Patient information recording method and system

Abstract

The present invention provides a patient information recording method and system, which relates to the technical field of data processing. The method comprises: obtaining patient medical record information; constructing an entity recognition model based on a named entity recognition algorithm and a conditional random field algorithm; performing semantic extraction on the patient medical record information through the entity recognition model to obtain patient information to be recorded; classifying the patient information to be recorded to generate a category of patient information to be recorded; generating an input window for patient identity information based on an XSL file, wherein the XSL file includes an XSLT tool and an XPath tool, and the input window includes a plurality of operation buttons for the information to be recorded; receiving patient identity information; displaying a plurality of sub-input windows associated with the patient identity information; receiving user input through the sub-input window; and displaying user input according to the category of patient information to be recorded. The efficiency and accuracy of patient information recording are improved without increasing the memory burden of medical staff.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a patient information recording method and system. Background Art

[0002] Patient information recording refers to the process of medical staff recording patient-related information during work. This information includes the patient's personal information, medical history, condition changes, treatment plan, medication status, etc. The purpose of recording this information is to provide patients with comprehensive medical services, continuously monitor the patient's health status, evaluate the treatment effect, coordinate the work of the medical team, and ensure patient safety.

[0003] The importance of patient information records lies in providing a basis for medical decision-making and helping medical staff to better manage and treat patients. With the development of medical technology, information tools such as electronic medical record systems have gradually replaced traditional paper records, improving the efficiency and accuracy of information records. At the same time, it is also easier for medical staff to access and share patient information, improving the quality and efficiency of medical services.

[0004] At present, medical staff generally record relevant patient information through paper notebooks or general electronic devices, which are not very targeted. When recording patient information, medical staff need to hold electronic devices and need to remember a large amount of patient information that needs to be recorded. It is inconvenient for medical staff to use and has a heavy memory burden. In addition, the recorded patient data often has a large difference in order and is difficult to access. Summary of the invention

[0005] In order to solve the technical problems existing in the prior art that medical staff generally record relevant patient information through paper notebooks or general electronic devices, which are not very targeted, and when recording patient information, medical staff need to hold electronic devices and need to remember a large amount of patient information that needs to be recorded, which is inconvenient for medical staff to use and has a heavy memory burden, and the recorded patient data often has a large difference in sequence and is difficult to access, the present invention provides a patient information recording method and system.

[0006] The technical solution provided by the embodiment of the present invention is as follows:

[0007] First aspect

[0008] A patient information recording method provided by an embodiment of the present invention is applied to a portable electronic recording device, wherein the portable electronic recording device is detachably arranged on a sleeve of a work uniform through a single-sided transparent cloth bag, and the portable electronic recording device is connected to a HIS system, and the method comprises:

[0009] S1: Obtain patient medical record information;

[0010] S2: constructing an entity recognition model based on a named entity recognition algorithm and a conditional random field algorithm, wherein the entity recognition model includes an input module, a first module based on the named entity recognition algorithm, a second module based on the conditional random field algorithm, and an output module connected in sequence;

[0011] S3: semantically extracting the patient's medical record information through an entity recognition model to obtain the patient information to be recorded, wherein the patient information to be recorded includes the patient's identity information;

[0012] S4: classifying the patient information to be recorded and generating a category of the patient information to be recorded;

[0013] S5: generating an input window for patient identity information based on the XSL file, wherein the input window includes various sub-input windows bound to the patient information to be recorded, wherein the XSL file includes an XSLT tool and an XPath tool, and the input window includes a plurality of operation buttons for the information to be recorded;

[0014] S6: receiving patient identification information;

[0015] S7: displaying a plurality of sub-input windows associated with patient identification information;

[0016] S8: receiving user input through the sub-input window;

[0017] S9: Display user input according to the category of patient information to be recorded.

[0018] Second aspect

[0019] An embodiment of the present invention provides a patient information recording system, comprising:

[0020] processor;

[0021] A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the patient information recording method as described in the first aspect is implemented.

[0022] The third aspect

[0023] An embodiment of the present invention provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the patient information recording method as described in the first aspect is implemented.

