CN117558392A - Electronic medical record sharing collaboration method and system - Google Patents
Electronic medical record sharing collaboration method and system Download PDFInfo
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Abstract
The invention relates to the technical field of sharing collaboration, and discloses a method and a system for sharing collaboration of electronic medical records, wherein the method comprises the following steps: collecting electronic medical record data with different data sources, and analyzing a medical record diagnosis form structure of the collected electronic medical record data; semantic content extraction is carried out on the contents of the electronic medical record data tables with different data sources; generating content topics of different table areas by using a mixed table classification strategy model; and fusing all medical record information under the same content subject to obtain the electronic medical record sharing diagnosis information for sharing collaboration. According to the method, the edge deflection direction of the rectangular area is obtained by combining with the deflection direction calculation of the pixels, the identification processing of the form frame lines in the electronic medical record data is realized, all medical record information under the same content theme is filtered and fused according to the content theme of different form areas in the acquired electronic medical record data, and the electronic medical record sharing diagnosis information representing the time sequence information change of the patient disease is obtained for sharing cooperation.
Description
Technical Field
The invention relates to the field of sharing collaboration, in particular to a method and a system for sharing collaboration of electronic medical records.
Background
With the development of medical informatization, electronic medical record systems are generally adopted by all medical institutions for patient data management and storage, and the electronic medical record data usually takes a form as a core carrier to record patient diagnosis information. However, due to the existence of information islands among medical institutions, electronic medical record systems used by different medical institutions often have the problems of poor interoperability, inconsistent data formats and the like, so that data sharing and collaboration are difficult to achieve. This not only affects the exchange of information and the collaborative work between doctors, but also limits the overall utilization of patient data and the effectiveness of health management. Aiming at the problem, the invention provides a method and a system for sharing and cooperating electronic medical records, which break the information barrier and promote the flow and cooperation of medical data.
Disclosure of Invention
In view of this, the invention provides a method for sharing and cooperating electronic medical records, which aims at: 1) Combining pixel value neighborhood changes of pixels in electronic medical record data, calculating to obtain a deflection direction of the pixels, generating a plurality of rectangular areas in the electronic medical record data, calculating to obtain an edge deflection direction of the rectangular areas in combination with the deflection direction of the pixels, realizing recognition processing of form frame lines in the electronic medical record data, dividing the electronic medical record data into a plurality of form contents, combining internal similarity and integral similarity of word segmentation results in the form contents and positions of the word segmentation results, generating position weights of the word segmentation results, realizing semantic weighting processing combined with semantic information and single-heat coding information, obtaining semantic contents corresponding to text data of the form contents, and realizing semantic content information extraction of different form areas in the electronic medical record data; 2) The method comprises the steps of combining semantic content information of a table area and occurrence frequencies of keywords of different content topics, classifying the content topics of the table contents in the table area, filtering and fusing all medical record information under the same content topics according to the content topics of the different table areas of acquired electronic medical record data from different hospitals and different departments to obtain electronic medical record sharing diagnosis information representing time sequence information change of a patient, obtaining the electronic medical record sharing diagnosis information by doctors participating in patient diagnosis, providing diagnosis suggestions, carrying out diagnosis treatment on the patient according to the diagnosis suggestions, recording main symptom information of the patient in the diagnosis treatment process to form electronic medical records for sharing diagnosis, and carrying out electronic medical record sharing diagnosis information update to realize electronic medical record information fusion and sharing collaboration of different data sources.
In order to achieve the above purpose, the invention provides a method for sharing and cooperating electronic medical records, which comprises the following steps:
s1: collecting electronic medical record data with different data sources, analyzing a medical record diagnosis table structure of the collected electronic medical record data, and extracting table contents of the electronic medical record data;
s2: semantic content extraction is carried out on the contents of the electronic medical record data tables with different data sources;
s3: generating content topics of different table areas by utilizing a mixed table classification strategy model based on semantic content extraction results, wherein the mixed table classification strategy model takes the semantic content extraction results as input and the content topics as output;
s4: and according to the content topics of different form areas, fusing all medical record information under the same content topic to obtain electronic medical record sharing diagnosis information for sharing collaboration.
As a further improvement of the present invention:
optionally, the step S1 of collecting electronic medical record data from different data sources includes:
all electronic medical record data of the same patient under different data sources are collected, wherein the data sources comprise different hospitals and different departments, the collected electronic medical record data are in the form of electronic medical record images, and the collected electronic medical record data are collected as follows:
；
Wherein:
represents the nth electronic medical record data collected, and N represents the total number of the electronic medical record data of the patient.
