Automatic conversion method and medium for disease codes of outpatient and emergency treatment information pages

Abstract

The present invention relates to a method and medium for automatically converting disease codes of outpatient and emergency treatment information pages, and relates to the technical field of medical information identification. The first medical information and the second medical information of the patient are obtained based on an electronic medical record system; the first medical information includes drug information, allergy history and chief complaint information, and the second medical information includes a disease name and an ICD code; in response to the obtained first medical information and the second medical information, identification is performed according to a preset first identification rule, and the identified disease name is used as the second disease name; the similarity between the first disease name and the second disease name is calculated as the first similarity, and if the first similarity reaches the first similarity threshold, the disease code in the information page is edited based on the national standard code corresponding to the second disease name. Compared with the prior art, the efficiency and accuracy of filling in the national standard code and the corresponding disease name in the information page are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of medical information identification, and in particular to a method and medium for automatically converting disease codes of outpatient and emergency diagnosis and treatment information pages. Background Art

[0002] The outpatient (emergency) diagnosis and treatment information page (hereinafter referred to as the "information page", a total of 72 fields) is an information summary reflecting the patient's current treatment process, which is formed by the hospital based on the outpatient (emergency) medical records and various information generated during the patient's outpatient (emergency) treatment in this hospital, including the patient's basic information, treatment process information, diagnosis and treatment information, and cost information.

[0003] The disease code needs to be filled in the information page. my country currently uses Diagnosis-related Groups (DRGs) to allocate and control medical insurance funds. In the DRGs scheme, disease codes use ICD-10 type classification codes (hereinafter referred to as ICD codes), while the new regulations currently issued clearly state that disease codes need to use the classification codes of the "National Clinical Edition of Disease Classification and Codes" type (hereinafter referred to as national standard codes).

[0004] In the electronic medical record system currently used by hospitals, the only classification code that can be edited for disease classification is the ICD code. Therefore, how to convert the ICD code into the national standard code when it is read from the electronic medical record system to the information page has become an urgent problem to be solved. Summary of the invention

[0005] The technical problem to be solved by the present application is to provide a method and medium for automatically converting disease codes of outpatient and emergency treatment information pages, which has the characteristic of automatically converting ICD codes into national standard codes when disease codes are read from the medical information system to the information page.

[0006] In a first aspect, an embodiment provides a method for automatically converting disease codes of an outpatient and emergency treatment information page, comprising:

[0007] Acquire the patient's first medical information and second medical information based on the electronic medical record system; the first medical information includes drug information, allergy history and chief complaint information, and the second medical information includes disease name and ICD code; use the disease name in the second medical information as the first disease name, and use the ICD code in the second medical information as the first ICD code;

[0008] In response to the acquired first medical information and second medical information, identification is performed according to a preset first identification rule, and the identified disease name is used as the second disease name, wherein the first identification rule is used to identify the national standard code and the corresponding disease name;

[0009] The similarity between the first disease name and the second disease name is calculated as the first similarity. If the first similarity reaches a first similarity threshold, the disease code in the information page is edited based on the national standard code corresponding to the second disease name.

[0010] In one embodiment, the identification is performed according to a preset first identification rule in response to the acquired first medical information and second medical information, including:

[0011] In response to the acquired first medical information and second medical information, the national standard code and the corresponding disease name are identified based on the national standard code recognition model; the national standard code recognition model is a neural network model trained in combination with a loss function.

[0012] In one embodiment, the training method of the national standard code recognition model includes:

[0013] Collect multiple medical text data including first medical information and second medical information, and mark them with national standard codes and corresponding disease names;

[0014] Convert multiple medical text data with national standard codes and corresponding disease names into a format suitable for the input of the Hua Tuo model to obtain labeled data;

[0015] Dividing the labeled data into a training set, a validation set, and a test set;

[0016] Based on the preset training parameters, the training set is used as the input of the Hua Tuo model for training. The performance of the Hua Tuo model is evaluated and the hyperparameters are adjusted based on the validation set during the training process. The generalization ability of the Hua Tuo model is evaluated based on the test set, thereby obtaining a trained and improved Hua Tuo model.

[0017] The trained improved Hua Tuo model is used as the national standard coding recognition model.

[0018] In one embodiment, if the first similarity reaches a first similarity threshold, editing the disease code in the information page based on the national standard code corresponding to the second disease name includes:

[0019] If the first similarity reaches a first similarity threshold, the national standard code corresponding to the second disease name is edited as the disease code in the information page.

