CN116069904A - Electronic medical record searching method, system, device, storage medium and product - Google Patents
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Abstract
The application discloses a method, a system, a device, a storage medium and a product for searching an electronic medical record, wherein the method for searching the electronic medical record comprises the following steps: acquiring medical record data and preprocessing the medical record data, wherein the medical record data at least comprises field data and time data; classifying and counting the preprocessed field data, clustering and counting medical data in the medical record data, and generating a first feature corresponding to the medical record data based on the classified and counted field data result and the clustered and counted medical data result; acquiring combined information, entity information and search information, and generating a second feature based on the combined information, the entity information and the search information; and ordering the searched electronic medical records according to the search statement, the time data, the first characteristic corresponding to the medical record data and the second characteristic corresponding to the entity. The method can improve the relevance and data quality of the search results.
Description
Technical Field
The present application relates generally to the field of medical treatment, and in particular, to a method, system, device, storage medium and product for searching an electronic medical record.
Background
Medical records are records of medical activities such as examination, diagnosis and treatment of occurrence, development and prognosis of diseases of patients by medical staff. The collected data is also summarized, arranged and comprehensively analyzed, and the medical health file of the patient is written according to a specified format and requirements. The medical records are not only summaries of clinical practice work, but also legal basis for exploring disease rules and handling medical disputes, and are valuable wealth of the country. The medical record has important effects on medical treatment, prevention, teaching, scientific research, hospital management and the like.
In the prior art, the problem of low correlation degree and deviation from scientific research topics exists in the search of electronic medical records, and often, a scientific research personnel is required to manually delete unwanted medical records which are not in line with each other so as to form a search result set.
Disclosure of Invention
In view of the foregoing drawbacks or shortcomings of the prior art, it is desirable to provide an electronic medical record searching method, system, apparatus, storage medium and product.
In one aspect, the present application provides a method for searching an electronic medical record, including:
acquiring medical record data and preprocessing the medical record data, wherein the medical record data at least comprises field data and time data;
classifying and counting the preprocessed field data, clustering and counting medical data in the medical record data, and generating a first feature corresponding to the medical record data based on the classified and counted field data result and the clustered and counted medical data result;
acquiring combined information, entity information and search information, and generating a second feature based on the combined information, the entity information and the search information;
and ordering the searched electronic medical records according to the search statement, the time data, the first characteristic corresponding to the medical record data and the second characteristic corresponding to the entity.
Further, obtaining medical record data and preprocessing the medical record data, wherein the medical record data at least comprises field data and time data, and the medical record data further comprises:
normalizing field data in the medical record data to obtain standard field data, wherein the field data at least comprises one or more of the following diseases, symptoms, inspection, examination, operation and medicines;
and carrying out standardized processing on the time data in the medical record data to obtain standard time data.
Preferably, before the electronic medical records are sorted according to the search statement, the time data, the first feature corresponding to the medical record data and the second feature corresponding to the entity, the method further includes:
preprocessing the search statement to obtain a standard statement;
inputting the standard statement into a pre-trained entity classification model, and outputting disease names and disease label information for semantic analysis;
and generating an entity set based on the disease name and the disease label information, wherein the entity at least comprises a classified lexicon and a medical lexicon entity.
Further, based on the disease name and the disease label information, generating an entity set, wherein the entity at least comprises a classified lexicon and a medical lexicon entity, and further comprises:
obtaining synonyms, father and son levels of the entity set;
and generating an entity expansion set based on the synonyms of the entity set and the father-son level.
Preferably, the classifying and counting the preprocessed field data, and meanwhile, the clustering and counting the medical data in the medical record data, and generating a first feature corresponding to the medical record data based on the classifying and counting result of the field data and the clustering and counting result of the medical data, further includes:
acquiring a field data statistical result and a field data weight;
generating weights corresponding to medical record data based on the field data statistical result and the field data weights;
obtaining the weight corresponding to the medical record data by carrying out clustering statistics on the medical data in the medical record data;
and obtaining a first characteristic based on the weight of the field data and the weight corresponding to the medical record data.
Preferably, the standard sentence is input to a pre-trained entity classification model, and disease name and disease label information are output for semantic analysis, and the method further includes:
and carrying out error correction and interception processing on the standard statement.