[0024] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0025] In the present invention, an entity recognition model is constructed based on a named entity recognition algorithm and a conditional random field algorithm. The patient information to be recorded is first identified by the named entity recognition algorithm, and then the conditional random field algorithm is used to check the difference from the correct patient information to be recorded. After multiple screenings, the final patient information to be recorded is determined, and the deviation between the patient information and the patient's medical record information is reduced, thereby ensuring the accuracy of the information to be recorded obtained based on the patient's medical record information. Moreover, this method can also be applied to electronic medical record information of different formats in different hospitals, and can automatically extract the patient information to be recorded, thereby reducing the memory burden of medical staff when collecting patient information. Then, the obtained patient information to be recorded is secondary classified, so that similar information to be recorded is classified as the same In the larger category, it is convenient for medical staff to collect and check patient information, avoiding the confusion of information management. Finally, the XSL file is used to generate various sub-input windows bound to the patient information to be recorded. The input window generated by the XSL file can be easily customized according to the needs, making the interface simple and beautiful. Such an input interface can make medical staff more focused on recording patient information, reducing unnecessary visual interference, binding the information to be recorded with the sub-input window, and providing operation buttons at the same time, so that medical staff can record patient information intuitively and conveniently, improving work efficiency and recording quality, avoiding medical problems caused by deviations in manual memory of medical staff, and improving information management efficiency and accuracy. In addition, the portable electronic recording device is detachably arranged on the sleeve of the work clothes through a single-sided transparent cloth bag, without the need to hold a notebook or recorder. The portable electronic recording device is connected to the HIS system, which can greatly release the work efficiency of medical staff and ensure the integrity of the recorded patient information. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0027] Figure 1 A schematic diagram of a process of recording patient information provided by an embodiment of the present invention;

[0028] Figure 2 A schematic diagram of the structure of a portable electronic recording device provided by an embodiment of the present invention;

[0029] Figure 3 A schematic diagram of the structure of an entity recognition model provided by an embodiment of the present invention;

[0030] Figure 4A schematic diagram of the structure of a patient information recording system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solution of the present invention is described below in conjunction with the accompanying drawings.

[0032] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "example" in the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of the word "example" is intended to present the concept in a specific way. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or it can be either of the two.

[0033] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same. "of", "corresponding, relevant" and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference between them is not emphasized, the meanings they intend to express are the same.

[0034] In the embodiments of the present invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript such as W1. When the difference is not emphasized, the meanings to be expressed are consistent.

[0035] In order to make the technical problems, technical solutions and advantages to be solved by the present invention more clear, a detailed description will be given below with reference to the accompanying drawings and specific embodiments.

[0036] Reference Manual Attached Figure 1 , which shows a flow chart of a patient information recording method provided by an embodiment of the present invention. Figure 2 , which shows a schematic diagram of the structure of a portable electronic recording device provided by an embodiment of the present invention. Figure 3 , showing a structural diagram of an entity recognition model provided by an embodiment of the present invention.

[0037] The embodiment of the present invention provides a patient information recording method, which can be implemented by a patient information recording device, which can be a terminal or a server. The portable electronic recording device is applied to the portable electronic recording device, which is detachably arranged on the sleeve of the work clothes through a single-sided transparent cloth bag, and the portable electronic recording device is connected to the HIS system.

[0038] It should be noted that the cloth bag can be in the form of one side being transparent and the other side being cloth, and can be specifically arranged on the sleeve of the work clothes in a detachable manner such as nylon hook and loop fasteners or zippers. This portable electronic recording device is detachably arranged on the sleeve of the work clothes of the medical staff through the single-sided transparent cloth bag, so that the medical staff can carry it with them and record the patient information conveniently and quickly. At the same time, the portable electronic recording device is connected to the HIS system (Hospital Information System) to achieve real-time synchronization and data sharing of patient information. In this way, medical staff can record patient information anytime and anywhere without relying on paper records or fixed electronic devices, which greatly improves the convenience and efficiency of information recording. Connection with the HIS system can enable the device to obtain the patient's medical record information and synchronize the collected information to the shared terminal in a timely manner, so that the medical staff related to the patient can timely and uniformly check the patient's medical record information and complete unified information management.

[0039] The processing flow of the patient information recording method may include the following steps:

[0040] S1: Obtain patient medical record information.

[0041] Specifically, the patient's electronic medical record information or diagnosis and treatment information can be viewed through the HIS system.

[0042] S2: Build an entity recognition model based on named entity recognition algorithm and conditional random field algorithm.

[0043] The entity recognition model includes an input module, a first module based on a named entity recognition algorithm, a second module based on a conditional random field algorithm, and an output module which are connected in sequence.

[0044] Among them, the named entity recognition algorithm (NER algorithm) is a technology used to identify named entities from text. Named entities usually refer to entities with specific meanings in the text, such as names of people, places, and names of organizations. By automatically identifying and classifying named entities in text, it can help understand the semantic structure of the text, thereby realizing the automated processing and analysis of text information. The conditional random field algorithm (CRF algorithm) is a statistical modeling method that is commonly used in sequence labeling tasks, such as named entity recognition and part-of-speech tagging. Based on the conditional probability model, the interdependence between input sequences is taken into account, and the joint probability distribution between the observed sequence and the labeled sequence is learned to achieve accurate modeling of sequence labeling.