[0020] In one embodiment, before obtaining the second disease name, the method further includes: in response to the obtained first medical information, identifying according to a preset second identification rule, using the identified disease name as the third disease name, and using the identified ICD code as the second ICD code, wherein the second identification rule is used to identify the ICD code and the corresponding disease name;

[0021] The similarity between the first disease name and the third disease name is calculated as the second similarity. If the second similarity reaches the second similarity threshold, the step of obtaining the second disease name is entered.

[0022] In one embodiment, the method further includes: in response to the acquired first medical information, identifying according to a preset second identification rule, the second identification rule is used to identify the ICD code and the corresponding disease name, using the identified disease name as the third disease name, and using the identified ICD code as the second ICD code;

[0023] If the first similarity reaches the first similarity threshold, the disease code in the information page is edited based on the national standard code corresponding to the second disease name, including: calculating the similarity between the first disease name and the third disease name as the second similarity; if the second similarity reaches the second similarity threshold and the first similarity reaches the first similarity threshold, the disease code in the information page is edited based on the national standard code corresponding to the second disease name.

[0024] In one embodiment, the step of identifying the acquired first medical information according to a preset second identification rule includes:

[0025] In response to the acquired first medical information, the ICD code and the corresponding disease name are identified based on an ICD code recognition model; the ICD code recognition model is a neural network model trained in combination with a loss function.

[0026] In one embodiment, the training method of the ICD code recognition model includes:

[0027] Collect multiple medical text data containing first medical information, and annotate ICD codes and corresponding disease names;

[0028] Convert multiple medical text data with ICD codes and corresponding disease names annotated into a format suitable for the Hua Tuo model input to obtain annotated data;

[0029] Dividing the labeled data into a training set, a validation set, and a test set;

[0030] Based on the preset training parameters, the training set is used as the input of the Hua Tuo model for training. The performance of the Hua Tuo model is evaluated and the hyperparameters are adjusted based on the validation set during the training process. The generalization ability of the Hua Tuo model is evaluated based on the test set, thereby obtaining a trained and improved Hua Tuo model.

[0031] The trained improved Hua Tuo model is used as the ICD coding recognition model.

[0032] In one embodiment, calculating the similarity between the first disease name and the third disease name, and / or calculating the similarity between the first disease name and the second disease name, includes:

[0033] Vectorize the two disease names whose similarity needs to be calculated respectively to obtain a first vector and a second vector;

[0034] Calculate the modulus of the first vector and the modulus of the second vector respectively;

[0035] Calculate the product of the modulus of the first vector and the modulus of the second vector as a first product;

[0036] calculating a product of the first vector and the second vector as a second product;

[0037] The ratio of the second product to the first product is calculated. The closer the ratio is to 1, the higher the similarity.

[0038] In a second aspect, an embodiment provides a computer-readable storage medium, wherein the medium stores a program, and the program can be loaded by a processor to execute the method for automatic conversion of disease codes described in any one of the above embodiments.

[0039] The beneficial effects of the present invention are:

[0040] Based on the present application scheme, the ICD code is automatically converted into the national standard code to realize automatic conversion of the disease code, and the information page is edited based on the national standard code obtained by the automatic conversion, thereby improving the efficiency of filling in the national standard code and the disease name on the information page, and, by calculating the similarity between the first disease name and the second disease name, the accuracy of filling in the national standard code and its corresponding disease name in the information page is improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a flowchart of a method for automatically converting disease codes on an outpatient and emergency treatment information page according to an embodiment of the present application;

[0042] Figure 2 It is a flowchart of a training method for a national standard code recognition model according to an embodiment of the present application;

[0043] Figure 3 This is a flow chart of a method for calculating the similarity between two disease names according to an embodiment of the present application;

[0044] Figure 4 It is a flowchart of a training method for an ICD code recognition model according to an embodiment of the present application. DETAILED DESCRIPTION

[0045] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments adopt associated similar element numbers. In the following embodiments, many detailed descriptions are for making the present application better understood. However, those skilled in the art can easily recognize that some features can be omitted in different situations, or can be replaced by other elements, materials, methods. In some cases, some operations related to the present application are not shown or described in the specification, this is to avoid the core part of the present application being overwhelmed by too much description, and for those skilled in the art, it is not necessary to describe these related operations in detail, and they can fully understand the related operations according to the description in the specification and the general technical knowledge in the art.