In a second aspect, the present application provides an electronic medical record search system, the system comprising:
the preprocessing module is used for acquiring medical record data and preprocessing the medical record data, wherein the medical record data at least comprises field data and time data;
the first feature acquisition module is used for carrying out classification statistics on the preprocessed field data, carrying out clustering statistics on medical data in the medical record data, and generating first features corresponding to the medical record data based on the field data classification statistics result and the medical data clustering statistics result;
the second feature acquisition module is used for acquiring the combination information, the entity information and the search information and generating a second feature based on the combination information, the entity information and the search information;
and the ordering module is used for ordering the searched electronic medical records according to the search statement, the time data, the first characteristic corresponding to the medical record data and the second characteristic corresponding to the entity.
In a third aspect, the present application provides an electronic medical record searching device, which is characterized by comprising a processor and a memory, wherein at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the electronic medical record searching method according to any one of the embodiments of the present application.
In a fourth aspect, the present application provides a non-transitory computer readable storage medium, which when executed by a processor of a mobile terminal, causes the mobile terminal to execute to implement the electronic medical record searching method according to any one of the embodiments of the present application.
In a fifth aspect, the present application provides a computer program product, which when executed by a processor of a terminal, enables the terminal to perform the electronic medical record searching method according to any one of the embodiments of the present application.
In summary, according to the electronic medical record searching method provided by the invention, the search is weighted by acquiring the first characteristic and the second characteristic, so that the relevance and the data quality of the search result are improved.
Drawings
Other features, objects and advantages of the present application will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the following drawings, in which:
fig. 1 is a flowchart of a method for searching an electronic medical record according to an embodiment of the present application;
FIG. 2 is a schematic diagram of the result of converting into standard field data according to an embodiment of the present application;
FIG. 3 is a schematic diagram of the medical record data according to the embodiment of the present application;
FIG. 4 is a schematic diagram of field data weights provided in an embodiment of the present application;
FIG. 5 is a schematic diagram of output results of the entity classification model according to an embodiment of the present application;
FIG. 6 is a schematic diagram of entity set expansion provided in an embodiment of the present application;
FIG. 7 is a block diagram of an electronic medical record search system according to an embodiment of the present application;
fig. 8 is a schematic structural diagram of an electronic medical record searching device according to an embodiment of the present application;
fig. 9 is a schematic diagram of the conversion to standard time data results provided in the embodiments of the present application.
Detailed Description
The present application is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the invention are shown in the drawings.
It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
The present application may relate to the field of medical science in general, so as to promote relevance of electronic medical record searching, and the following embodiments of the present application exemplarily illustrate an electronic medical record searching method.
Referring to fig. 1 in detail, the present application provides a method for searching an electronic medical record, including:
s101, obtaining medical record data and preprocessing the medical record data, wherein the medical record data at least comprises field data and time data.
Specifically, information of medical record data is acquired, and preprocessing is performed at the same time, wherein the preprocessing is used for standardizing field data and time data, so that searching is facilitated.
In some embodiments, medical record data is obtained and preprocessed, the medical record data including at least field data and time data, further comprising:
normalizing field data in the medical record data to obtain standard field data, wherein the field data at least comprises one or more of the following diseases, symptoms, inspection, examination, operation and medicines;
and carrying out standardized processing on the time data in the medical record data to obtain standard time data.
Specifically, six major types of data concerned in medical treatment include diseases, symptoms, inspection, examination, surgery and medicines, and of course, the field weights of the six major types are larger than those of other fields, the fields of each document are labeled in category, and the labeled fields are normalized to obtain standard field data, as shown in fig. 2. And acquiring time data in the medical record data, namely the time of treatment. For example, the current time data is: 2017/4/7:16, two years old, 11/03/2013, and after normalization, are respectively expressed as: 2019-04-07 9:16, 2017-08-01:53:09, 2013-11-03:53:09, as shown in fig. 9.
S102, carrying out classification statistics on the preprocessed field data, carrying out clustering statistics on medical data in the medical record data, and generating a first feature corresponding to the medical record data based on the field data classification statistics result and the medical data clustering statistics result.
Specifically, the first characteristic corresponding to the medical record data is obtained through the method, so that the medical record data can be circulated in the subsequent application process.