[0045] The advantage of combining the NER algorithm and the CRF algorithm to construct an entity recognition model is that it comprehensively considers the contextual information between named entities and improves the accuracy of entity recognition. The CRF algorithm can use the dependencies between entities in the sequence to perform annotations in a global optimization manner, thereby avoiding the cumulative effect of local annotation errors. Secondly, it can handle multiple types of named entities. The NER algorithm can usually recognize some common named entities, but may not perform well for named entities in specific fields or specific types. The CRF algorithm can better adapt to different types of named entity recognition tasks by learning the features and patterns in the training data. It is also highly scalable. The CRF-based entity recognition model can improve performance by introducing more features and contextual information, and can also adapt to data sets of different sizes and complexities. It can make full use of the contextual information in the text to achieve accurate recognition and annotation of named entities, thereby providing a reliable foundation for subsequent information processing and analysis.

[0046] S3: Use the entity recognition model to perform semantic extraction of patient medical record information and obtain the patient information to be recorded.

[0047] The patient information to be recorded includes the patient's identity information.

[0048] Among them, the patient information to be recorded refers to the key information to be recorded after semantic extraction from the patient's medical record information, including the patient's identity information and other important information that may be related to the medical record. Patient identity information may include basic information such as the patient's name, age, gender, and ID number, which is used to identify the patient. In addition, other patient information to be recorded may also involve the patient's medical history, diagnosis results, treatment plan, medication, examination report, and other content related to the patient's medical record. This information is an important basis for medical staff to record and manage patient information, and can help medical staff better understand the patient's condition and treatment, so as to provide more effective medical services and management. This real-time generation can extract medical record information in different medical record formats, not just limited to a single case information format, and improve the scope of application of this patient information recording method.

[0049] In a possible implementation, S3 specifically includes:

[0050] S301: Acquire a historical patient medical record information data set, wherein the historical patient medical record information data set includes a plurality of historical patient medical record information with label information, wherein the label information includes patient identity information, items to be recorded, and time of items to be recorded in each historical patient medical record information.

[0051] S302: Use the historical medical record information dataset to train the entity recognition model.

[0052] S303: Receive patient medical record information through the input module in the trained entity recognition model.

[0053] S304: extracting specific named entities, i.e., label information, from the patient's medical record information through the first module in the trained entity recognition model, wherein the specific named entities include patient identity information, items to be recorded, and time of items to be recorded.

[0054] In a possible implementation manner, S304 specifically includes:

[0055] The confidence parameter is introduced to extract specific named entities, i.e. label information, from the patient medical record information through the first module in the trained entity recognition model, specifically:

[0056] Y * = argmax Y P(Y|X;θ) α

[0057]

[0058]

[0059] Among them, Y * represents the alternative label information, P(Y|X;θ) represents the conditional probability of label information Y given the patient medical record information X, argmax Y Indicates that the label information Y that maximizes the conditional probability is taken as the candidate label information, θ represents the model parameter, α represents the adjustable parameter, β represents the confidence parameter, that is, the degree of trust of the first module in the label information, e represents the base of the natural logarithm, N represents the number of types of label information, and p i Represents the predicted probability of the first module for the i-th category label information.

[0060] It should be noted that the advantage of introducing the confidence parameter is that it can help the entity recognition model to more accurately evaluate and select label information, thereby improving the reliability and accuracy of entity recognition. By introducing the confidence parameter into the model, the results can be weighted according to the model's confidence in the label information, thereby filtering out unreliable prediction results and improving the stability of entity recognition. Such an optimized design makes the entity recognition model more reliable when processing information such as patient medical records, reduces the possibility of misidentification, and thus improves the accuracy and efficiency of patient information records.

[0061] The candidate label information is used as label information, that is, output as a specific named entity.

[0062] Specifically, the average prediction probability of the model for the label is used as the value of parameter β, which represents the overall trust of the first module in the model for the label information. When the model's prediction probability for all labels is high, the value of parameter β will increase accordingly, reflecting the model's high confidence in the label information. That is, the higher the degree of trust, the more accurate the selected label information is, and irrelevant label information with low confidence can be effectively eliminated.

[0063] S305: Input the specific named entity into the second module of the trained entity recognition model.

[0064] In a possible implementation manner, S305 specifically includes:

[0065] Construct a loss function based on the conditional random field algorithm:

[0066] L(θ)=-logP(Y|X;θ)+λ‖θ‖ 2

[0067] Among them, L(θ) represents the loss function under the model parameter θ, log represents the natural logarithm function, represents, λ represents the regularization parameter, ‖θ‖ 2 It means taking the square norm of the model parameters.

[0068] The specific named entity is corrected through the loss function, and the specific named entity corresponding to the minimum loss function value is output.