[0046] In addition, the features, operations or characteristics described in the specification can be combined in any appropriate manner to form various implementations. At the same time, the steps or actions in the method description can also be interchanged or adjusted in a manner that is obvious to those skilled in the art. Therefore, the various sequences in the specification and the drawings are only for the purpose of clearly describing a certain embodiment and are not meant to be a required sequence, unless otherwise specified that a certain sequence must be followed.

[0047] The serial numbers assigned to the components in this article, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning.

[0048] To facilitate the description of the inventive concept of the present application, the disease coding conversion technology is briefly described below.

[0049] At present, the electronic medical record system edited by doctors can only accept ICD codes and their corresponding disease names. If you want to fill in the national standard code and its corresponding disease classification in the information page, the most direct way is for doctors to fill it in manually according to their professional knowledge of disease classification. However, this filling method is inefficient and is limited by doctors' professional knowledge of disease classification. It is easy to make mistakes and requires a lot of additional manual review, which causes a lot of inconvenience and waste of resources.

[0050] In view of this, an embodiment of the present application provides a method and medium for automatically converting disease codes of outpatient and emergency treatment information pages. In the scheme, the first medical information and the second medical information of the patient are obtained based on the electronic medical record system; wherein the first medical information includes drug information, allergy history and chief complaint information, and the second medical information includes the first disease name and the first ICD code; the disease name in the second medical information is used as the first disease name, and the ICD code in the second medical information is used as the first ICD code; in response to the acquired first medical information and second medical information, the second disease name is obtained by identifying according to the preset first identification rule; the similarity between the first disease name and the second disease name is calculated as the first similarity, and if the first similarity reaches the first similarity threshold, the disease code in the information page is edited based on the national standard code corresponding to the second disease name. In this way, through automatic identification, the efficiency and accuracy of filling in the national standard code and its corresponding disease name in the information page are improved.

[0051] Please refer to Figure 1 In one embodiment of the present application, a method for automatically converting disease codes of an outpatient and emergency treatment information page is provided, comprising:

[0052] Step S10, obtaining the first medical information and the second medical information of the patient based on the electronic medical record system. The first medical information includes drug information, allergy history and chief complaint information, and the second medical information includes the disease name and ICD code. The disease name in the second medical information is used as the first disease name, and the ICD code in the second medical information is used as the first ICD code.

[0053] The information in the electronic medical record system is edited by the doctor. The first medical information is not limited to the patient's medication information, the patient's allergy history and the patient's chief complaint information, but can also include other information recorded by the doctor in the electronic medical record that helps to determine the name of the disease. Since the current electronic medical record system only accepts ICD codes and their corresponding disease names, it is impossible to directly extract the national standard codes and their corresponding disease names from the electronic medical record.

[0054] Step S20, in response to the acquired first medical information and second medical information, identification is performed according to a preset first identification rule, and the identified disease name is used as the second disease name. The first identification rule is used to identify the national standard code and the corresponding disease name.

[0055] The applicant found in the research that ICD code is an international standard code and national standard code is a standard code in my country. Although there is a certain correspondence between ICD code and national standard code, the correspondence is not an absolute one-to-one relationship. It needs to be further classified in combination with the specific condition. In addition, there are some detailed classified diseases that do not have ICD codes, and the corresponding disease names are also different. Therefore, it is not realistic to directly convert ICD codes into national standard codes.

[0056] In view of this, the first medical information and the second medical information are integrated to identify the national standard code and the disease name corresponding to the national standard code.

[0057] In one embodiment, in response to the acquired first medical information and second medical information, the national standard code and the corresponding disease name are identified based on the national standard code recognition model, wherein the national standard code recognition model is a neural network model trained in combination with a loss function.

[0058] The Hua Tuo Big Model, also known as Hua Tuo GPT, is an open source medical big model of the existing ChatGPT. It has a good ability to understand the natural language of medical information. Patients can interact with Hua Tuo GPT in a conversational manner to achieve department triage, auxiliary diagnosis and other functions, providing assistance to doctors.

[0059] In one embodiment, based on the Hua Tuo model suitable for the medical field, the Hua Tuo model is trained and fine-tuned to obtain a national standard code recognition model suitable for identifying national standard codes and disease names corresponding to national standard codes. Figure 2 , the training method of the national standard coding recognition model includes:

[0060] Step S201, collect multiple medical text data including first medical information and second medical information, and mark them with national standard codes and corresponding disease names.