In some embodiments, classifying and counting the preprocessed field data, and simultaneously performing clustering and counting on medical data in the medical record data, generating a first feature corresponding to the medical record data based on the field data classifying and counting result and the medical data clustering and counting result, and further comprising:
acquiring a field data statistical result and a field data weight;
generating weights corresponding to medical record data based on the field data statistical result and the field data weights;
obtaining the weight corresponding to the medical record data by carrying out clustering statistics on the medical data in the medical record data;
and obtaining a first characteristic based on the weight of the field data and the weight corresponding to the medical record data.
Specifically, classification statistics is performed based on field data in the medical record data. For example, the medical record data shown in fig. 3, the statistical result is:
the first page of the medical records: 0;
first page surgery: 1 (operation name);
first page diagnosis: 2 (diagnosis name, differential diagnosis name);
medical record diagnosis: 2 (diagnosis name, differential diagnosis name)
Admission recording: 19 (Master complaint-symptoms, master complaint-disease, master complaint-test, master complaint-surgery, master complaint-drug, current medical history-symptoms, current medical history-disease, current medical history-test, current medical history-surgery, current medical history-drug, current history-symptoms, current history-disease, current history-test, current history-surgery, current history-drug, family history-disease);
first course of disease record: 4 (diagnosis plan-disease, diagnosis plan-test, diagnosis plan-check, diagnosis name);
daily course of disease records: 5 (disease name, diagnosis plan-disease, diagnosis plan-test, diagnosis plan-check, disease record-disease name);
and obtaining the weight corresponding to the medical record data according to the field data classification statistical result.
Meanwhile, the weight of each field data is calculated according to the statistical result of the field data, for example, the statistical result of each field data is N1, N2 and N3.. The calculation formula of Wn is shown as follows:
Nsum = ∑Nm(m =1,2,3...m)
Wm = Nm/Nsum + 1。
the field data weights are shown in fig. 4. And generating a first feature based on the field data statistics and the field data weights, the first feature = n1×w1+n2×w2+n3× W3..
Specifically, the medical data in the medical record data is firstly classified and analyzed, for example, the analysis result is as follows:
disease= { homepage diagnosis-diagnosis name, admission record-prior history-disease name, superior physician ward round record-disease name;
symptom= { admission record-complaint-symptom name, admission record-current history-symptom name;
drug= { order-drug name, admission record-current medical history-drug name. };
surgery= { home surgery-surgery name, surgery record-surgery name..mate;
check = { check report-check item name. };
check = { check report-check item name. };
clustering statistics is carried out on the value fields of the normalization fields of each class, and the clustering statistics is carried out on the value fields of all normalization fields of the disease class by way of illustration, wherein the statistics result is as follows:
{ coronary atherosclerotic heart disease=1000, hypertension=800, diabetes=500, myocardial infarction=100 }
Based on the statistics, the number of each disease/total number of diseases, the weight of each disease is calculated as follows:
coronary atherosclerotic heart disease = 1000/(1000+800+500+100) =0.42;
hypertension=800/(1000+800+500+100) =0.33;
diabetes=500/(1000+800+500+100) =0.21;
myocardial infarction=100/(1000+800+500+100) =0.04.
Adding a weight field to each field with data category, extracting the field value field, adding 1 to the weight if the value field has value, then matching with the statistical result of each field data of the category, adding the corresponding weight of the disease if the matching is possible, adding the weight of each field, and adding the sum to the weight corresponding to the medical record data to obtain the final first feature. The first feature is a ranked offline feature.
And S103, acquiring the combination information, the entity information and the search information, and generating a second feature based on the combination information, the entity information and the search information.
Specifically, the combination information, the entity information and the search information are acquired, the weights of the combination information and the entity information are accumulated, and the second characteristic is obtained according to the search information. The second feature is a ranked online feature. For example, for a multi-dimensional combined advanced search outputting a precise combined query, the weight is (qw 1.. qwn) and the set of entity extensions in each combination, the entities include commonly used six medical category word stock and other medical entities, the weight is (w1...wn). For full-text searching, an expanded set of entities is output, the entities including commonly used six medical classification word banks and other medical entities, with weights (w1...wn). Firstly, weighting according to the importance of each entity in the search statement to obtain a result of qw1+ & qwn +w1+ & wn, and weighting according to the weight (Xw) of the search information to obtain a second feature=qw1+ & qwn +w1+ & wn+xw.
In some embodiments, the combination information, the entity information, the search information is obtained, the second feature is generated based on the combination information, the entity information, the search information including one or more of: department information, field information, history search information.