[0069] It should be noted that the correction of specific named entities is achieved by constructing a loss function based on the conditional random field algorithm. This loss function can quantify the accuracy of the model prediction and adjust the model parameters in combination with the regularization parameter to minimize the value of the loss function. By minimizing the loss function, the specific named entity corresponding to the best prediction result can be found, thereby improving the performance and accuracy of the entity recognition model. This method can effectively optimize the model parameters, reduce the deviation of the prediction results, and make the entity recognition process more accurate and reliable.

[0070] S306: Output the corrected specific named entity as the patient information to be recorded through the output module in the trained entity recognition model.

[0071] It should be noted that multiple automated screening and confirmation of patient information to be recorded can reduce the workload of medical staff while ensuring the accuracy of patient information to be recorded. The named entity recognition algorithm and conditional random field algorithm are used in combination, and confidence parameters and loss function optimization are introduced to improve the accuracy and reliability of the entity recognition model when extracting patient information. During the training phase, the model is trained using a historical patient medical record information dataset so that the model can learn a wealth of medical knowledge. The introduction of confidence parameters can help filter unreliable prediction results and improve the stability of the model. In the application phase, the construction of a loss function based on the conditional random field algorithm further optimizes the model parameters, enabling the model to more accurately identify and correct specific named entities, thereby improving the performance and accuracy of entity recognition. This enables the entity recognition model to better adapt to the needs of medical information processing and provides reliable technical support for the recording and management of patient information.

[0072] In actual application, nursing staff need to record multiple patients during information recording, which is a heavy workload and prone to errors. In particular, information conveyed orally or in writing to nursing staff causes serious memory workload and confusion in patient medical records, which easily leads to missing information records. Guided by the patient's medical record information, the information to be recorded is automatically generated according to the time period of the medical record information and medical information recording. The attending physician's medical record is the main factor, and the nursing staff cooperates with complete and accurate patient information recording. The nursing staff only needs to enter the patient's name or number to obtain the patient's information to be recorded, that is, what patient data is recorded at what time, optimize and unify the patient information management method, make the patient information recording process transparent and standardized, avoid the problem of medical accidents that cannot be accurately attributed due to incomplete and non-standard information recorded orally or in writing, improve the management system of primary medical information, improve the efficiency and quality of medical information management links, and reduce the workload of front-line nursing staff.

[0073] S4: Classify the patient information to be recorded and generate a category of the patient information to be recorded.

[0074] It should be noted that the patient information to be recorded may fall into many categories depending on the type of disease the patient suffers. Each related type of information to be recorded is classified into one category. The final clustering result is the secondary classification result of the information to be recorded. Similar information to be recorded is classified into the same larger category for easy reference and recording.

[0075] In a possible implementation, the record information is classified by a K-means clustering algorithm. S4 specifically includes:

[0076] S401: Vectorize the patient information to be recorded to obtain multiple feature vectors of the patient information to be recorded.

[0077] S402: Determine the number and cluster centers of the feature vectors of the patient information to be recorded, wherein the cluster centers are randomly selected from the feature vectors of the patient information to be recorded.

[0078] S403: Distribute the feature vectors of the patient information to be recorded to different cluster centers according to the principle of closest distance:

[0079] c i =argmin j ‖x i -μ j ‖ 2

[0080] Among them, c i Represents the feature vector x of the i-th patient information to be recorded i The closest cluster center represents the jth cluster center.

[0081] S404: Calculate the mean of the feature vector of each type of patient information to be recorded, and redetermine the cluster center in the distribution result:

[0082]

[0083] Among them, μ' j represents the re-determined cluster center, S j Represents the set of feature vectors of patient information to be recorded in the jth cluster center.

[0084] S405: The number of iterations is increased by 1, and S403-S404 are repeated until the cluster center remains unchanged or the maximum number of iterations is reached.

[0085] Among them, the K-means clustering algorithm is a commonly used unsupervised learning algorithm, which is used to divide the data set into K clusters, so that each data point belongs to the cluster represented by the cluster center closest to it. The advantage of the K-means clustering algorithm is that it is simple and efficient, suitable for processing large-scale data sets, and the results have good interpretability and can intuitively present the clustering of data. In addition, the K-means algorithm is relatively insensitive to the selection of the initial cluster center, and can obtain stable clustering results in multiple runs, so it has wide applicability and reliability in practical applications.

[0086] It should be noted that the advantage of using the K-means algorithm to classify the information to be recorded is its simplicity, ease of use and high efficiency. First, the K-means algorithm can automatically divide the information to be recorded into different categories without presetting the number of categories, thereby reducing the cost of manual intervention. Secondly, the algorithm performs well when processing large-scale data sets, has high computational efficiency, and can quickly classify large amounts of patient information. In addition, the results of the K-means algorithm are intuitively interpretable, and each category represents a group of patient information with similar characteristics, which helps medical staff understand and analyze the distribution of patient information. Therefore, the K-means algorithm can improve data processing efficiency when classifying recorded information and provide strong support for medical decision-making and management.

