CN111554405A - Intelligent data extraction and quality evaluation method for evidence-based medicine RCT - Google Patents
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Abstract
The invention discloses an intelligent data extraction and quality evaluation method of evidence-based medicine RCT, which relates to the technical field of medicine and comprises the following specific steps: s1, firstly, the computer calls the diagnosis data literature of the relevant illness state from the evidence-based medical literature base, and carries out different illness state classification arrangement on different diagnosis data literatures, and carries out manual marking on the relevant keywords appearing in the diagnosis data literature while carrying out classification arrangement. The invention calls the diagnosis data literature and clinical practice data of the related illness state from the evidence-based medical literature base and the data sharing platform through the computer, manually marks the related keywords, performs pairwise comparison and preferential judgment by using the rand () function, and limits the number of the data after preferential judgment.
Description
Technical Field
The invention relates to the technical field of medicine, in particular to an intelligent data extraction and quality evaluation method of evidence-based medicine RCT.
Background
The syndrome-following medicine means "the medicine following evidence", which is also called as empirical medicine. The core idea is that medical decision-making (i.e. patient treatment, treatment guidelines and medical policy control) should be made based on the best available clinical research basis, and it is also important to integrate with the individual clinical experience. The process of practicing evidence-based medicine includes five steps, namely the construction and method of evidence-based problems, evidence retrieval and collection, strict evaluation of evidence, application of best evidence, experience summary and aftereffect evaluation. Among these, the traditional methods rely heavily on labor-intensive manual processes in the step of rigorous evaluation of evidence. However, the existing research finds that the manual processing has the problems of high error rate and insufficient accuracy.
Chinese patent No. 201810893180.9 discloses a evidence-based medical document screening method and apparatus, which can improve the screening efficiency while ensuring the accuracy of document screening. The method comprises the following steps: s1, removing repeated documents in the evidence-based medical document data source; s2, automatically extracting document screening rules by using a pre-constructed intelligent document screening model, judging whether each document accords with the document screening rules one by one, and reserving documents which accord with the document screening rules.
In the scheme, only repeated medical documents are removed when data are extracted, but a large number of similar or similar medical documents exist in a large number of databases, and if only the repeated medical documents are removed, the similar or similar medical documents cannot achieve the purpose of rapid screening. Therefore, it is necessary to develop an intelligent data extraction and quality evaluation method for evidence-based medicine RCT to solve the above problems.
Disclosure of Invention
Technical problem to be solved
The invention aims to provide an intelligent data extraction and quality evaluation method of evidence-based medicine RCT, and aims to solve the problems that the existing evidence-based medical document screening method only removes repeated medical documents, and similar or similar medical documents cannot be screened quickly.
(II) technical scheme
In order to solve the problems that the existing evidence-based medical document screening method only removes repeated medical documents and similar or similar medical documents cannot achieve rapid screening, the invention provides the following technical scheme:
an intelligent data extraction and quality evaluation method for evidence-based medicine RCT comprises the following specific steps:
s1, firstly, calling diagnosis data documents of related disease conditions from a evidence-based medical document library through a computer, carrying out different disease condition classification arrangement on different diagnosis data documents, and carrying out manual marking on related keywords appearing in the diagnosis data documents while carrying out classification arrangement;
s2, comparing every two manually marked related keywords appearing in the step S1, comparing preferred data, and repeating the comparison process, or deleting the data until the number of the diagnosis data documents reaches a certain value;
s3, calling clinical practice data of relevant doctors in a data sharing platform formed by hospital modules through a computer, and manually marking keywords similar to the diagnosis data in the step S1;
s4, performing random pairwise comparison on the manually marked keywords appearing in the step S3, comparing the preferred data, and repeating the comparison process, otherwise, deleting the data until the number of the clinical practice data reaches a certain value;
s5, integrating the diagnosis data literature and the clinical practice data preferentially compared in the steps S2 and S4 to obtain the diagnosis data literature and the clinical practice data, simulating the treatment effect through a computer to obtain an optimal treatment scheme, and then perfectly combining the diagnosis data literature and the clinical practice data to make treatment measures for the patient according to the will and the value of the patient;
and S6, checking the condition of the treated patient regularly, recording and diagnosing the coefficient indexes of the check, and uploading the recorded and diagnosed data to a evidence-based medical literature library and a data sharing platform.