[0061] The medical text data can be extracted from historical electronic medical records, and based on the extracted medical text data and the "National Clinical Edition of Disease Classification and Code" standard, the national standard code and the corresponding disease name are marked.

[0062] Labeling requires professional medical knowledge and a rigorous attitude to ensure the accuracy and consistency of labeling. It is necessary to label the national standard code that complies with the "National Clinical Edition of Disease Classification and Code" and to label the corresponding disease name.

[0063] It can be understood by those skilled in the art that the provided medical text data needs to cover all types of national standard codes and disease names, and the medical text data corresponding to each type of national standard code and disease name should be as much as possible to ensure the accuracy of the trained model.

[0064] Step S202, converting a plurality of medical text data annotated with national standard codes and corresponding disease names into a format suitable for input into the Hua Tuo large model to obtain annotated data.

[0065] Those skilled in the art can understand that text data needs to be converted into a vector representation that can be understood by the model through word segmentation, word vector conversion, etc.

[0066] Step S203, dividing the labeled data into a training set, a validation set and a test set.

[0067] The training set is used to train the model, the validation set is used to evaluate the performance of the model and adjust the hyperparameters during the training process, and the test set is used to finally evaluate the generalization ability of the model.

[0068] Step S204: Based on the preset training parameters, the training set is used as the input of the Hua Tuo model for training. The performance of the Hua Tuo model is evaluated and the hyperparameters are adjusted based on the validation set during the training process. The generalization ability of the Hua Tuo model is evaluated based on the test set, thereby obtaining a trained improved Hua Tuo model.

[0069] You can set appropriate training parameters such as learning rate, batch size, and number of training rounds according to the model requirements and computing resources. The learning rate determines the step size of the model parameter update, the batch size affects the efficiency and stability of the model training, and the number of training rounds determines the number of iterations of the model training.

[0070] During the training process, the loss function of the model is calculated based on the output of the model and the annotated true results, and the parameters of the model are updated using an optimization algorithm to reduce the value of the loss function. In one embodiment, during the training process, the weight file of the model can be saved regularly so that the training can be resumed when the training is interrupted or a problem occurs.

[0071] In one embodiment, the trained model is evaluated using a validation set, and the accuracy, recall and other indicators of the model on the validation set are calculated to evaluate the performance of the model for entity extraction. Based on the evaluation results, the problems and deficiencies of the model are analyzed.

[0072] In one embodiment, the model can be optimized based on the results of the model evaluation. The training parameters can be adjusted, the amount of training data can be increased, the quality of data annotation can be improved, etc., to improve the performance of the model. If the model has overfitting or underfitting problems, regularization techniques, increasing data diversity, etc. can be used to solve them.

[0073] Step S205, using the trained improved Hua Tuo large model as the national standard coding recognition model.

[0074] In the embodiment of the present application, based on the Hua Tuo model suitable for the medical field, the Hua Tuo model is trained and fine-tuned to obtain a national standard code recognition model suitable for extracting national standard codes and corresponding disease names.

[0075] The national standard code recognition model obtained based on the above embodiment can identify the corresponding national standard code and the corresponding disease name based on electronic medical record data.

[0076] Step S30, calculating the similarity between the first disease name and the second disease name as the first similarity, if the first similarity reaches the first similarity threshold, then editing the disease code in the information page based on the national standard code corresponding to the second disease name.

[0077] During the research, the applicant found that when doctors edit ICD codes in the electronic medical record system, the probability of errors is relatively high. If the ICD code given by the doctor is wrong, the national standard code and the corresponding disease name derived based on the first medical information and the second medical information will also be wrong.

[0078] In view of this, the similarity between the first disease name and the second disease name is calculated as the first similarity, and based on whether the first similarity reaches the similarity threshold, it is verified whether the disease code in the information page can be edited based on the national standard code corresponding to the second disease name.

[0079] In one embodiment, if the first similarity reaches the first similarity threshold, it is considered that there is no problem with the ICD code, and the given national standard code and the corresponding disease name are also correct. The national standard code corresponding to the second disease name is edited as the disease code in the information page. If the first similarity does not reach the first similarity threshold, it is considered that the ICD code is likely to be wrong, and the result of failing the first similarity test is returned so that the ICD code can be manually checked.