Specifically, the second feature is determined according to department information, field information and historical search information, so that the search relevance can be improved. Department information weight is dw, domain information bw, history search information hw1+. Hwn. xw=dw+bw+hw.
S104, sorting the searched electronic medical records according to the search statement, the time data, the first characteristics corresponding to the medical record data and the second characteristics corresponding to the entity.
Specifically, the first feature and the second feature are used as influence factors, and full text is searched according to the search statement in a combined mode of searching in multiple dimensions. And searching the content according to the requirement of the user, and sorting according to the time information and the weight information of the content to obtain the content display which is more fit with the user. The higher the weight, the higher the ranking, while the older the ranking, the later, based on time information.
For example, the importance of each entity in a search term is first weighted
In some embodiments, before sorting the searched electronic medical records according to the search statement, the time data, the first feature corresponding to the medical record data, and the second feature corresponding to the entity, the method further includes:
preprocessing the search statement to obtain a standard statement;
inputting the standard statement into a pre-trained entity classification model, and outputting disease names and disease label information for semantic analysis;
and generating an entity set based on the disease name and the disease label information, wherein the entity at least comprises a classified lexicon and a medical lexicon entity.
Specifically, the search sentence is preprocessed so as to be convenient to analyze, and the search sentence is subjected to full-text search and multi-dimensional combined advanced search. The preprocessing of the search statement comprises: general analysis, full text search analysis, multi-dimensional combined advanced search analysis. The general analysis is full-half angle conversion, case-case conversion and complex conversion; nonsensical symbol removal: special symbol content such as Mars symbols, emoji emoticons, etc. is removed. The full text search analysis is to cut the search sentence with a certain length, and analyze the sentence with non-high level grammar and site and conditional splice. The multidimensional combined advanced search analysis is the analysis of wild cards, [ ], \and the like. The sentences after the general analysis, full text search analysis and multi-dimensional combination advanced search analysis are standard sentences.
Secondly, through training an entity classification model, each word vector, part-of-speech information and label information corresponding to each word vector are obtained, and then semantic analysis is carried out by using the model. The semantic analysis is used for full text searching. For example, the input content is coronary heart disease 10 years ago, recent dizziness, palpitation, dyspnea, black , syncope, epigastric discomfort, nausea and vomiting; after semantic analysis, time node information, symptom free information, and diagnostic information are output, as shown in fig. 5.
Thus, based on the disease name and the disease label information, an entity set is output, and the entity comprises a common six-size medical classification word stock and other medical entities.
In other embodiments, advanced searches are combined for multiple dimensions: and outputting accurate combination query and entity sets in each combination, wherein the entities comprise commonly used six medical classification word banks and other medical entities. For full text search: and outputting an entity set, wherein the entity comprises a common six-size medical classification word stock and other medical entities.
In some embodiments, inputting the standard sentence into a pre-trained entity classification model, outputting a disease name and disease label information for semantic analysis, further comprising:
and carrying out error correction and interception processing on the standard statement.
Specifically, the spelling of Chinese may be misspelled and error correction is required to avoid search problems.
For example, misspellings may occur due to the keyboard input and the user's own dialect, habit, and knowledge. Entity error correction is used for full text searches. First, dictionary-based entity error correction. A frequently incorrect vocabulary is constructed (e.g. "myocarditis" is written as "endocarditis") and if a wrong word is matched, the correct word is given. Second, rule-based entity error correction. And generating candidate words of the original words by using the editing distance, and matching the nearest candidate words in the medical knowledge graph data for error correction. Then, model-based entity error correction. Such as building a deep learning language model, etc. Finally, based on the spelling mode, a normal word list with consistent spelling is obtained, and error correction (such as 'coronary heart disease' (spelling: guankinbing), 'Guan Xinbing' (spelling: guankinbing)) is carried out. For multi-dimensional combined advanced search, correction is performed based on a data model for spelling errors of search fields, values corresponding to the search fields are corrected according to the types of the fields, for example: the type of value, the value needs to be converted into a value; date type, value requires date normalization.
In this embodiment, it is also necessary to intercept unreasonable characters in the standard sentence, so as to avoid breakdown of the search engine, for example, intercept special symbol contents such as Mars symbols and emoji emoticons in the standard sentence; and simultaneously deleting contents irrelevant to medical knowledge in the standard statement by utilizing semantic analysis.