Optionally, in the step S1, medical record diagnosis table structure analysis is performed on the collected electronic medical record data, and table contents of the electronic medical record data are extracted, including:
analyzing the medical record diagnosis table structure of the acquired electronic medical record data, and extracting table contents of the electronic medical record data, wherein the electronic medical record dataThe analysis flow of the medical record diagnosis form structure is as follows:
s11: calculating to obtain electronic medical record dataPixels of any x-th row and y-th column +.>Is deflected in the direction of:
；
；
；
；
wherein:
representing electronic medical record data->Pixels of any x-th row and y-th column +.>Is deflected in the direction of deflection;
representing pixel +.>Deviation value in horizontal direction, +.>Representing pixel +.>A deflection value in the vertical direction;
sequentially representing pixels +>Color values at the RGB color channel;
representing pixel +.>Pixel values of (2);
s12: in the electronic medical record dataAnd calculating to obtain the edge deflection direction of the rectangular areas, wherein the calculation formula of the edge deflection direction of the kth rectangular area is as follows:
；
wherein:
represents the edge deflection direction of the generated kth rectangular region,/- >K represents the total number of rectangular areas generated;
a set of rectangular edge pixels representing the generated kth rectangular region;
representing the deflection direction of any rectangular edge pixel point r in the rectangular edge pixel point set;
if it isIf the deviation value is smaller than the preset deviation threshold value, taking the rectangular edge of the kth rectangular area as the electronic medical record dataForm frame line of (2) and electronic medical record data are +.>Dividing into M table areas;
s13: electronic medical record data obtained by recognition of OCR (optical character recognition) technologyForm content text data of each form area in which electronic medical record data +.>Form content text data of the m-th form area is +.>，/>。
Optionally, in the step S2, extracting semantic content from the electronic medical record data table content includes:
semantic content extraction is performed on the contents of the electronic medical record data table, wherein the electronic medical record dataThe semantic content extraction flow of the m-th table area is as follows:
s21: for electronic medical record dataForm content text data of the mth form area +.>Performing word segmentation processing to obtain a word segmentation result sequence:
；
wherein:
text data representing table contents->The j-th word segmentation result in (a),>text data representing table contents- >The total number of word segmentation results;
in the embodiment of the invention, a jieba word segmentation tool is adopted to segment text data of table contents;
s22: encoding and representing the word segmentation result by adopting a single-heat encoding mode, and calculating to obtain the position weight of the word segmentation result, wherein the word segmentation resultThe result of the coding representation of +.>Word segmentation result->The position weights of (a) are:
；
wherein:
representing word segmentation result->Is a position weight of (2);
representing word segmentation result->Text data->Frequency of occurrence,/->Representing word segmentation result->Electronic medical record data->Is a frequency of occurrence in the first and second embodiments;
an exponential function that is based on a natural constant;
s23: weighting the coded representation of the segmentation result in combination with the position weight, wherein the coded representationThe weighting formula of (2) is:
；
wherein:
representing coded representation results->Is a result of the weighting process;
s24: extracting electronic medical record dataSemantic content of the mth table region:
；
；
wherein:
representing electronic medical record data->Semantic content of an mth table region;
representing the weighted result +.>Semantic information of (2); in an embodiment of the present invention, in the present invention, representing the semantic coding matrix.
Optionally, generating the content topics of different table areas by using the mixed table classification policy model based on the semantic content extraction result in the step S3 includes:
Generating content topics of different form areas by utilizing a mixed form classification strategy model based on semantic content extraction results, wherein the mixed form classification strategy model takes the semantic content extraction results as input and takes the content topics as output, the content topic types comprise personal information types, main symptom information types, current medical history information types, past medical history information types and family medical history information types, and the mixed form classification strategy model comprises an input layer, a semantic information mapping layer and a classification information output layer for fusing probability information;
the input layer is used for receiving semantic content of the form area;
the semantic information mapping layer is used for carrying out probability mapping on the semantic content to obtain probability information of the semantic content in different content theme types;
the classification information output layer integrating the probability information is used for integrating the probability information of the semantic content in different content theme types and outputting the content theme classification result of the semantic content;
electronic medical record dataThe generating flow of the content theme of the m-th table area is as follows:
s31: the input layer receives the electronic medical record dataSemantic content of the mth table area +.>A word segmentation result sequence;
s32: semantic information mapping layer maps semantic content Probability mapping is carried out to obtain probability information of semantic content in different content theme types, wherein a probability mapping formula is as follows:
；
wherein:
representing semantic content +.>Probability information on the type of subject of the content of the u th kind,/->The 1 st to 5 th content theme types are, in order, a personal information type, a main symptom information type, an existing medical history information type, and a family medical history information type;
represents an L1 norm;
a common keyword set representing a subject type of the u-th content;
representing the result of single-hot coding and semantic coding processing of the keyword q;
s33: the classification information output layer integrating probability information integrates probability information of semantic content in different content topic types and outputs content topic classification results of the semantic content, wherein the semantic contentThe probability of belonging to the u-th content topic type is:
；
wherein:
representing semantic content +.>Probability of belonging to the u-th content topic type;
representation->All keywords in the word segmentation result sequenceIs a frequency of occurrence in the first and second embodiments;
selecting the content subject type with highest probability to output as the electronic medical record dataThe content subject matter classification result of the m-th table area.
