CN110874409A - Disease grading prediction system, method, electronic device and readable storage medium - Google Patents
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Abstract
The application discloses a disease condition grading prediction system and method based on an electronic medical record, electronic equipment and a storage medium. The system comprises: the electronic medical record information is stored through the first storage module, the electronic medical record information is read through the first information filter and is filtered, and for multi-modal data of the structured and text, the structured data and the text data are processed through a hierarchical predictor based on deep learning and an attention mechanism, so that text feature mining information, an illness state hierarchical prediction result and a visual analysis text are obtained. By adopting the grading prediction system, the electronic medical record information can be processed more quickly, the grading prediction result of the disease condition can be obtained, the obtained grading prediction result of the disease condition, the text feature mining information and the visual analysis text can be used as the reference of a doctor, and the doctor is assisted in grading so as to improve the grading speed and the grading accuracy.
Description
Technical Field
The present invention relates to the field of information processing technologies, and in particular, to a disease condition grading prediction system, method, electronic device, and readable storage medium.
Background
The triage is the first gateway for emergency patients to see a doctor, and is the most important factor affecting the emergency department. The patient can be treated according to the critical degree of the illness state of the patient in a grading way, thereby being beneficial to fully utilizing emergency resources, maintaining the treatment order of the emergency patients, shortening the waiting time of the critical patients, improving the working efficiency and preventing the emergency resources from being exhausted in advance due to insufficient triage or over triage.
The advanced triage standards in foreign countries are all classified according to the disease conditions and are generated under specific medical environment and social background, while the domestic and foreign medical insurance systems, emergency modes and medical treatment modes are greatly different, so that the preliminary examination and triage standards cannot be carried or applied, and therefore, a set of simple, convenient, effective and scientific emergency treatment preliminary examination and triage standards which are in line with international conditions and are favorable for disease condition classification in China need to be established.
The experience triage mode is mainly adopted at the present stage of China, so that the requirement on the level of doctors is high, the triage speed is low, and the condition that emergency resources are exhausted in advance due to insufficient triage or excessive triage is easy to occur along with the rapid increase of the number of emergency patients.
Disclosure of Invention
In view of the above, embodiments of the present invention are proposed to provide a disease grading prediction system, method, electronic device and readable storage medium that overcome or at least partially solve the above problems.
In a first aspect, the present application provides a disease staging prediction system based on an electronic medical record, the system including:
the first storage module is used for storing electronic medical record information, and the electronic medical record information comprises objective indexes, current medical history and physical examination results;
the first information filter is connected with the first storage module and used for reading the electronic medical record information from the first storage module and filtering the electronic medical record information to obtain the filtered electronic medical record information which comprises structured data and text data;
and the hierarchical predictor is used for respectively inputting the structured data and the text data into an emergency disease hierarchical prediction model to obtain text characteristic mining information, a disease hierarchical prediction result and a visual analysis text.
Optionally, the emergency condition staging prediction model comprises a representation model and a fusion model, and the staging predictor comprises:
the first representation module is used for inputting the text data into the representation model to obtain the visual analysis text, the text feature mining information and a text feature vector;
the second representation module is used for inputting the structured data into the representation model to obtain a structured feature vector;
and the fusion module is used for inputting the structural feature vector and the text feature vector into the fusion model to obtain the disease condition grading prediction result.
Optionally, the first representation module comprises:
the mapping submodule is used for inputting the text data into the representation model, mapping each Chinese character of the text data to a vector, and extracting context information embedded in the text data to obtain an embedded vector;
the attention submodule is used for carrying out attention processing on the embedded vector to obtain the visual analysis text and the text feature vector;
and the extraction submodule is used for carrying out phrase extraction on the visual analysis text to obtain the text feature mining information.
Optionally, the system further comprises:
the second storage module is used for storing a plurality of electronic medical record information samples;
the second information filter is connected with the second storage module and used for reading the plurality of electronic medical record information samples from the second storage module and filtering the plurality of electronic medical record information samples to obtain a plurality of filtered medical record information samples which comprise a plurality of structured data samples and text data samples;
and the model trainer is used for dividing the multiple structured data samples and the text data samples into a training set, a verification set and a test set, performing multiple rounds of training on the preset model until the evaluation score of the trained preset model does not rise within the preset number of rounds, stopping training, and determining the model corresponding to the highest evaluation score as the emergency disease condition graded prediction model.