In some embodiments, based on the disease name and the disease tag information, generating a set of entities including at least a categorized lexicon and a medical lexicon entity, further comprising:
obtaining synonyms, father and son levels of the entity set;
and generating an entity expansion set based on the synonyms of the entity set and the father-son level.
Specifically, the medical entity in the standard statement is expanded and supplemented through the medical knowledge graph. For example, first, a synonym of an entity set is obtained, and the synonym is expanded, so that the searching efficiency is improved. And secondly, expanding the parent-child level of the entity set. Finally, the entity set is expanded, and reasoning is performed through a reasoning mechanism of the graph database, such as: node (hypertension) relationship (symptoms) node (dizziness); node (hypertension grade 1) relationship (symptom) node (dizziness); it was thus deduced that hypertension and hypertension class 1 are similar diseases, as shown in fig. 6.
In summary, according to the electronic medical record searching method provided by the invention, the search is weighted by acquiring the first characteristic and the second characteristic, so that the relevance and the data quality of the search result are improved.
With further reference to FIG. 4, a schematic diagram of an electronic medical record search system 200 is shown, according to one embodiment of the present application.
A preprocessing module 210, configured to obtain medical record data and preprocess the medical record data, where the medical record data at least includes field data and time data;
the first feature obtaining module 220 performs classification statistics on the preprocessed field data, performs cluster statistics on medical data in the medical record data, and generates a first feature corresponding to the medical record data based on the field data classification statistics result and the medical data cluster statistics result;
a second feature obtaining module 230, configured to obtain combination information, entity information, and search information, and generate a second feature based on the combination information, the entity information, and the search information;
the sorting module 240 is configured to sort the searched electronic medical records according to the search statement, the time data, the first feature corresponding to the medical record data, and the second feature corresponding to the entity.
The division of the modules or units mentioned in the above detailed description is not mandatory. Indeed, the features and functionality of two or more modules or units described above may be embodied in one module or unit in accordance with embodiments of the present disclosure. Conversely, the features and functions of one module or unit described above may be further divided into a plurality of modules or units to be embodied.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation instructions of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, blocks shown in two separate connections may in fact be performed substantially in parallel, or they may sometimes be performed in the reverse order, depending on the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. The foregoing description is only of the preferred embodiments of the present application and is presented as a description of the principles of the technology being utilized. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this application is not limited to the specific combinations of features described above, but it is intended to cover other embodiments in which any combination of features described above or equivalents thereof is possible without departing from the spirit of the disclosure. Such as the above-described features and technical features having similar functions (but not limited to) disclosed in the present application are replaced with each other.
With further reference to fig. 8, a schematic structural diagram of an electronic medical record searching apparatus 300 according to one embodiment of the present application is shown.
The execution main body of the electronic medical record searching method in this embodiment is an electronic medical record searching device, and the electronic medical record searching device in this embodiment may be implemented in software and/or hardware, and may be configured in an electronic apparatus or in a server for controlling the electronic apparatus, where the server communicates with the electronic apparatus to control the electronic apparatus.
The electronic device in this embodiment may include, but is not limited to, a personal computer, a platform computer, a smart phone, and the like, and the embodiment is not particularly limited to the electronic device.
The electronic medical record searching device 300 of the present embodiment comprises a processor and a memory, the processor and the memory being connected to each other, wherein the memory is configured to store a computer program, the computer program comprising program instructions, the processor being configured to invoke the program instructions to perform the method according to any of the preceding claims.
In the embodiments of the present application, the processor is a processing device that performs logic operations, such as a Central Processing Unit (CPU), a field programmable logic array (FPGA), a Digital Signal Processor (DSP), a single chip Microcomputer (MCU), an application specific logic circuit (ASIC), an image processor (GPU), or the like, and has data processing capability and/or program execution capability. It will be readily appreciated that the processor is typically communicatively coupled to a memory, on which is stored any combination of one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. Volatile memory can include, for example, random Access Memory (RAM) and/or cache memory (cache) and the like. The non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), USB memory, flash memory, and the like. One or more computer instructions may be stored on the memory and executed by the processor to perform the relevant analysis functions. Various applications and various data, such as various data used and/or generated by the applications, may also be stored in the computer readable storage medium.