[0087] S5: Generate an input window for patient identity information based on the XSL file.

[0088] The input window includes various sub-input windows bound to the patient information to be recorded.

[0089] The XSL file includes an XSLT tool and an XPath tool, and the input window includes a plurality of operation buttons regarding the information to be recorded.

[0090] Among them, the XSL file is a language used to describe the presentation of XML documents. It is usually used to convert XML documents into user interfaces or other formats. The XSL file is used to generate an input window for recording patient identity information. The XSL file is a style sheet language that can define the appearance and layout of the patient identity information input window as needed. Using the XSLT tool and the XPath tool, the style and structure of the input window can be easily customized to meet specific needs and user preferences. The input window is bound to the patient information to be recorded to ensure that the entered data corresponds correctly to the patient identity information and avoid information confusion or entry errors. This binding relationship can improve the accuracy and reliability of data entry. The input window contains multiple operation buttons that can implement various operations on the recorded information, such as saving, editing, and deleting. These operation buttons provide more functions, allowing users to easily manage and process the information to be recorded. The input window generated by the XSL file can be designed with a simple, intuitive, and easy-to-operate interface according to user needs. Reasonable layout and clear operation buttons enable users to quickly understand and use the input window, improving user experience and work efficiency. Since the XSL file is based on standard XML technology, the generated input window has good cross-platform compatibility. This means that the input window can be used across different devices and platforms, whether it is desktop or mobile, and can maintain good display and functionality.

[0091] In a possible implementation, S5 specifically includes:

[0092] S501: Convert the patient information to be recorded into Extensible Hypertext Markup Language using an XSLT tool.

[0093] S502: Using the converted extensible hypertext language page as a template to create an original component.

[0094] S503: modifying the tag characters existing in the page corresponding to the converted extensible hypertext language into custom tags, and creating custom components, wherein each custom component corresponds to a sub-input window.

[0095] S504: Connect the custom component with the original component.

[0096] S505: Adding attributes to the patient information to be recorded through the form controls of the XSL file, wherein the attributes include the name and type of the patient information to be recorded.

[0097] S506: Regularize the description of the attribute to obtain a character string set related to the attribute.

[0098] S507: Obtain the field corresponding to the specific named entity in the patient information to be recorded through the XPath tool.

[0099] S508: Match the field corresponding to the specific named entity with the string set. If the match succeeds, retain the attribute corresponding to the specific named entity. Otherwise, create the attribute of the specific named entity that fails to match in the form control.

[0100] S509: Pass the retained and created properties to the custom component.

[0101] S510: Submit the custom component to obtain a sub-input window.

[0102] It should be noted that converting the patient information to be recorded into the form of Extensible Hypertext Markup Language (XML) and creating a custom input window based on the XML form provides medical staff with a convenient information entry and management tool. Through the XML format, the information is highly structured and easy to parse and process. The creation of a custom input window makes the input information more intuitive and friendly, which is conducive to improving the work efficiency of medical staff and the accuracy of information recording. At the same time, the use of XSLT tools and XPath tools can realize flexible processing and customized display of information, meet the needs of different medical scenarios, and provide reliable support for medical informatization.

[0103] S6: Receive patient identity information.

[0104] Among them, the patient identity information can be input by medical staff, that is, the patient's number or name in the hospital. That is, you only need to enter the patient's identity information to directly display the relevant content and the time point of recording, that is, the doctor's order is the main factor, which can effectively avoid the problems of incomplete or incorrect recording of patient information to be recorded due to memory errors of nursing staff, reducing the memory burden of medical staff. In addition, it can also effectively attribute responsibilities when dividing responsibilities, that is, what has not been recorded in time can be intuitively observed through the sub-input window, greatly improving the orderly management ability of medical processes.

[0105] S7: Displaying multiple sub-input windows associated with the patient identification information.

[0106] S8: receiving user input through the sub-input window.

[0107] In one possible implementation, a portable electronic recording device includes an input panel and a speech recognizer.

[0108] User input includes text input and voice input.

[0109] The users are specifically medical personnel, that is, personnel who use portable electronic recording devices.

[0110] It is understandable that the input panel provides a text input function, and the user can input text information through a keyboard or a touch screen. In addition, the device is also equipped with a voice recognizer, which allows the user to convert spoken information into text through voice input. This design can meet the needs and preferences of different users, provide a flexible and convenient way to enter information, and help improve the user's work efficiency and operational convenience.

[0111] S9: Display user input according to the category of patient information to be recorded.

[0112] In a possible implementation, S9 specifically includes:

[0113] S901: Receive designated patient identity information.

[0114] S902: Obtain user input related to the designated patient identity information, and output the category of patient information to be recorded input by the user.