In a second aspect, the present application further provides a method for predicting a disease condition based on an electronic medical record, where the method includes:
storing electronic medical record information, wherein the electronic medical record information comprises objective indexes, current medical history and physical examination results;
reading the electronic medical record information and filtering the electronic medical record information to obtain filtered electronic medical record information which comprises structured data and text data;
and respectively inputting the structured data and the text data into an emergency disease condition grading prediction model to obtain text characteristic mining information, a disease condition grading prediction result and a visual analysis text.
Optionally, the emergency disease condition grading prediction model includes a representation model and a fusion model, and the structured data and the text data are respectively input into the emergency disease condition grading prediction model to obtain text feature mining information, a disease condition grading prediction result, and a visual analysis text, including:
inputting the text data into the representation model to obtain the visual analysis text, the text feature mining information and a text feature vector;
inputting the structured data into the representation model to obtain a structured feature vector;
and inputting the structural feature vector and the text feature vector into the fusion model to obtain the disease condition grading prediction result.
Optionally, inputting the text data into a representation model to obtain the visualization analysis text, the text feature mining information, and a text feature vector, where the method includes:
inputting the text data into the representation model, mapping each Chinese character of the text data to a vector, and extracting context information embedded in the text data to obtain an embedded vector;
performing attention processing on the embedded vector to obtain the visual analysis text and a text feature vector;
and performing phrase extraction on the visual analysis text to obtain text feature mining information.
Optionally, the method further comprises:
storing a plurality of electronic medical record information samples;
reading the plurality of electronic medical record information samples and filtering the plurality of electronic medical record information samples to obtain a plurality of filtered electronic medical record information samples, wherein the plurality of filtered electronic medical record information samples comprise a plurality of structured data samples and text data samples;
and dividing the multiple structured data samples and the text data samples into a training set, a verification set and a test set, performing multiple rounds of training on the preset model until the evaluation score of the trained preset model does not rise within the preset rounds, stopping training, and determining the model corresponding to the highest evaluation score as the emergency disease condition grading prediction model.
In a third aspect, an embodiment of the present application further provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the computer program when executed by the processor implements the steps of the method for predicting a medical condition grade based on an electronic medical record according to the second aspect.
In a fourth aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the method for predicting a disease condition rating based on an electronic medical record according to the second aspect.
The embodiment of the invention has the following advantages:
in the embodiment of the invention, the electronic medical record information is stored through the first storage module, the electronic medical record information is read through the first information filter and is filtered to obtain the structured data and the text data, and the structured data and the text data are processed through the hierarchical predictor and the hierarchical prediction model to obtain the text characteristic mining information, the disease condition hierarchical prediction result and the visual analysis text. By adopting the grading prediction system, the electronic medical record information can be processed more quickly, the grading prediction result of the disease condition can be obtained, the used grading prediction model has higher grading speed and higher accuracy, the obtained grading prediction result of the disease condition, the text feature mining information and the visual analysis text can be used as the reference of a doctor, the doctor is assisted in grading to improve the grading speed and the grading accuracy, and the limited emergency call resources are fully utilized.
Drawings
FIG. 1 is a block diagram of a medical condition grading prediction system based on electronic medical records according to the present invention;
FIG. 2 is a block diagram of a hierarchical predictor of the present invention;
FIG. 3 is a block diagram of another electronic medical record-based disease staging prediction system according to the present invention;
FIG. 4 is a flowchart illustrating steps of a method for medical condition stratification prediction based on electronic medical records according to the present invention.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Referring to fig. 1, fig. 1 is a block diagram illustrating a medical condition grading prediction system based on electronic medical records according to an embodiment of the present application, and as shown in fig. 1, the system includes the following structures:
the first storage module 101 is configured to store electronic medical record information, where the electronic medical record information includes objective indicators, current medical history, and physical examination results.