Optionally, in the step S4, according to the content topics of different table areas, all medical record information under the same content topic is fused, including:
According to the content topics of different form areas in the N pieces of electronic medical record data, fusing all medical record information under the same content topic to obtain electronic medical record sharing diagnosis information, wherein the fusion flow of the medical record information is as follows:
s41: calculating cosine similarity of semantic contents of any two table areas under the same content subject;
s42: calculating to obtain the average cosine similarity between the semantic content of any form area and the semantic content of all form areas of the same content subject under different electronic data medical records;
s43: if the cosine similarity of the semantic content of any two table areas under the same content subject is higher than a preset similarity threshold, filtering the table content of the table area with higher average cosine similarity;
s44: repeating the steps S41-S44 to obtain form contents of a plurality of form areas for filtering repeated information under the same content subject, and sequencing the form contents according to the filling time sequence of electronic medical record data corresponding to the form contents to form electronic medical record sharing diagnosis information;
and carrying out sharing cooperation processing on the electronic medical record sharing diagnosis information.
Optionally, in the step S4, the sharing collaboration processing is performed on the electronic medical record sharing diagnostic information, including:
And a doctor participating in the diagnosis of the patient acquires the electronic medical record sharing diagnosis information and provides diagnosis suggestions, diagnosis treatment is carried out on the patient according to the diagnosis suggestions, the main symptom information of the patient in the diagnosis treatment process is recorded to form the electronic medical record sharing diagnosis, and the newly generated electronic medical record information is updated to the electronic medical record sharing diagnosis information according to the steps S1-S4.
In order to solve the above problems, the present invention provides an electronic medical record sharing collaboration system, which is characterized in that the system includes:
the electronic medical record analysis module is used for collecting electronic medical record data with different data sources, analyzing a medical record diagnosis table structure of the collected electronic medical record data, extracting table contents of the electronic medical record data, and extracting semantic contents of the table contents of the electronic medical record data with different data sources;
the electronic medical record content classification module is used for generating content topics of different form areas by utilizing the mixed form classification strategy model based on semantic content extraction results;
and the medical record sharing cooperation device is used for fusing all medical record information under the same content theme according to the content themes of different form areas to obtain electronic medical record sharing diagnosis information for sharing cooperation.
In order to solve the above-mentioned problems, the present invention also provides an electronic apparatus including:
a memory storing at least one instruction;
the communication interface is used for realizing the communication of the electronic equipment; and the processor executes the instructions stored in the memory to realize the electronic medical record sharing collaboration method.
In order to solve the above problems, the present invention further provides a computer readable storage medium, where at least one instruction is stored, where the at least one instruction is executed by a processor in an electronic device to implement the electronic medical record sharing collaboration method described above.
Compared with the prior art, the invention provides an electronic medical record sharing collaboration method, which has the following advantages:
firstly, the scheme provides a semantic information extraction flow of the electronic medical record, and semantic content extraction is carried out on the contents of a table of the electronic medical record data, wherein the electronic medical record dataThe semantic content extraction flow of the m-th table area is as follows: electronic medical record data->Form content text data of the mth form area +.>Performing word segmentation processing to obtain a word segmentation result sequence:
；
wherein:text data representing table contents- >The j-th word segmentation result in (a),>text data representing table contents->The total number of word segmentation results; encoding and representing the word segmentation result by adopting a single-heat encoding mode, and calculating to obtain the position weight of the word segmentation result, wherein the word segmentation result is +.>The result of the coding representation of +.>Word segmentation resultThe position weights of (a) are:
；
wherein:representing word segmentation result->Is a position weight of (2); />Representing word segmentation result->Text data->Frequency of occurrence,/->Representing word segmentation result->Electronic medical record data->Is a frequency of occurrence in the first and second embodiments; />An exponential function that is based on a natural constant; weighting the coded representation of the segmentation result in combination with the position weight, wherein the coded representation is +.>The weighting formula of (2) is:
；
wherein:representing coded representation results->Is a result of the weighting process; extracting electronic medical record data->Semantic content of the mth table region:
；
；
wherein:representing electronic medical record data->Semantic content of an mth table region; />Representing the weighted result +.>Semantic information of (2); />Representing the semantic coding matrix. According to the scheme, the pixel value neighborhood change of pixels in the electronic medical record data is combined, the deflection direction of the pixels is calculated, a plurality of rectangular areas are generated in the electronic medical record data, the edge deflection direction of the rectangular areas is calculated by combining the deflection direction of the pixels, the recognition processing of the form frame lines in the electronic medical record data is realized, the electronic medical record data is divided into a plurality of form contents, the internal similarity and the integral similarity of word segmentation results in the form contents and the positions of the word segmentation results are combined, the position weight of the word segmentation results is generated, and the combination of semantic information and independent heat is realized Semantic weighting processing of the encoded information is carried out to obtain semantic content corresponding to the table content text data, so that semantic content information extraction of different table areas in the electronic medical record data is realized.