In this embodiment of the present application, each module may be implemented by a processor executing related computer instructions, for example, the acquisition module may be implemented by the processor executing acquired instructions, the input module may be implemented by the processor executing instructions of the rule model, and the neural network may be implemented by the processor executing instructions of the neural network algorithm.
In the embodiment of the application, each module may run on the same processor or may run on multiple processors; the modules may be run on processors of the same architecture, e.g., all on processors of the X86 system, or on processors of different architectures, e.g., the image processing module runs on the CPU of the X86 system and the machine learning module runs on the GPU. The modules may be packaged in one computer product, for example, the modules are packaged in one computer software and run in one computer (server), or may be packaged separately or partially in different computer products, for example, the image processing modules are packaged in one computer software and run in one computer (server), and the machine learning modules are packaged separately in separate computer software and run in another computer (server); the computing platform when each module executes may be local computing, cloud computing, or hybrid computing composed of local computing and cloud computing.
The computer system includes a Central Processing Unit (CPU) 301 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM) 302 or a program loaded from a storage section 308 into a Random Access Memory (RAM) 303. In the RAM303, various programs and data required for operation instructions of the system are also stored. The CPU301, ROM302, and RAM303 are connected to each other through a bus 304. An input/output (I/O) interface 305 is also connected to bus 304.
The following components are connected to the I/O interface 305; an input section 306 including a keyboard, a mouse, and the like; an output portion 307 including a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, a speaker, and the like; a storage section 308 including a hard disk or the like; and a communication section 309 including a network interface card such as a LAN card, a modem, or the like. The communication section 309 performs communication processing via a network such as the internet. The drive 310 is also connected to the I/O interface 305 as needed. A removable medium 311 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is installed on the drive 310 as needed, so that a computer program read therefrom is installed into the storage section 308 as needed.
In particular, according to embodiments of the present application, the process described above with reference to flowchart fig. 1 may be implemented as a computer software program. For example, embodiments of the present application include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method shown in the flowcharts. In such an embodiment, the computer program contains program code for performing the method shown in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network via the communication portion 309, and/or installed from the removable medium 311. The above-described functions defined in the system of the present application are performed when the computer program is executed by a Central Processing Unit (CPU) 301.
The electronic device provided by the embodiment of the application is provided with a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the method according to any one of the above.
It should be noted that the computer readable medium shown in the present application may be a computer readable signal medium or a computer readable storage medium, or any combination of the two. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples of the computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the context of this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present application, however, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, with computer-readable program code embodied therein. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
In one embodiment, a computer program product is provided, which, when executed by a processor of an electronic device, causes an electronic medical record searching apparatus to perform the steps of: acquiring medical record data and preprocessing the medical record data, wherein the medical record data at least comprises field data and time data;
classifying and counting the preprocessed field data, clustering and counting medical data in the medical record data, and generating a first feature corresponding to the medical record data based on the classified and counted field data result and the clustered and counted medical data result;
acquiring combined information, entity information and search information, and generating a second feature based on the combined information, the entity information and the search information;
and ordering the searched electronic medical records according to the search statement, the time data, the first characteristic corresponding to the medical record data and the second characteristic corresponding to the entity.
It is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are merely for convenience in describing and simplifying the description based on the orientation or positional relationship shown in the drawings, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be construed as limiting the invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. The terminology used herein is for the purpose of describing particular implementations only and is not intended to be limiting of the invention. Terms such as "disposed" or the like as used herein may refer to either one element being directly attached to another element or one element being attached to another element through an intermediate member. Features described herein in one embodiment may be applied to another embodiment alone or in combination with other features unless the features are not applicable or otherwise indicated in the other embodiment.
The present invention has been described in terms of the above embodiments, but it should be understood that the above embodiments are for purposes of illustration and description only and are not intended to limit the invention to the embodiments described. Those skilled in the art will appreciate that many variations and modifications are possible in light of the teachings of the invention, which variations and modifications are within the scope of the invention as claimed.
Claims (10)
1. The electronic medical record searching method is characterized by comprising the following steps of:
acquiring medical record data and preprocessing the medical record data, wherein the medical record data at least comprises field data and time data;
classifying and counting the preprocessed field data, clustering and counting medical data in the medical record data, and generating a first feature corresponding to the medical record data based on the classified and counted field data result and the clustered and counted medical data result;
acquiring combined information, entity information and search information, and generating a second feature based on the combined information, the entity information and the search information;
and ordering the searched electronic medical records according to the search statement, the time data, the first characteristic corresponding to the medical record data and the second characteristic corresponding to the entity.