In this embodiment, before the first storage module stores the electronic medical record information, the medical staff has acquired the electronic medical record information of the patient through various medical means, including objective indexes, current medical history and physical examination results, specifically, the objective indexes may include respiration rate, systolic pressure, diastolic pressure, pulse, body temperature, blood oxygen saturation, age and gender, the current medical history may include trauma, loss of consciousness, dysphagia, vomiting, limb swelling and pain, epilepsy and the like, and the physical examination may include mental state, reflex function, limb tension, respiratory sound, heart rate, tenderness, rebound pain and the like. Objective indexes, current medical history and physical examination results are input into the electronic medical record-based disease grading prediction by medical staff and are stored by the first storage module.
And the first information filter 102 is connected with the first storage module and is used for reading the electronic medical record information from the first storage module and filtering the electronic medical record information to obtain the filtered electronic medical record information, wherein the filtered electronic medical record information comprises structured data and text data.
In this embodiment, the first information filter is connected to the first storage module, and further reads the electronic medical record information from the first storage module, and filters the electronic medical record information, specifically, deletes missing data and erroneous recorded data, for example, deletes the following data:
(1) temperature <30 deg.C, (2) SBP >400mmHg, and (3) DBP <5 mmHg.
And further obtaining the filtered electronic medical record information which comprises structured data and text data, wherein the structured data comprises objective indexes, and the text data comprises current medical history and physical examination results so as to improve the classification accuracy.
And the hierarchical predictor 103 is used for respectively inputting the structured data and the text data into an emergency disease hierarchical prediction model to obtain text characteristic mining information, a disease hierarchical prediction result and a visual analysis text.
In the embodiment, the hierarchical predictor inputs the structured data and the text data into a trained emergency disease hierarchical prediction model, respectively processes the structured data and the text data through the emergency disease hierarchical prediction model, and obtains text feature mining information, disease hierarchical prediction results and visual analysis texts through an LSTM (long short term memory network) in combination with an attention mechanism to serve as reference for doctor grading, so that the grading rate and the grading accuracy are improved.
In the embodiment of the invention, the electronic medical record information is stored through the first storage module, the electronic medical record information is read through the first information filter and is filtered to obtain the structured data and the text data, and the structured data and the text data are processed through the hierarchical predictor and the hierarchical prediction model to obtain the text characteristic mining information, the disease condition hierarchical prediction result and the visual analysis text. By adopting the grading prediction system, the electronic medical record information can be processed more quickly, the grading prediction result of the disease condition can be obtained, the used grading prediction model has higher grading speed and higher accuracy, the obtained grading prediction result of the disease condition, the text feature mining information and the visual analysis text can be used as the reference of a doctor, the doctor is assisted in grading to improve the grading speed and the grading accuracy, and the limited emergency call resources are fully utilized.
Referring to fig. 2, fig. 2 shows a block diagram of a hierarchical predictor according to an embodiment of the present application, and as shown in fig. 2, in a possible implementation, the hierarchical prediction model of an emergency condition includes a representation model and a fusion model, and the hierarchical predictor includes the following three modules 201 and 203:
the first representation module 201 is configured to input the text data into the representation model, so as to obtain the visualization analysis text, the text feature mining information, and a text feature vector.
In this embodiment, the first representation module can input text data into the representation model and process the text data to obtain a visualization analysis text, text feature mining information, and a text feature vector. And the visualized analysis text and the text feature mining information are directly output, and the text feature vector is used for subsequent continuous processing.
In one possible embodiment, the first representation module comprises:
the mapping submodule is used for inputting the text data into the representation model, mapping each Chinese character of the text data to a vector, and extracting context information embedded in the text data to obtain an embedded vector;
the attention submodule is used for carrying out attention processing on the embedded vector to obtain the visual analysis text and the text feature vector;
and the extraction submodule is used for carrying out phrase extraction on the visual analysis text to obtain the text feature mining information.