[0115] S903: Extract the patient information to be recorded according to the category of the patient information to be recorded.

[0116] S904: Extracting each sub-input window bound to the patient information to be recorded.

[0117] S905: Display the user input through the extracted sub-input window.

[0118] It should be noted that different sub-input windows are displayed by category, and the display interface is clearly divided into categories, which is convenient for reference and improves work efficiency.

[0119] Optionally, it also includes:

[0120] Generate a user input backup and save it to the HIS system.

[0121] It is understandable that the relevant information entered by the user can be directly saved locally. In order to facilitate the internal interaction of medical information, storing the user input backup in the HIS system can effectively ensure that different doctors of the same patient can timely access the relevant information of the patient, thereby improving information management efficiency.

[0122] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0123] In the present invention, an entity recognition model is constructed based on a named entity recognition algorithm and a conditional random field algorithm. The patient information to be recorded is first identified by the named entity recognition algorithm, and then the conditional random field algorithm is used to check the difference from the correct patient information to be recorded. After multiple screenings, the final patient information to be recorded is determined, and the deviation between the patient information and the patient's medical record information is reduced, thereby ensuring the accuracy of the information to be recorded obtained based on the patient's medical record information. Moreover, this method can also be applied to electronic medical record information of different formats in different hospitals, and can automatically extract the patient information to be recorded, thereby reducing the memory burden of medical staff when collecting patient information. Then, the obtained patient information to be recorded is secondary classified, so that similar information to be recorded is classified as the same In the larger category, it is convenient for medical staff to collect and check patient information, avoiding the confusion of information management. Finally, the XSL file is used to generate various sub-input windows bound to the patient information to be recorded. The input window generated by the XSL file can be easily customized according to the needs, making the interface simple and beautiful. Such an input interface can make medical staff more focused on recording patient information, reducing unnecessary visual interference, binding the information to be recorded with the sub-input window, and providing operation buttons at the same time, so that medical staff can record patient information intuitively and conveniently, improving work efficiency and recording quality, avoiding medical problems caused by deviations in manual memory of medical staff, and improving information management efficiency and accuracy. In addition, the portable electronic recording device is detachably arranged on the sleeve of the work clothes through a single-sided transparent cloth bag, without the need to hold a notebook or recorder. The portable electronic recording device is connected to the HIS system, which can greatly release the work efficiency of medical staff and ensure the integrity of the recorded patient information.

[0124] Reference Manual Attached Figure 4 , showing a structural schematic diagram of a patient information recording system provided by the present invention.

[0125] The present invention further provides a patient information recording system 20, which is applied to the above-mentioned patient information recording method, comprising:

[0126] Processor 201;

[0127] The memory 202 stores computer-readable instructions, and when the computer-readable instructions are executed by the processor 201, the patient information recording method described in the method embodiment is implemented.

[0128] The patient information recording system 20 provided by the present invention can execute the above-mentioned patient information recording method and achieve the same or similar technical effects. To avoid repetition, the present invention will not go into details.

[0129] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0130] In the present invention, an entity recognition model is constructed based on a named entity recognition algorithm and a conditional random field algorithm. The patient information to be recorded is first identified by the named entity recognition algorithm, and then the conditional random field algorithm is used to check the difference from the correct patient information to be recorded. After multiple screenings, the final patient information to be recorded is determined, and the deviation between the patient information and the patient's medical record information is reduced, thereby ensuring the accuracy of the information to be recorded obtained based on the patient's medical record information. Moreover, this method can also be applied to electronic medical record information of different formats in different hospitals, and can automatically extract the patient information to be recorded, thereby reducing the memory burden of medical staff when collecting patient information. Then, the obtained patient information to be recorded is secondary classified, so that similar information to be recorded is classified as the same In the larger category, it is convenient for medical staff to collect and check patient information, avoiding the confusion of information management. Finally, the XSL file is used to generate various sub-input windows bound to the patient information to be recorded. The input window generated by the XSL file can be easily customized according to the needs, making the interface simple and beautiful. Such an input interface can make medical staff more focused on recording patient information, reducing unnecessary visual interference, binding the information to be recorded with the sub-input window, and providing operation buttons at the same time, so that medical staff can record patient information intuitively and conveniently, improving work efficiency and recording quality, avoiding medical problems caused by deviations in manual memory of medical staff, and improving information management efficiency and accuracy. In addition, the portable electronic recording device is detachably arranged on the sleeve of the work clothes through a single-sided transparent cloth bag, without the need to hold a notebook or recorder. The portable electronic recording device is connected to the HIS system, which can greatly release the work efficiency of medical staff and ensure the integrity of the recorded patient information.

[0131] It should be understood that the processor in the embodiment of the present invention may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0132] It should also be understood that the memory in the embodiments of the present invention may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0133] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented by software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the process or function described in the embodiment of the present invention is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center by wired (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state hard disk.