[0080] Through the above-mentioned embodiment scheme, the ICD code is automatically converted into the national standard code to realize the automatic conversion of the disease code, and the information page is edited based on the national standard code obtained by the automatic conversion, thereby improving the efficiency of filling in the national standard code and the disease name on the information page, and, by calculating the similarity between the first disease name and the second disease name, the accuracy of filling in the national standard code and its corresponding disease name in the information page is improved.

[0081] ICD coding errors are a problem that cannot be avoided in various hospitals at present, and a large amount of manpower is needed to manually check whether there are coding errors.

[0082] In view of this, in order to further avoid ICD coding errors, in response to the first medical information obtained, the preset second recognition rule is used for recognition, and the recognized disease name is used as the third disease name, and the recognized ICD code is used as the second ICD code. Among them, the second recognition rule is used to identify the ICD code and the corresponding disease name. Then, the similarity between the first disease name and the third disease name is calculated as the second similarity, and it is determined whether the second similarity reaches the second similarity threshold.

[0083] In one embodiment, before obtaining the second disease name, the second similarity is calculated first, and it is determined whether the second similarity reaches the second similarity threshold. If the second similarity reaches the second similarity threshold, the step of obtaining the second disease name is entered. In this way, the correctness of the ICD code in the electronic medical record is first verified. If the first round of verification is correct, the step of determining whether the first similarity reaches the first similarity threshold is entered, thereby achieving the second round of verification. If both rounds of verification are correct, it means that the ICD code in the electronic medical record is correct. Finally, the disease code in the information page is edited based on the national standard code corresponding to the second disease name.

[0084] If the first round of verification finds that the second similarity does not reach the second similarity threshold, it is considered that the ICD code is likely to be wrong, and a result indicating that the second similarity check failed is returned so that the ICD code can be manually checked.

[0085] In another embodiment, the first round of verification and the second round of verification are not in order, that is, if the second similarity reaches the second similarity threshold, and the first similarity reaches the first similarity threshold, the disease code in the information page is edited based on the national standard code corresponding to the second disease name. If one of the second similarity threshold and the first similarity threshold does not reach the corresponding preset threshold, it is considered that the ICD code is likely to be wrong, and the corresponding similarity test failure result is returned to facilitate manual inspection of the ICD code.

[0086] In one embodiment, please refer to Figure 3 , calculating the similarity between the first disease name and the third disease name, and / or, calculating the similarity between the first disease name and the second disease name, may include:

[0087] Step S100, vectorizing two disease names whose similarity needs to be calculated respectively to obtain a first vector and a second vector.

[0088] Taking the calculation of the similarity between the first disease name and the second disease name as an example, the first disease name is vectorized to obtain a first vector A, and the second disease name is vectorized to obtain a second vector B.

[0089] In one embodiment, a pre-trained Word2Vec model can be used to input the disease name into the model to obtain the corresponding word vector. For example, the word vector corresponding to "cold" may be [0.1, 0.2, -0.3,..., 0.4], and the word vector corresponding to "flu" may be [0.2, 0.3, -0.2,..., 0.5], etc. These word vectors can reflect the semantic similarity between disease names. For example, the word vectors of "cold" and "flu" are close in the vector space because they are semantically similar and both belong to respiratory infections.

[0090] Step S200, respectively calculating the modulus of the first vector and the modulus of the second vector.

[0091] In one embodiment, step S200 can be expressed as:

[0092] ,

[0093] ,

[0094] in, represents the magnitude of the first vector, represents the magnitude of the second vector, represents the value of the i-th element in the first vector, represents the value of the i-th element in the second vector, n represents the total number of elements in the first vector or the second vector, 1≤i≤n.

[0095] Those skilled in the art can understand that in order to compare the similarity between the first vector and the second vector, the first vector and the second vector have the same form and the same number of elements.

[0096] Step S300: Calculate the product of the modulus of the first vector and the modulus of the second vector as a first product.

[0097] Step S400: Calculate the product of the first vector and the second vector as a second product.

[0098] Step S500, calculating the ratio of the second product to the first product, the closer the ratio is to 1, the higher the similarity is.

[0099] In one embodiment, step S500 can be expressed as:

[0100] ,

[0101] in, Represents the similarity value.

[0102] For the obtained similarity value, when the value is 1, it is considered that the two vectors are exactly the same, when the value is -1, it is considered that the two vectors are completely opposite, and when the value is 0, it means that the two vectors are orthogonal. The closer to 1, the more similar the two disease names are.