In the embodiment, the mapping submodule inputs text data into the representation model, each Chinese character of the text data is mapped to a vector through the Embedding layer, context information embedded in the text data is extracted through the BilSTM layer to obtain an embedded vector, the embedded vector is subjected to attention processing through the attention submodule to obtain a visual analysis text and a text feature vector, the visual analysis text is further processed through the extraction submodule to extract text features of phrase levels in the visual analysis text and obtain text feature mining information, the visual analysis text and the text feature mining information can be directly output, and the text feature vector is used for subsequent further processing. The extraction of the text features at the phrase level is more meaningful, and the grading accuracy can be improved, such as: if "no heat generation" is classified into "no" and "heat generation", the "no heat generation" cannot be found out correctly. The visualized analysis text and the text feature mining information can meet clinical interpretability, and in order to facilitate a doctor to quickly acquire important information, in a specific implementation mode, the important information (namely, the text feature mining information) in the visualized analysis text can be displayed in different colors, and the more important information is displayed in a darker color.
And a second representation module 202, configured to input the structured data into the representation model, so as to obtain a structured feature vector.
In this embodiment, the second representation module may input the structured data into the representation model, and perform feature extraction on the structured data through two full connection layers to obtain a structured feature vector.
In the above manner, the structured data and the text data are respectively processed by using different structural layers, so that more accurate data can be acquired.
And the fusion module 203 is used for inputting the structural feature vector and the text feature vector into the fusion model to obtain the disease condition grading prediction result.
In the embodiment, the fusion module can fuse the structural feature vector and the text feature vector for processing, so that an illness state grading prediction result is obtained, and the illness state grading prediction result, the text feature mining information and the visual analysis text can be used as reference for doctor grading after being output, so that the doctor grading rate and the grading accuracy are improved.
Referring to fig. 3, fig. 3 is a block diagram illustrating a structure of another electronic medical record-based disease staging prediction system according to an embodiment of the present application, and as shown in fig. 3, in a possible implementation, the system further includes:
the second storage module 301 is configured to store a plurality of electronic medical record information samples.
Before the electronic medical record information is classified, an emergency disease condition grading prediction model needs to be obtained through training, so a plurality of electronic medical record information samples need to be stored in the second storage module, wherein a plurality of pieces of existing electronic medical record information are obtained manually and are used as training samples, and each electronic medical record information sample is marked with a classification result manually and then is input into the second storage module.
A second information filter 302, connected to the second storage module, and configured to read the multiple electronic medical record information samples from the second storage module and filter the multiple electronic medical record information samples to obtain multiple filtered medical record information samples, where the multiple filtered medical record information samples include multiple structured data samples and text data samples;
the filtering operation of the second information filter is the same as the filtering operation of the first information filter, and the second information classifier is similar to the first information classifier, which may refer to the above specific explanation of the operations of the first information filter and the first information classifier, and will not be described herein again.
And the model trainer 303 is configured to divide the multiple structured data samples and the text data samples into a training set, a verification set and a test set, perform multiple rounds of training on the preset model until the evaluation score of the trained preset model does not rise within a preset number of rounds, stop the training, and determine the model corresponding to the highest evaluation score as the emergency disease condition grading prediction model.
In the present embodiment, multiple structured data samples and text data samples are divided into a training set, a validation set, and a test set, e.g., multiple structured data samples and text data samples can be divided into a training set of 70%, a validation set of 10% and a test set of 20%, performing multiple rounds of training on the preset model, specifically, training the preset model by using a training set, verifying the trained preset model by using a verification set, calculating an error value, updating the preset model by a back propagation algorithm, evaluating the trained preset model by using a test set, repeating the steps, and performing multiple rounds of training operations on the updated preset model until the evaluation score of the trained preset model does not rise within the preset number of rounds, stopping training, and determining the model corresponding to the highest evaluation score as the emergency disease condition grading prediction model. The model trainer is connected with the grading predictor, and can input the trained emergency disease grading prediction model into the grading predictor.
In the embodiment, the emergency disease condition grading prediction model obtained by training in the above manner can quickly process the structured data and the text data, and the obtained classification result has high accuracy.
Based on the same inventive concept, an embodiment of the present application provides a disease condition grading prediction method based on an electronic medical record, referring to fig. 4, where fig. 4 is a flowchart illustrating steps of the disease condition grading prediction method based on the electronic medical record according to the embodiment of the present application, and as shown in fig. 4, the method includes:
step S401: storing electronic medical record information, wherein the electronic medical record information comprises objective indexes, current medical history and physical examination results;
step S402: reading the electronic medical record information and filtering the electronic medical record information to obtain filtered electronic medical record information which comprises structured data and text data;
step S403: and respectively inputting the structured data and the text data into an emergency disease condition grading prediction model to obtain text characteristic mining information, a disease condition grading prediction result and a visual analysis text.