[0134] It should be understood that the term "and / or" in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship, but it may also indicate an "and / or" relationship. Please refer to the context for specific understanding.

[0135] In the present invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can be represented by: a, b, c, ab, ac, bc, or abc, where a, b, c can be single or multiple.

[0136] It should be understood that in various embodiments of the present invention, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0137] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0138] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described equipment, devices and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0139] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another device, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0140] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0141] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0142] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in various embodiments of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0143] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein when the program is executed by a processor, the patient information recording method as described in the method embodiment is implemented.

[0144] A computer-readable storage medium provided by the present invention can implement the steps and effects of the patient information recording method of the above method embodiment. To avoid repetition, the present invention will not go into details.

[0145] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0146] In the present invention, an entity recognition model is constructed based on a named entity recognition algorithm and a conditional random field algorithm. The patient information to be recorded is first identified by the named entity recognition algorithm, and then the conditional random field algorithm is used to check the difference from the correct patient information to be recorded. After multiple screenings, the final patient information to be recorded is determined, and the deviation between the patient information and the patient's medical record information is reduced, thereby ensuring the accuracy of the information to be recorded obtained based on the patient's medical record information. Moreover, this method can also be applied to electronic medical record information of different formats in different hospitals, and can automatically extract the patient information to be recorded, thereby reducing the memory burden of medical staff when collecting patient information. Then, the obtained patient information to be recorded is secondary classified, so that similar information to be recorded is classified as the same In the larger category, it is convenient for medical staff to collect and check patient information, avoiding the confusion of information management. Finally, the XSL file is used to generate various sub-input windows bound to the patient information to be recorded. The input window generated by the XSL file can be easily customized according to the needs, making the interface simple and beautiful. Such an input interface can make medical staff more focused on recording patient information, reducing unnecessary visual interference, binding the information to be recorded with the sub-input window, and providing operation buttons at the same time, so that medical staff can record patient information intuitively and conveniently, improving work efficiency and recording quality, avoiding medical problems caused by deviations in manual memory of medical staff, and improving information management efficiency and accuracy. In addition, the portable electronic recording device is detachably arranged on the sleeve of the work clothes through a single-sided transparent cloth bag, without the need to hold a notebook or recorder. The portable electronic recording device is connected to the HIS system, which can greatly release the work efficiency of medical staff and ensure the integrity of the recorded patient information.

[0147] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

[0148] Compared with the prior art, the above technical solution has at least the following beneficial effects:

[0149] In the present invention, the light and shadow of the static objects can be optimized based on the light baking technology, and the light streaming of the game animation can be optimized based on the light streaming processing technology, and the screen of the nostalgic online game can be optimized to enhance the attractiveness of the nostalgic online game to new players and enhance the market competitiveness of the nostalgic online game.

[0150] There are a few points to note:

[0151] (1) The drawings of the embodiments of the present invention only relate to the structures related to the embodiments of the present invention, and other structures may refer to the general design.

[0152] (2) For the sake of clarity, in the drawings used to describe the embodiments of the present invention, the thickness of the layers or regions is exaggerated or reduced, that is, these drawings are not drawn according to the actual scale. It is understood that when an element such as a layer, film, region or substrate is referred to as being "on" or "under" another element, the element may be "directly" "on" or "under" the other element or there may be intermediate elements.

[0153] (3) In the absence of conflict, the embodiments of the present invention and the features therein may be combined with each other to obtain new embodiments.

[0154] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. The protection scope of the present invention shall be based on the protection scope of the claims.

