding absolute position information encoding.

5. The clinical informed consent intelligent recommendation device based on the BERT model according to claim 1, characterized in that: Obtaining a surgical risk level according to the type of surgery, wherein the surgical risk level includes low risk, medium risk, and high risk; When the surgical risk level is low risk, the corresponding template does not contain medical history keywords and is supplemented with surgical risks and postoperative care content based on actual conditions. When the surgical risk level is medium risk and high risk, the corresponding template contains medical history keywords and is supplemented with surgical risks and postoperative care content based on actual conditions.

6. The clinical informed consent intelligent recommendation device based on the BERT model according to claim 5, characterized in that: The medical knowledge level of the patients was set as professional level, good level or general level according to the degree of their understanding of medical terms; alternative words with different levels of understanding difficulty were set for each term according to different medical knowledge levels, and the surgical type labels were marked; terms with the same surgical type label constituted a term list.

7. The BERT model-based intelligent recommendation device for clinical informed consent according to claim 1, characterized in that: The clinical informed consent form generation module includes a risk level identification unit, a knowledge base calling unit and a model running unit. The risk level identification unit is used to obtain the corresponding surgical risk level based on the surgical type of the patient to be tested; The knowledge base calling unit is used to obtain the corresponding clinical informed consent form template and term list according to the surgery type and surgery risk level of the patient to be tested; The model running unit is used to run the trained clinical informed consent form generation model, input the pre-processed data of the patient to be tested into the trained clinical informed consent form generation model and call the corresponding informed consent form template and term list to generate a personalized clinical informed consent form for the patient to be tested.

8. The BERT model-based intelligent recommendation device for clinical informed consent according to claim 7, characterized in that: When the historical treatment plan of the patient to be tested includes similar operations, the similarities and differences between this operation and similar operations will be added to the corresponding clinical informed consent template for dynamic update; when the patient to be tested has a history of allergies, the drugs and materials used and their potential allergic reaction risks will be added to the corresponding clinical informed consent template for dynamic update; when the patient to be tested has a history of chronic diseases, the relevant chronic disease management recommendations and the risks and complications of the relevant chronic diseases during surgery will be added to the corresponding clinical informed consent template for dynamic update, and a personalized clinical informed consent for the patient to be tested will be generated based on the updated clinical informed consent template.

9. The clinical informed consent intelligent recommendation device based on the BERT model according to claim 1, characterized in that: The clinical informed consent generation model is pre-trained based on the pre-processed data to obtain a trained clinical informed consent generation model including: Get the dataset from the preprocessed data and perform data augmentation; The data-augmented dataset is used as the input of the clinical informed consent form generation model, and pre-training is performed according to the pre-training task. The trained clinical informed consent form generation model is obtained by updating the parameters in its network structure.

10. The clinical informed consent intelligent recommendation device based on the BERT model according to claim 1, characterized in that: The doctor-patient interaction platform includes a front-end module, a communication module, a management module, and a back-end module. The front-end module is used to establish a user interface through a component library, the communication module is used to realize communication between the front-end module and the back-end module by establishing a full-duplex communication channel, the management module is used to manage the user status, and the back-end module is used to realize user authentication and authority control, message processing, and data storage.

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