Optionally, the emergency disease condition grading prediction model includes a representation model and a fusion model, and the structured data and the text data are respectively input into the emergency disease condition grading prediction model to obtain text feature mining information, a disease condition grading prediction result, and a visual analysis text, including:
inputting the text data into the representation model to obtain the visual analysis text, the text feature mining information and a text feature vector;
inputting the structured data into the representation model to obtain a structured feature vector;
and inputting the structural feature vector and the text feature vector into the fusion model to obtain the disease condition grading prediction result.
Optionally, inputting the text data into a representation model to obtain the visualization analysis text, the text feature mining information, and a text feature vector, where the method includes:
inputting the text data into the representation model, and mapping each Chinese character of the text data to a vector through the representation model to obtain an embedded vector;
performing attention processing on the embedded vector to obtain the visual analysis text and a text feature vector;
and performing phrase extraction on the visual analysis text to obtain text feature mining information.
Optionally, the method further comprises:
storing a plurality of electronic medical record information samples;
reading the plurality of electronic medical record information samples and filtering the plurality of electronic medical record information samples to obtain a plurality of filtered electronic medical record information samples;
classifying the filtered electronic medical record information to obtain a plurality of structured data samples and text data samples;
and dividing the multiple structured data samples and the text data samples into a training set, a verification set and a test set, performing multiple rounds of training on the preset model until the evaluation score of the trained preset model does not rise within the preset rounds, stopping training, and determining the model corresponding to the highest evaluation score as the emergency disease condition grading prediction model.
Based on the same inventive concept, another embodiment of the present application provides an electronic device, including: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the method according to any of the above embodiments.
Based on the same inventive concept, another embodiment of the present application provides a computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, implements the steps in the method according to any of the above-mentioned embodiments of the present application.
As for the method embodiment, since it is basically similar to the system embodiment, the description is simple, and the relevant points can be referred to the partial description of the system embodiment.
The embodiments in the present specification are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, embodiments of the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, embodiments of the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
Embodiments of the present invention are described with reference to flowchart illustrations and/or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing terminal to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing terminal to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing terminal to cause a series of operational steps to be performed on the computer or other programmable terminal to produce a computer implemented process such that the instructions which execute on the computer or other programmable terminal provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present invention have been described, additional variations and modifications of these embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all such alterations and modifications as fall within the scope of the embodiments of the invention.
Finally, it should also be noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or terminal that comprises the element.
The present invention provides a disease grading prediction system based on electronic medical records, a disease grading prediction method based on electronic medical records, an electronic device and a computer readable storage medium, which are described in detail above, wherein specific examples are applied to illustrate the principle and the implementation of the present invention, and the description of the above embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present invention.
Claims (10)
1. A system for staging and predicting a medical condition based on an electronic medical record, the system comprising:
the first storage module is used for storing electronic medical record information, and the electronic medical record information comprises objective indexes, current medical history and physical examination results;
the first information filter is connected with the first storage module and used for reading the electronic medical record information from the first storage module and filtering the electronic medical record information to obtain the filtered electronic medical record information which comprises structured data and text data;
and the hierarchical predictor is used for respectively inputting the structured data and the text data into an emergency disease hierarchical prediction model to obtain text characteristic mining information, a disease hierarchical prediction result and a visual analysis text.
2. The system of claim 1, wherein the emergency condition staging prediction model comprises a representation model and a fusion model, and wherein the staging predictor comprises:
the first representation module is used for inputting the text data into the representation model to obtain the visual analysis text, the text feature mining information and a text feature vector;
the second representation module is used for inputting the structured data into the representation model to obtain a structured feature vector;
and the fusion module is used for inputting the structural feature vector and the text feature vector into the fusion model to obtain the disease condition grading prediction result.