2. The electronic medical record searching method according to claim 1, wherein medical record data is acquired and preprocessed, the medical record data at least including field data and time data, further comprising:
normalizing field data in the medical record data to obtain standard field data, wherein the field data at least comprises one or more of the following diseases, symptoms, inspection, examination, operation and medicines;
and carrying out standardized processing on the time data in the medical record data to obtain standard time data.
3. The electronic medical record searching method according to claim 1, wherein before sorting the searched electronic medical records according to the search statement, the time data, the first feature corresponding to the medical record data, and the second feature corresponding to the entity, further comprising:
preprocessing the search statement to obtain a standard statement;
inputting the standard statement into a pre-trained entity classification model, and outputting disease names and disease label information for semantic analysis;
and generating an entity set based on the disease name and the disease label information, wherein the entity at least comprises a classified lexicon and a medical lexicon entity.
4. The electronic medical record searching method according to claim 3, wherein an entity set is generated based on the disease name and the disease tag information, the entity at least including a classified lexicon and a medical lexicon entity, further comprising:
obtaining synonyms, father and son levels of the entity set;
and generating an entity expansion set based on the synonyms of the entity set and the father-son level.
5. The electronic medical record searching method according to claim 1, wherein the classifying and counting the preprocessed field data and simultaneously clustering and counting medical data in the medical record data, and generating a first feature corresponding to the medical record data based on the classifying and counting result of the field data and the clustering and counting result of the medical data, further comprises:
acquiring a field data statistical result and a field data weight;
generating weights corresponding to medical record data based on the field data statistical result and the field data weights;
obtaining the weight corresponding to the medical record data by carrying out clustering statistics on the medical data in the medical record data;
and obtaining a first characteristic based on the weight of the field data and the weight corresponding to the medical record data.
6. The electronic medical record searching method of claim 3, wherein inputting the standard sentence into a pre-trained entity classification model, outputting a disease name and disease label information for semantic analysis, further comprising:
and carrying out error correction and interception processing on the standard statement.
7. An electronic medical record search system, comprising:
the preprocessing module is used for acquiring medical record data and preprocessing the medical record data, wherein the medical record data at least comprises field data and time data;
the first feature acquisition module is used for carrying out classification statistics on the preprocessed field data, carrying out clustering statistics on medical data in the medical record data, and generating first features corresponding to the medical record data based on the field data classification statistics result and the medical data clustering statistics result;
the second feature acquisition module is used for acquiring the combination information, the entity information and the search information and generating a second feature based on the combination information, the entity information and the search information;
and the ordering module is used for ordering the searched electronic medical records according to the search statement, the time data, the first characteristic corresponding to the medical record data and the second characteristic corresponding to the entity.
8. An electronic medical record searching device, characterized by comprising a processor and a memory, wherein at least one instruction, at least one section of program, code set or instruction set is stored in the memory, and the instruction, the program, the code set or the instruction set is loaded and executed by the processor to implement the electronic medical record searching method according to any one of claims 1-6.
9. A non-transitory computer readable storage medium, characterized in that instructions in the storage medium, when executed by a processor of a mobile terminal, enable the mobile terminal to perform the electronic medical record searching method according to any one of claims 1-6.
10. A computer program product, characterized in that instructions in the computer program product, when executed by a processor of a mobile terminal, enable the mobile terminal to perform the electronic medical record searching method according to any one of claims 1-6.
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| CN109616212A (en) * | 2018-11-09 | 2019-04-12 | 金色熊猫有限公司 | Disease data processing method, device, electronic equipment and readable medium |
| CN109753516A (en) * | 2019-01-31 | 2019-05-14 | 北京嘉和美康信息技术有限公司 | A kind of sort method and relevant apparatus of case history search result |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109616212A (en) * | 2018-11-09 | 2019-04-12 | 金色熊猫有限公司 | Disease data processing method, device, electronic equipment and readable medium |
| CN109753516A (en) * | 2019-01-31 | 2019-05-14 | 北京嘉和美康信息技术有限公司 | A kind of sort method and relevant apparatus of case history search result |
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Application publication date: 20230505 |