Meanwhile, the scheme provides a content subject classification method for different form areas in the electronic medical record data, and the electronic medical record dataThe generating flow of the content theme of the m-th table area is as follows: an input layer receives electronic medical record data->Semantic content of the mth table area +.>A word segmentation result sequence; semantic information mapping layer +_semantic content->Probability mapping is carried out to obtain probability information of semantic content in different content theme types, wherein a probability mapping formula is as follows:
；
wherein:representing semantic content +.>Probability information on the type of subject of the content of the u th kind,/->The 1 st to 5 th content theme types are, in order, a personal information type, a main symptom information type, an existing medical history information type, and a family medical history information type; />Represents an L1 norm; />A common keyword set representing a subject type of the u-th content; />Representing the result of single-hot coding and semantic coding processing of the keyword q; the classification information output layer integrating probability information integrates probability information of semantic content in different content topic types and outputs content topic classification results of the semantic content, wherein the semantic content is +. >The probability of belonging to the u-th content topic type is:
；
wherein:representing semantic content +.>Probability of belonging to the u-th content topic type; />Representation->All keywords in the word segmentation result sequence +.>Is a frequency of occurrence in the first and second embodiments; selecting the content subject type with highest probability to output as electronic medical record data +.>The content subject matter classification result of the m-th table area. The scheme combines semantic content information of the table area and occurrence frequency of keywords of different content topics, classifies the content topics of the table content in the table area according to the acquired content topicsFiltering and fusing all medical record information under the same content theme from content themes of different table areas of electronic medical record data of different hospitals and different departments to obtain electronic medical record sharing diagnosis information representing time sequence information change of a patient, obtaining the electronic medical record sharing diagnosis information by doctors participating in patient diagnosis and providing diagnosis suggestions, carrying out diagnosis treatment on the patient according to the diagnosis suggestions, recording main symptom information of the patient in the diagnosis treatment process to form electronic medical records for sharing diagnosis, and updating the electronic medical record sharing diagnosis information to realize electronic medical record information fusion and sharing cooperation of different data sources.
Drawings
FIG. 1 is a schematic flow chart of a method for sharing collaboration of electronic medical records according to an embodiment of the present invention;
FIG. 2 is a functional block diagram of an electronic medical record sharing collaboration system according to an embodiment of the present invention;
in fig. 2: the system comprises a 100 electronic medical record sharing collaboration system, a 101 electronic medical record analysis module, a 102 electronic medical record content classification module and a 103 medical record sharing collaboration device;
fig. 3 is a schematic structural diagram of an electronic device for implementing the electronic medical record sharing collaboration method according to an embodiment of the present invention.
In fig. 3: 1 an electronic device, 10 a processor, 11 a memory, 12 a program, 13 a communication interface;
the achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides an electronic medical record sharing collaboration method. The execution body of the electronic medical record sharing collaboration method includes, but is not limited to, at least one of a server, a terminal and the like capable of being configured to execute the method provided by the embodiment of the application. In other words, the electronic medical record sharing collaboration method may be performed by software or hardware installed in a terminal device or a server device, where the software may be a blockchain platform. The service end includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like.
Example 1
S1: collecting electronic medical record data with different data sources, analyzing a medical record diagnosis table structure of the collected electronic medical record data, and extracting table contents of the electronic medical record data.
And S1, acquiring electronic medical record data of different data sources, wherein the electronic medical record data comprises:
all electronic medical record data of the same patient under different data sources are collected, wherein the data sources comprise different hospitals and different departments, the collected electronic medical record data are in the form of electronic medical record images, and the collected electronic medical record data are collected as follows:
；
wherein:
represents the nth electronic medical record data collected, and N represents the total number of the electronic medical record data of the patient.
In the step S1, medical record diagnosis table structure analysis is performed on the collected electronic medical record data, and table contents of the electronic medical record data are extracted, including:
analyzing the medical record diagnosis table structure of the acquired electronic medical record data, and extracting table contents of the electronic medical record data, wherein the electronic medical record dataThe analysis flow of the medical record diagnosis form structure is as follows:
s11: calculating to obtain electronic medical record dataPixels of any x-th row and y-th column +.>Is deflected in the direction of:
；
；
；
；
wherein:
Representing electronic medical record data->Pixels of any x-th row and y-th column +.>Is deflected in the direction of deflection;
representing pixel +.>Deviation value in horizontal direction, +.>Representing pixel +.>A deflection value in the vertical direction;
sequentially representing pixels +>Color values at the RGB color channel;
representing pixel +.>Pixel values of (2);
s12: in the electronic medical record dataAnd calculating to obtain the edge deflection direction of the rectangular areas, wherein the calculation formula of the edge deflection direction of the kth rectangular area is as follows:
；
wherein:
represents the edge deflection direction of the generated kth rectangular region,/->K represents the total number of rectangular areas generated;
a set of rectangular edge pixels representing the generated kth rectangular region;
representing the deflection direction of any rectangular edge pixel point r in the rectangular edge pixel point set;
if it isIf the deviation value is smaller than the preset deviation threshold value, taking the rectangular edge of the kth rectangular area as the electronic medical record dataForm frame line of (2) and electronic medical record data are +.>Dividing into M table areas;
s13: electronic medical record data obtained by recognition of OCR (optical character recognition) technologyForm content text data of each form area in which electronic medical record data +.>Form content text data of the m-th form area is +. >，/>。
S2: and extracting semantic content from the contents of the electronic medical record data tables with different data sources.