3. The system of claim 2, wherein the first representation module comprises:
the mapping submodule is used for inputting the text data into the representation model, mapping each Chinese character of the text data to a vector, and extracting context information embedded in the text data to obtain an embedded vector;
the attention submodule is used for carrying out attention processing on the embedded vector to obtain the visual analysis text and the text feature vector;
and the extraction submodule is used for carrying out phrase extraction on the visual analysis text to obtain the text feature mining information.
4. The system of claim 1, further comprising:
the second storage module is used for storing a plurality of electronic medical record information samples;
the second information filter is connected with the second storage module and used for reading the plurality of electronic medical record information samples from the second storage module and filtering the plurality of electronic medical record information samples to obtain a plurality of filtered medical record information samples which comprise a plurality of structured data samples and text data samples;
and the model trainer is used for dividing the multiple structured data samples and the text data samples into a training set, a verification set and a test set, performing multiple rounds of training on the preset model until the evaluation score of the trained preset model does not rise within the preset number of rounds, stopping training, and determining the model corresponding to the highest evaluation score as the emergency disease condition graded prediction model.
5. A disease grading prediction method based on an electronic medical record is characterized by comprising the following steps:
storing electronic medical record information, wherein the electronic medical record information comprises objective indexes, current medical history and physical examination results;
reading the electronic medical record information and filtering the electronic medical record information to obtain filtered electronic medical record information, wherein the filtered electronic medical record information comprises
Structured data and text data;
and respectively inputting the structured data and the text data into an emergency disease condition grading prediction model to obtain text characteristic mining information, a disease condition grading prediction result and a visual analysis text.
6. The method of claim 5, wherein the emergency disease grading prediction model comprises a representation model and a fusion model, and the structured data and the text data are respectively input into the emergency disease grading prediction model to obtain text feature mining information, a disease grading prediction result and a visual analysis text, and the method comprises the following steps:
inputting the text data into the representation model to obtain the visual analysis text, the text feature mining information and a text feature vector;
inputting the structured data into the representation model to obtain a structured feature vector;
and inputting the structural feature vector and the text feature vector into the fusion model to obtain the disease condition grading prediction result.
7. The method of claim 6, wherein inputting the text data into a representation model, resulting in the visual analysis text, the text feature mining information, and a text feature vector, comprises:
inputting the text data into the representation model, mapping each Chinese character of the text data to a vector, and extracting context information embedded in the text data to obtain an embedded vector;
performing attention processing on the embedded vector to obtain the visual analysis text and a text feature vector;
and performing phrase extraction on the visual analysis text to obtain text feature mining information.
8. The method of claim 5, further comprising:
storing a plurality of electronic medical record information samples;
reading the plurality of electronic medical record information samples and filtering the plurality of electronic medical record information samples to obtain a plurality of filtered electronic medical record information samples, wherein the plurality of filtered electronic medical record information samples comprise a plurality of structured data samples and text data samples;
and dividing the multiple structured data samples and the text data samples into a training set, a verification set and a test set, performing multiple rounds of training on the preset model until the evaluation score of the trained preset model does not rise within the preset rounds, stopping training, and determining the model corresponding to the highest evaluation score as the emergency disease condition grading prediction model.
9. An electronic device, comprising: a memory, a processor and a computer program stored on the memory and executable on the processor, the computer program when executed by the processor implementing the steps of the method for predicting a medical condition grade based on an electronic medical record according to any one of claims 5 to 8.
10. A computer-readable storage medium, having a computer program stored thereon, which, when being executed by a processor, implements the steps of the method for medical condition stratification prediction based on electronic medical record according to any of claims 5 to 8.