[0103] Those skilled in the art can understand that the second similarity threshold and the first similarity threshold can be the same or different. Those skilled in the art can understand that the second similarity threshold and the first similarity threshold can be set based on actual needs. In one embodiment, the first similarity threshold and the second similarity threshold can both be set to 0.95.

[0104] Based on the above embodiment, by calculating the similarity between the first disease name and the third disease name, the accuracy of filling in the national standard code and its corresponding disease name in the information page is further improved.

[0105] In one embodiment, in response to the acquired first medical information, the ICD code and the corresponding disease name are identified based on an ICD code recognition model, wherein the ICD code recognition model is a neural network model trained in combination with a loss function.

[0106] In one embodiment, based on the Hua Tuo model suitable for the medical field, the Hua Tuo model is trained and fine-tuned to obtain a national standard code recognition model suitable for identifying ICD codes and disease names corresponding to ICD codes. Figure 4 , the training method of the ICD code recognition model includes:

[0107] Step S211, collect multiple medical text data containing first medical information, and mark ICD codes and corresponding disease names.

[0108] The medical text data can be extracted from historical electronic medical records, and based on the extracted medical text data and the ICD-10 international standard, ICD codes and disease names corresponding to the ICD codes are labeled.

[0109] Labeling requires professional medical knowledge and a rigorous attitude to ensure the accuracy and consistency of labeling. It is necessary to label ICD codes that comply with the ICD-10 international standard and to label the corresponding disease names.

[0110] Those skilled in the art will appreciate that the provided medical text data needs to cover all types of ICD codes and disease names, and the medical text data corresponding to each type of ICD code and disease name should be as much as possible to ensure the accuracy of the trained model.

[0111] Step S212, converting the multiple medical text data with the ICD codes and corresponding disease names annotated into a format suitable for the Hua Tuo model input to obtain annotated data.

[0112] Those skilled in the art can understand that text data needs to be converted into a vector representation that can be understood by the model through word segmentation, word vector conversion, etc.

[0113] Step S213, dividing the labeled data into a training set, a validation set and a test set.

[0114] The training set is used to train the model, the validation set is used to evaluate the performance of the model and adjust the hyperparameters during the training process, and the test set is used to finally evaluate the generalization ability of the model.

[0115] Step S214, based on the preset training parameters, the training set is used as the input of the Hua Tuo model for training, the performance of the Hua Tuo model is evaluated and the hyperparameters are adjusted during the training process based on the validation set, and the generalization ability of the Hua Tuo model is evaluated based on the test set, so as to obtain a trained improved Hua Tuo model.

[0116] Step S215, using the trained improved Hua Tuo large model as the ICD coding recognition model.

[0117] In an embodiment of the present application, based on the Hua Tuo model suitable for the medical field, the Hua Tuo model is trained and fine-tuned to obtain an ICD code recognition model suitable for extracting ICD codes and corresponding disease names.

[0118] The ICD code recognition model obtained based on the above embodiment can identify the corresponding ICD code and the corresponding disease name based on the electronic medical record data.

[0119] In one embodiment of the present application, a computer-readable storage medium is provided, on which a program is stored. The stored program includes a method that can be loaded by a processor and process any of the above embodiments.

[0120] Those skilled in the art will appreciate that all or part of the functions of the various methods in the above-mentioned embodiments can be implemented by hardware or by computer programs. When all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can be stored in a computer-readable storage medium, and the storage medium can include: read-only memory, random access memory, disk, optical disk, hard disk, etc., and the program is executed by a computer to implement the above-mentioned functions. For example, the program is stored in the memory of the device, and when the program in the memory is executed by the processor, all or part of the above-mentioned functions can be implemented. In addition, when all or part of the functions in the above-mentioned embodiments are implemented by computer programs, the program can also be stored in a storage medium such as a server, another computer, disk, optical disk, flash disk or mobile hard disk, and can be downloaded or copied and saved in the memory of the local device, or the system of the local device is updated, and when the program in the memory is executed by the processor, all or part of the functions in the above-mentioned embodiments can be implemented.

[0121] The above specific examples are used to illustrate the present invention, which is only used to help understand the present invention and is not intended to limit the present invention. For those skilled in the art, according to the concept of the present invention, some simple deductions, modifications or substitutions can be made.