In the step S2, semantic content extraction is performed on the contents of the electronic medical record data table, and the method comprises the following steps:
semantic content extraction is performed on the contents of the electronic medical record data table, wherein the electronic medical record dataThe semantic content extraction flow of the m-th table area is as follows:
s21: for electronic medical record dataForm content text data of the mth form area +.>Performing word segmentation processing to obtain a word segmentation result sequence:
；
wherein:
text data representing table contents->The j-th word segmentation result in (a),>text data representing table contents->The total number of word segmentation results;
s22: encoding and representing the word segmentation result by adopting a single-heat encoding mode, and calculating to obtain the position weight of the word segmentation result, wherein the word segmentation resultThe result of the coding representation of +.>Word segmentation result->The position weights of (a) are:
；
wherein:
representing word segmentation result->Is a position weight of (2);
representing word segmentation result->Text data->Frequency of occurrence,/->Representing word segmentation result->Electronic medical record data->Is a frequency of occurrence in the first and second embodiments;
an exponential function that is based on a natural constant;
s23: weighting the coded representation of the segmentation result in combination with the position weight, wherein the coded representation The weighting formula of (2) is:
；
wherein:
representing coded representation results->Is a result of the weighting process;
s24: extracting electronic medical record dataSemantic content of the mth table region:
；
；
wherein:
representing electronic medical record data->Semantic content of an mth table region;
representing the weighted result +.>Semantic information of (2); />Representing the semantic coding matrix.
S3: based on the semantic content extraction result, generating content topics of different table areas by using a mixed table classification strategy model, wherein the mixed table classification strategy model takes the semantic content extraction result as input and takes the content topic as output.
In the step S3, based on the semantic content extraction result, the content topics of different table areas are generated by using the mixed table classification policy model, including:
generating content topics of different form areas by utilizing a mixed form classification strategy model based on semantic content extraction results, wherein the mixed form classification strategy model takes the semantic content extraction results as input and takes the content topics as output, the content topic types comprise personal information types, main symptom information types, current medical history information types, past medical history information types and family medical history information types, and the mixed form classification strategy model comprises an input layer, a semantic information mapping layer and a classification information output layer for fusing probability information;
The input layer is used for receiving semantic content of the form area;
the semantic information mapping layer is used for carrying out probability mapping on the semantic content to obtain probability information of the semantic content in different content theme types;
the classification information output layer integrating the probability information is used for integrating the probability information of the semantic content in different content theme types and outputting the content theme classification result of the semantic content;
electronic medical record dataThe generating flow of the content theme of the m-th table area is as follows:
s31: the input layer receives the electronic medical record dataSemantic content of the mth table area +.>A word segmentation result sequence;
s32: semantic information mapping layer maps semantic contentProbability mapping is carried out to obtain probability information of semantic content in different content theme types, wherein a probability mapping formula is as follows:
；
wherein:
representing semantic content +.>Probability information on the type of subject of the content of the u th kind,/->The 1 st to 5 th content theme types are, in order, a personal information type, a main symptom information type, an existing medical history information type, and a family medical history information type;
represents an L1 norm;
a common keyword set representing a subject type of the u-th content;
representing the result of single-hot coding and semantic coding processing of the keyword q;
S33: the classification information output layer integrating probability information integrates probability information of semantic content in different content topic types and outputs content topic classification results of the semantic content, wherein the semantic contentThe probability of belonging to the u-th content topic type is:
；
wherein:
representing semantic content +.>Probability of belonging to the u-th content topic type;
representation->All keywords in the word segmentation result sequenceIs a frequency of occurrence in the first and second embodiments;
selecting the content theme type with highest probability to performOutput as electronic medical record dataThe content subject matter classification result of the m-th table area.
S4: and according to the content topics of different form areas, fusing all medical record information under the same content topic to obtain electronic medical record sharing diagnosis information for sharing collaboration.