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Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111724897A (en) * | 2020-06-12 | 2020-09-29 | 电子科技大学 | Motion function data processing method and system |
| CN112687367A (en) * | 2020-12-29 | 2021-04-20 | 中国人民解放军总医院 | Medical record grouping method, device and equipment based on dynamic disease condition and storage medium |
| WO2021159761A1 (en) * | 2020-09-09 | 2021-08-19 | 平安科技（深圳）有限公司 | Pathological data analysis method and apparatus, and computer device and storage medium |
| CN113793678A (en) * | 2021-08-31 | 2021-12-14 | 中国医学科学院北京协和医院 | Emergency classification auxiliary method and device for atypical critical patients |
| CN117009924A (en) * | 2023-10-07 | 2023-11-07 | 之江实验室 | Multi-mode self-adaptive multi-center data fusion method and system guided by electronic medical records |
| CN117558391A (en) * | 2024-01-11 | 2024-02-13 | 天津市胸科医院 | Postoperative condition deep learning method and system based on aortic dissection medical record data |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140257122A1 (en) * | 2013-03-08 | 2014-09-11 | Singapore Health Services Pte Ltd | System and method of determining a risk score for triage |
| CN107169298A (en) * | 2017-05-26 | 2017-09-15 | 深圳市第二人民医院 | A kind of intelligent emergency treatment classification system for distribution of out-patient department |
| US20190035505A1 (en) * | 2017-07-31 | 2019-01-31 | Boe Technology Group Co., Ltd. | Intelligent triage server, terminal and system based on medical knowledge base (mkb) |
| CN109508377A (en) * | 2018-11-26 | 2019-03-22 | 南京云思创智信息科技有限公司 | Text feature, device, chat robots and storage medium based on Fusion Model |
| CN110097955A (en) * | 2019-03-07 | 2019-08-06 | 南通奕霖智慧医学科技有限公司 | A kind of paediatrics intelligence emergency treatment previewing triage system based on support vector machine classifier |
| CN110164549A (en) * | 2019-05-20 | 2019-08-23 | 南通奕霖智慧医学科技有限公司 | A kind of paediatrics based on neural network classifier point examines method and system |
-
2019
- 2019-10-22 CN CN201911007766.1A patent/CN110874409A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140257122A1 (en) * | 2013-03-08 | 2014-09-11 | Singapore Health Services Pte Ltd | System and method of determining a risk score for triage |
| CN107169298A (en) * | 2017-05-26 | 2017-09-15 | 深圳市第二人民医院 | A kind of intelligent emergency treatment classification system for distribution of out-patient department |
| US20190035505A1 (en) * | 2017-07-31 | 2019-01-31 | Boe Technology Group Co., Ltd. | Intelligent triage server, terminal and system based on medical knowledge base (mkb) |
| CN109508377A (en) * | 2018-11-26 | 2019-03-22 | 南京云思创智信息科技有限公司 | Text feature, device, chat robots and storage medium based on Fusion Model |
| CN110097955A (en) * | 2019-03-07 | 2019-08-06 | 南通奕霖智慧医学科技有限公司 | A kind of paediatrics intelligence emergency treatment previewing triage system based on support vector machine classifier |
| CN110164549A (en) * | 2019-05-20 | 2019-08-23 | 南通奕霖智慧医学科技有限公司 | A kind of paediatrics based on neural network classifier point examines method and system |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111724897A (en) * | 2020-06-12 | 2020-09-29 | 电子科技大学 | Motion function data processing method and system |
| CN111724897B (en) * | 2020-06-12 | 2022-07-01 | 电子科技大学 | Motion function data processing method and system |
| WO2021159761A1 (en) * | 2020-09-09 | 2021-08-19 | 平安科技（深圳）有限公司 | Pathological data analysis method and apparatus, and computer device and storage medium |
| CN112687367A (en) * | 2020-12-29 | 2021-04-20 | 中国人民解放军总医院 | Medical record grouping method, device and equipment based on dynamic disease condition and storage medium |
| CN113793678A (en) * | 2021-08-31 | 2021-12-14 | 中国医学科学院北京协和医院 | Emergency classification auxiliary method and device for atypical critical patients |
| CN117009924A (en) * | 2023-10-07 | 2023-11-07 | 之江实验室 | Multi-mode self-adaptive multi-center data fusion method and system guided by electronic medical records |
| CN117009924B (en) * | 2023-10-07 | 2024-01-26 | 之江实验室 | Multi-mode self-adaptive multi-center data fusion method and system guided by electronic medical records |
| CN117558391A (en) * | 2024-01-11 | 2024-02-13 | 天津市胸科医院 | Postoperative condition deep learning method and system based on aortic dissection medical record data |
| CN117558391B (en) * | 2024-01-11 | 2024-03-12 | 天津市胸科医院 | Postoperative condition deep learning method and system based on aortic dissection medical record data |
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