Claims

1. A method for automatically converting disease codes of outpatient and emergency treatment information pages, characterized in that: include: Acquiring first medical information and second medical information of the patient based on the electronic medical record system; The first medical information includes drug information, allergy history and chief complaint information, and the second medical information includes disease name and ICD code; the disease name in the second medical information is used as the first disease name, and the ICD code in the second medical information is used as the first ICD code; In response to the acquired first medical information and second medical information, identification is performed according to a preset first identification rule, and the identified disease name is used as the second disease name, wherein the first identification rule is used to identify the national standard code and the corresponding disease name; Calculate the similarity between the first disease name and the second disease name as the first similarity, and if the first similarity reaches the first similarity threshold, edit the disease code in the information page based on the national standard code corresponding to the second disease name; The method also includes: in response to the acquired first medical information, identifying according to a preset second identification rule, the second identification rule is used to identify the ICD code and the corresponding disease name, using the identified disease name as the third disease name, and using the identified ICD code as the second ICD code; if the first similarity reaches the first similarity threshold, then based on the national standard code corresponding to the second disease name, the disease code in the information page is edited, including: calculating the similarity between the first disease name and the third disease name as the second similarity, if the second similarity reaches the second similarity threshold, and the first similarity reaches the first similarity threshold, then based on the national standard code corresponding to the second disease name, the disease code in the information page is edited.

2. The method for automatically converting disease codes according to claim 1, characterized in that: The step of identifying the acquired first medical information and second medical information according to a preset first identification rule includes: In response to the acquired first medical information and second medical information, the national standard code and the corresponding disease name are identified based on the national standard code recognition model; the national standard code recognition model is a neural network model trained in combination with a loss function.

3. The method for automatic conversion of disease codes according to claim 1, characterized in that: The training method of the national standard coding recognition model includes: Collect multiple medical text data including first medical information and second medical information, and mark them with national standard codes and corresponding disease names; Convert multiple medical text data with national standard codes and corresponding disease names into a format suitable for the input of the Hua Tuo model to obtain labeled data; Dividing the labeled data into a training set, a validation set, and a test set; Based on the preset training parameters, the training set is used as the input of the Hua Tuo model for training. The performance of the Hua Tuo model is evaluated and the hyperparameters are adjusted based on the validation set during the training process. The generalization ability of the Hua Tuo model is evaluated based on the test set, thereby obtaining a trained and improved Hua Tuo model. The trained improved Hua Tuo model is used as the national standard coding recognition model.

4. The method for automatically converting disease codes according to claim 1, characterized in that: If the first similarity reaches the first similarity threshold, the disease code in the information page is edited based on the national standard code corresponding to the second disease name, including: If the first similarity reaches a first similarity threshold, the national standard code corresponding to the second disease name is edited as the disease code in the information page.

5. The method for automatic conversion of disease codes according to claim 1, characterized in that: The step of identifying the acquired first medical information according to a preset second identification rule includes: In response to the acquired first medical information, the ICD code and the corresponding disease name are identified based on an ICD code recognition model; the ICD code recognition model is a neural network model trained in combination with a loss function.

6. The method for automatic conversion of disease codes according to claim 5, characterized in that: The training method of the ICD code recognition model includes: Collect multiple medical text data containing first medical information, and annotate ICD codes and corresponding disease names; Convert multiple medical text data with ICD codes and corresponding disease names annotated into a format suitable for the Hua Tuo model input to obtain annotated data; Dividing the labeled data into a training set, a validation set, and a test set; Based on the preset training parameters, the training set is used as the input of the Hua Tuo model for training. The performance of the Hua Tuo model is evaluated and the hyperparameters are adjusted based on the validation set during the training process. The generalization ability of the Hua Tuo model is evaluated based on the test set, thereby obtaining a trained and improved Hua Tuo model. The trained improved Hua Tuo model is used as the ICD coding recognition model.

7. The method for automatically converting disease codes according to claim 1, characterized in that: Calculating the similarity between the first disease name and the third disease name, and / or calculating the similarity between the first disease name and the second disease name, including: Vectorize the two disease names whose similarity needs to be calculated respectively to obtain a first vector and a second vector; Calculate the modulus of the first vector and the modulus of the second vector respectively; Calculate the product of the modulus of the first vector and the modulus of the second vector as a first product; calculating a product of the first vector and the second vector as a second product; The ratio of the second product to the first product is calculated. The closer the ratio is to 1, the higher the similarity.

8. A computer-readable storage medium, characterized in that: The medium stores a program, which can be loaded by a processor and execute the disease code automatic conversion method as described in any one of claims 1 to 7.

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