In the step S4, according to the content topics of different table areas, all medical record information under the same content topic is fused, including:
according to the content topics of different form areas in the N pieces of electronic medical record data, fusing all medical record information under the same content topic to obtain electronic medical record sharing diagnosis information, wherein the fusion flow of the medical record information is as follows:
s41: calculating cosine similarity of semantic contents of any two table areas under the same content subject;
S42: calculating to obtain the average cosine similarity between the semantic content of any form area and the semantic content of all form areas of the same content subject under different electronic data medical records;
s43: if the cosine similarity of the semantic content of any two table areas under the same content subject is higher than a preset similarity threshold, filtering the table content of the table area with higher average cosine similarity;
s44: repeating the steps S41-S44 to obtain form contents of a plurality of form areas for filtering repeated information under the same content subject, and sequencing the form contents according to the filling time sequence of electronic medical record data corresponding to the form contents to form electronic medical record sharing diagnosis information;
and carrying out sharing cooperation processing on the electronic medical record sharing diagnosis information.
And in the step S4, carrying out sharing cooperation processing on the electronic medical record sharing diagnosis information, wherein the sharing cooperation processing comprises the following steps:
and a doctor participating in the diagnosis of the patient acquires the electronic medical record sharing diagnosis information and provides diagnosis suggestions, diagnosis treatment is carried out on the patient according to the diagnosis suggestions, the main symptom information of the patient in the diagnosis treatment process is recorded to form the electronic medical record sharing diagnosis, and the newly generated electronic medical record information is updated to the electronic medical record sharing diagnosis information according to the steps S1-S4.
Example 2
Fig. 2 is a functional block diagram of an electronic medical record sharing collaboration system according to an embodiment of the present invention, which may implement the electronic medical record sharing collaboration method in embodiment 1.
The electronic medical record sharing collaboration system 100 of the present invention may be installed in an electronic device. According to the implemented functions, the electronic medical record sharing collaboration system may include an electronic medical record parsing module 101, an electronic medical record content classification module 102, and a medical record sharing collaboration device 103. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
The electronic medical record analysis module 101 is configured to collect electronic medical record data with different data sources, analyze a medical record diagnosis table structure of the collected electronic medical record data, extract table contents of the electronic medical record data, and extract semantic contents of the table contents of the electronic medical record data with different data sources;
the electronic medical record content classification module 102 is used for generating content topics of different form areas by utilizing the mixed form classification strategy model based on the semantic content extraction result;
The medical record sharing collaboration device 103 is configured to fuse all medical record information under the same content topic according to the content topics of different table areas, and obtain electronic medical record sharing diagnosis information for sharing collaboration.
In detail, the modules in the electronic medical record sharing collaboration system 100 in the embodiment of the present invention use the same technical means as the electronic medical record sharing collaboration method described in fig. 1, and can produce the same technical effects, which are not described herein.
Example 3
Fig. 3 is a schematic structural diagram of an electronic device for implementing the electronic medical record sharing collaboration method according to an embodiment of the present invention.
The electronic device 1 may comprise a processor 10, a memory 11, a communication interface 13 and a bus, and may further comprise a computer program, such as program 12, stored in the memory 11 and executable on the processor 10.
The memory 11 includes at least one type of readable storage medium, including flash memory, a mobile hard disk, a multimedia card, a card memory (e.g., SD or DX memory, etc.), a magnetic memory, a magnetic disk, an optical disk, etc. The memory 11 may in some embodiments be an internal storage unit of the electronic device 1, such as a removable hard disk of the electronic device 1. The memory 11 may in other embodiments also be an external storage device of the electronic device 1, such as a plug-in mobile hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) or the like, which are provided on the electronic device 1. Further, the memory 11 may also include both an internal storage unit and an external storage device of the electronic device 1. The memory 11 may be used not only for storing application software installed in the electronic device 1 and various types of data, such as codes of the program 12, but also for temporarily storing data that has been output or is to be output.
The processor 10 may be comprised of integrated circuits in some embodiments, for example, a single packaged integrated circuit, or may be comprised of multiple integrated circuits packaged with the same or different functions, including one or more central processing units (Central Processing unit, CPU), microprocessors, digital processing chips, graphics processors, combinations of various control chips, and the like. The processor 10 is a Control Unit (Control Unit) of the electronic device, connects respective components of the entire electronic device using various interfaces and lines, executes or executes programs or modules (a program 12 for realizing electronic medical record sharing collaboration, etc.) stored in the memory 11, and invokes data stored in the memory 11 to perform various functions of the electronic device 1 and process the data.
The communication interface 13 may comprise a wired interface and/or a wireless interface (e.g. WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device 1 and other electronic devices and to enable connection communication between internal components of the electronic device.
The bus may be a peripheral component interconnect standard (peripheral component interconnect, PCI) bus or an extended industry standard architecture (extended industry standard architecture, EISA) bus, among others. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory 11 and at least one processor 10 etc.
Fig. 3 shows only an electronic device with components, it being understood by a person skilled in the art that the structure shown in fig. 3 does not constitute a limitation of the electronic device 1, and may comprise fewer or more components than shown, or may combine certain components, or may be arranged in different components.