Claims

1. A method for recording patient information, characterized in that: Applied to a portable electronic recording device, the portable electronic recording device is detachably arranged on a sleeve of a work uniform through a single-sided transparent cloth bag, and the portable electronic recording device is connected to a HIS system. The method comprises: S1: Obtain patient medical record information; S2: constructing an entity recognition model based on a named entity recognition algorithm and a conditional random field algorithm, wherein the entity recognition model comprises an input module, a first module based on the named entity recognition algorithm, a second module based on the conditional random field algorithm, and an output module connected in sequence; S3: performing semantic extraction on the patient medical record information through the entity recognition model to obtain the patient information to be recorded, wherein the patient information to be recorded includes the patient identity information; S4: Classifying the patient information to be recorded to generate a category of the patient information to be recorded; S5: generating an input window for the patient identity information based on the XSL file, wherein the input window includes various sub-input windows bound to the patient information to be recorded, wherein the XSL file includes an XSLT tool and an XPath tool, and the input window includes a plurality of operation buttons for the information to be recorded; S6: receiving the patient identity information; S7: displaying a plurality of sub-input windows associated with the patient identification information; S8: receiving user input through the sub-input window; S9: Displaying the user input according to the category of the patient information to be recorded; Wherein, the S3 specifically includes: S301: Acquire a historical patient medical record information data set, wherein the historical patient medical record information data set includes a plurality of historical patient medical record information with label information, wherein the label information includes patient identity information, items to be recorded, and time of items to be recorded in each historical patient medical record information; S302: Using the historical medical record information dataset to train the entity recognition model; S303: receiving the patient medical record information through an input module in the trained entity recognition model; S304: extracting specific named entities, namely, the label information, from the patient medical record information through the first module in the trained entity recognition model, wherein the specific named entities include patient identity information, items to be recorded, and time of items to be recorded; S305: Inputting the specific named entity into the second module of the trained entity recognition model; S306: Outputting the corrected specific named entity as the patient information to be recorded through an output module in the trained entity recognition model; Wherein, the S304 specifically includes: The confidence parameter is introduced, and the specific named entity in the patient medical record information, namely the label information, is extracted through the first module in the trained entity recognition model, specifically: AND * =argmax Y P(Y|X;θ) α Among them, Y * represents the candidate label information, P(Y|X;θ) represents the conditional probability of the label information Y given the patient's medical record information X, argmax Y represents taking the label information Y that maximizes the conditional probability as the candidate label information, θ represents the model parameter, α represents the adjustable parameter, β represents the confidence parameter, i.e., the degree of trust of the first module in the label information, e represents the base of the natural logarithm, N represents the number of types of label information, and p i represents the predicted probability of the first module for the i-th category label information; Outputting the candidate label information as the label information, that is, as the specific named entity; Wherein, the S305 specifically includes: Construct a loss function based on the conditional random field algorithm: L(θ)=-logP(Y|X;θ)+λ‖θ‖ 2 Wherein, L(θ) represents the loss function under the model parameter θ, log represents the natural logarithm function, represents, λ represents the regularization parameter, ‖θ‖ 2 Indicates taking the square norm of the model parameters; Correcting the specific named entity by using the loss function, and outputting the specific named entity corresponding to the minimum loss function value; The information to be recorded is classified by using a K-means clustering algorithm; S4 specifically includes: S401: vectorizing the patient information to be recorded to obtain a plurality of feature vectors of the patient information to be recorded; S402: Determine the number and cluster centers of the feature vectors of the patient information to be recorded, wherein the cluster centers are randomly selected from the feature vectors of the patient information to be recorded; S403: Allocate the feature vectors of the patient information to be recorded to different cluster centers according to the principle of closest distance: c i =argmin j ‖x i -m j ‖ 2 Among them, c i Represents the feature vector x of the i-th patient information to be recorded i The closest cluster center represents the jth cluster center; S404: Calculate the mean of the feature vector of each type of patient information to be recorded, and redetermine the cluster center in the distribution result: Among them, μ' j represents the re-determined cluster center, S j represents the set of feature vectors of patient information to be recorded in the jth cluster center; S405: the number of iterations is increased by 1, and S403-S404 are repeated until the cluster center remains unchanged or the maximum number of iterations is reached; Wherein, the S5 specifically includes: S501: converting the patient information to be recorded into extensible hypertext markup language by using the XSLT tool; S502: Using the converted extensible hypertext language page as a template to create an original component; S503: modifying the tag characters existing in the page corresponding to the converted extensible hypertext language into custom tags, and creating custom components, wherein each custom component corresponds to a sub-input window; S504: Connecting the custom component to the original component; S505: adding attributes to the patient information to be recorded through the form controls of the XSL file, wherein the attributes include the name and type of the patient information to be recorded; S506: performing regularization description on the attribute to obtain a character string set related to the attribute; S507: Acquire, by using the XPath tool, a field corresponding to a specific named entity in the patient information to be recorded; S508: Matching the field corresponding to the specific named entity with the string set, retaining the attribute corresponding to the specific named entity if the match succeeds, otherwise creating the attribute of the specific named entity that fails to match in the form control; S509: passing the retained and created attributes to the custom component; S510: Submit the custom component to obtain the sub-input window.

2. The patient information recording method according to claim 1, characterized in that: The portable electronic recording device includes an input panel and a speech recognizer; The user input includes text input and voice input.

3. The patient information recording method according to claim 1, characterized in that: The S9 specifically includes: S901: receiving designated patient identity information; S902: Acquire user input related to the specified patient identity information, and output the category of patient information to be recorded input by the user; S903: extracting the patient information to be recorded respectively according to the categories of the patient information to be recorded; S904: extracting each sub-input window bound to the patient information to be recorded; S905: Display the user input through the extracted sub-input window.

4. A patient information recording system, characterized in that: include: processor; A memory having computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by the processor, the patient information recording method according to any one of claims 1 to 3 is implemented.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the patient information recording method according to any one of claims 1 to 3 is implemented.

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