For example, although not shown, the electronic device 1 may further include a power source (such as a battery) for supplying power to each component, and preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that functions of charge management, discharge management, power consumption management, and the like are implemented through the power management device. The power supply may also include one or more of any of a direct current or alternating current power supply, recharging device, power failure detection circuit, power converter or inverter, power status indicator, etc. The electronic device 1 may further include various sensors, bluetooth modules, wi-Fi modules, etc., which will not be described herein.
The electronic device 1 may optionally further comprise a user interface, which may be a Display, an input unit, such as a Keyboard (Keyboard), or a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device 1 and for displaying a visual user interface.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
It should be noted that, the foregoing reference numerals of the embodiments of the present invention are merely for describing the embodiments, and do not represent the advantages and disadvantages of the embodiments. And the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, apparatus, article, or method 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, apparatus, article, or method. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, apparatus, article or method that comprises the element.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (8)
1. The electronic medical record sharing collaboration method is characterized by comprising the following steps of:
s1: collecting electronic medical record data with different data sources, analyzing a medical record diagnosis table structure of the collected electronic medical record data, and extracting table contents of the electronic medical record data;
s2: semantic content extraction is carried out on the contents of the electronic medical record data tables with different data sources;
s3: generating content topics of different table areas by utilizing a mixed table classification strategy model based on semantic content extraction results, wherein the mixed table classification strategy model takes the semantic content extraction results as input and the content topics as output;
s4: and according to the content topics of different form areas, fusing all medical record information under the same content topic to obtain electronic medical record sharing diagnosis information for sharing collaboration.
2. The electronic medical record sharing collaboration method as claimed in claim 1, wherein the step S1 of collecting electronic medical record data of different data sources includes:
Collecting all electronic medical record data of the same patient under different data sources, analyzing a medical record diagnosis table structure of the collected electronic medical record data, and extracting table contents of the electronic medical record data, wherein the collected electronic medical record data are in the form of electronic medical record images, and the collected electronic medical record data are collected as follows:
；
wherein:
represents the nth electronic medical record data collected, and N represents the total number of the electronic medical record data of the patient.
3. The method for sharing and collaborating electronic medical records according to claim 2, wherein in step S1, the acquired electronic medical record data is subjected to medical record diagnosis table structure analysis, and table contents of the electronic medical record data are extracted, including:
analyzing the medical record diagnosis table structure of the acquired electronic medical record data, and extracting table contents of the electronic medical record data, wherein the electronic medical record dataThe analysis flow of the medical record diagnosis form structure is as follows:
s11: calculating to obtain electronic medical record dataPixels of any x-th row and y-th column +.>Is deflected in the direction of:
；
；
；
；
wherein:
representing electronic medical record data->Pixels of any x-th row and y-th column +.>Is deflected in the direction of deflection;
representing pixel +.>Deviation value in horizontal direction, +. >Representing pixel +.>A deflection value in the vertical direction;
sequentially representing pixels +>Color values at the RGB color channel;
representing pixel +.>Pixel values of (2);
s12: in the electronic medical record dataAnd calculating to obtain the edge deflection direction of the rectangular areas, wherein the calculation formula of the edge deflection direction of the kth rectangular area is as follows:
；
wherein:
represents the edge deflection direction of the generated kth rectangular region,/->K represents the total number of rectangular areas generated;
a set of rectangular edge pixels representing the generated kth rectangular region;
representing the deflection direction of any rectangular edge pixel point r in the rectangular edge pixel point set;
if it isIf the deviation value is smaller than the preset deviation threshold value, the rectangular edge of the kth rectangular area is used as electronic medical record data +.>Form frame line of (2) and electronic medical record data are +.>Dividing into M table areas;
s13: electronic medical record data obtained by recognition of OCR (optical character recognition) technologyForm content text data of each form area in which electronic medical record data +.>Form of the mth form area inContent text data is->，/>。
4. The electronic medical record sharing collaboration method as claimed in claim 3, wherein the step S2 of extracting semantic content from the electronic medical record data table content comprises:
Semantic content extraction is performed on the contents of the electronic medical record data table, wherein the electronic medical record dataThe semantic content extraction flow of the m-th table area is as follows:
s21: for electronic medical record dataForm content text data of the mth form area +.>Performing word segmentation processing to obtain a word segmentation result sequence:
；
wherein:
text data representing table contents->The j-th word segmentation result in (a),>text data representing table contents->The total number of word segmentation results;
s22: encoding and representing the word segmentation result by adopting a single-heat encoding mode, and calculating to obtain the position weight of the word segmentation result, wherein the word segmentation resultThe result of the coding representation of +.>Word segmentation result->The position weights of (a) are:
；
wherein:
representing word segmentation result->Is a position weight of (2);
representing word segmentation result->Text data->Frequency of occurrence,/->Representing word segmentation result->Electronic medical record data->Is a frequency of occurrence in the first and second embodiments;
an exponential function that is based on a natural constant;
s23: weighting the coded representation of the segmentation result in combination with the position weight, wherein the coded representationThe weighting formula of (2) is:
；
wherein:
representing coded representation results->Is a result of the weighting process;
s24: extracting electronic medical record data Semantic content of the mth table region:
；
；
wherein:
representing electronic medical record data->Semantic content of an mth table region;
representing the weighted result +.>Semantic information of (2); />Representing the semantic coding matrix.
5. The electronic medical record sharing collaboration method as claimed in claim 1, wherein the step S3 of generating content topics for different form areas using a hybrid form classification policy model based on semantic content extraction results comprises:
generating content topics of different form areas by utilizing a mixed form classification strategy model based on semantic content extraction results, wherein the mixed form classification strategy model takes the semantic content extraction results as input and takes the content topics as output, the content topic types comprise personal information types, main symptom information types, current medical history information types, past medical history information types and family medical history information types, and the mixed form classification strategy model comprises an input layer, a semantic information mapping layer and a classification information output layer for fusing probability information;
the input layer is used for receiving semantic content of the form area;
the semantic information mapping layer is used for carrying out probability mapping on the semantic content to obtain probability information of the semantic content in different content theme types;
The classification information output layer integrating the probability information is used for integrating the probability information of the semantic content in different content theme types and outputting the content theme classification result of the semantic content;
electronic medical record dataThe generating flow of the content theme of the m-th table area is as follows:
s31: the input layer receives the electronic medical record dataSemantic content of the mth table area +.>A word segmentation result sequence;
s32: semantic information mapping layer maps semantic contentProbability mapping is carried out to obtain probability information of semantic content in different content theme types, wherein a probability mapping formula is as follows:
wherein:
representing semantic content +.>Probability information on the type of subject of the content of the u th kind,/->The 1 st to 5 th content theme types are, in order, a personal information type, a main symptom information type, an existing medical history information type, and a family medical history information type;
represents an L1 norm;
a common keyword set representing a subject type of the u-th content;
representing the result of single-hot coding and semantic coding processing of the keyword q;
s33: the classification information output layer integrating probability information integrates probability information of semantic content in different content topic types and outputs content topic classification results of the semantic content, wherein the semantic content The probability of belonging to the u-th content topic type is:
；
wherein:
representing semantic content +.>Probability of belonging to the u-th content topic type;
representation->All keywords in the word segmentation result sequence +.>Is a frequency of occurrence in the first and second embodiments;
selecting the content subject type with highest probability to output as the electronic medical record dataThe content subject matter classification result of the m-th table area.
6. The method for sharing collaboration of electronic medical records according to claim 1, wherein in step S4, all medical record information under the same content subject is fused according to the content subjects of different form areas, including:
according to the content topics of different form areas in the N pieces of electronic medical record data, fusing all medical record information under the same content topic to obtain electronic medical record sharing diagnosis information, wherein the fusion flow of the medical record information is as follows:
s41: calculating cosine similarity of semantic contents of any two table areas under the same content subject;
s42: calculating to obtain the average cosine similarity between the semantic content of any form area and the semantic content of all form areas of the same content subject under different electronic data medical records;
s43: if the cosine similarity of the semantic content of any two table areas under the same content subject is higher than a preset similarity threshold, filtering the table content of the table area with higher average cosine similarity;
S44: repeating the steps S41-S44 to obtain form contents of a plurality of form areas for filtering repeated information under the same content subject, and sequencing the form contents according to the filling time sequence of electronic medical record data corresponding to the form contents to form electronic medical record sharing diagnosis information;
and carrying out sharing cooperation processing on the electronic medical record sharing diagnosis information.
7. The method for sharing and collaborating electronic medical records according to claim 6, wherein the step S4 of sharing and collaborating the electronic medical record sharing diagnosis information includes:
and a doctor participating in the diagnosis of the patient acquires the electronic medical record sharing diagnosis information and provides diagnosis suggestions, diagnosis treatment is carried out on the patient according to the diagnosis suggestions, the main symptom information of the patient in the diagnosis treatment process is recorded to form the electronic medical record sharing diagnosis, and the newly generated electronic medical record information is updated to the electronic medical record sharing diagnosis information according to the steps S1-S4.
8. An electronic medical record sharing collaboration system, the system comprising:
the electronic medical record analysis module is used for collecting electronic medical record data with different data sources, analyzing a medical record diagnosis table structure of the collected electronic medical record data, extracting table contents of the electronic medical record data, and extracting semantic contents of the table contents of the electronic medical record data with different data sources;
The electronic medical record content classification module is used for generating content topics of different form areas by utilizing the mixed form classification strategy model based on semantic content extraction results;
the medical record sharing cooperation device is used for fusing all medical record information under the same content subject according to the content subjects of different form areas to obtain electronic medical record sharing diagnosis information for sharing cooperation so as to realize the electronic medical record sharing cooperation method as claimed in any one of claims 1-7.
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