Preferably, a network server is arranged at the connecting end based on the computer, the connecting end of the network server is respectively connected with the evidence-based medical document library and the data sharing platform, the connecting end of the data sharing platform is connected with the hospital module, in addition, the connecting end of the computer is provided with an entry module, and the entry module is in wireless connection with the evidence-based medical document library and the data sharing platform through the computer and the network server.
Preferably, the hospital module is set to be a third-level hospital, a second-level hospital, a community health hospital, a maternal and child health care hospital and other medical institutions to achieve mutual data interconnection and intercommunication with the data sharing platform.
Preferably, a calling module, a comparing module, a judging and selecting module, an integrating module, a simulating module and a diagnosis and treatment scheme module are arranged in the computer, the output ends of the calling module, the judging and selecting module, the integrating module and the simulating module are sequentially connected, a keyword capturing and marking module is arranged between the calling module and the comparing module, and a deleting module is arranged between the judging and selecting module and the integrating module.
Preferably, the steps S2 and S4 also require the number of the diagnostic data literature and the clinical practice data to be limited, and the number of the diagnostic data literature and the clinical practice data to be less than or equal to five.
Preferably, the random pairwise alignment in steps S2 and S4 uses the rand () function in C language, that is:
(rand()％m)+1；
wherein m is the number of diagnostic data documents or the number of clinical practice data;
m≥5。
preferably, m is 2n, where n is an integer.
Preferably, the computer is further provided with a storage module for storing various data, and the storage module is connected with the computer through a USB interface.
(III) advantageous effects
Compared with the prior art, the invention provides an intelligent data extraction and quality evaluation method of evidence-based medicine RCT, which has the following beneficial effects: the invention utilizes a calling module in a computer to call diagnosis data documents and clinical practice data of related illness states from a evidence medical document library and a data sharing platform through a network server, manually marks the related keywords, utilizes a rand () function and a comparison module to carry out pairwise comparison and preferential judgment, selects the priority through a judgment and preferential module, deletes the diagnosis data documents or the clinical practice data with poor quality through a deletion module, integrates the diagnosis data documents or the clinical practice data with good quality through an integration module, limits the number of the data after preferential judgment, and carries out pathology simulation through a simulation module, thereby intelligently making the optimal treatment measures of patients in a diagnosis and treatment scheme module by utilizing information input by an expert through an input module, compared with the traditional method that a large amount of data is directly applied, the invention can screen similar or similar medical documents, the data extraction efficiency and the data quality are improved, and the accuracy in evidence evaluation is guaranteed.
Drawings
FIG. 1 is a schematic diagram of the intelligent data extraction and quality evaluation judgment of the present invention.
FIG. 2 is a schematic diagram of the system flow structure of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an intelligent data extraction and quality evaluation method for evidence-based medicine RCT is characterized by comprising the following specific steps:
s1, firstly, calling diagnosis data documents of related disease conditions from a evidence-based medical document library through a computer, carrying out different disease condition classification arrangement on different diagnosis data documents, and carrying out manual marking on related keywords appearing in the diagnosis data documents while carrying out classification arrangement;
s2, comparing every two related keywords marked manually in the step S1, comparing the preferred data, and repeating the comparison process, otherwise, deleting the data until the number of the diagnostic data documents is less than or equal to five,
randomly comparing every two by machine, and adopting a rand () function in the C language, namely:
(rand()％m)+1；
wherein m is the number of diagnostic data documents;
m≥5；
in addition, m is 2n, wherein n is an integer, so as to ensure that the number of the diagnostic data documents or the number m of the clinical practice data is always even, and avoid the situation that one data cannot be matched with the corresponding data for comparison because the number of the diagnostic data documents or the number of the clinical practice data is odd.
S3, calling clinical practice data of relevant doctors in a data sharing platform formed by hospital modules through a computer, and manually marking keywords similar to the diagnosis data in the step S1;
s4, comparing the manually marked keywords in step S3 randomly, repeating the comparison process after comparing the preferred data, otherwise deleting the data until the number of clinical practice data is less than or equal to five, wherein,
randomly comparing every two by machine, and adopting a rand () function in the C language, namely:
(rand()％m)+1；
wherein m is the number of clinical practice data;
m≥5；
in addition, m is 2n, wherein n is an integer, so that the number of the diagnostic data documents or the number m of the clinical practice data is always even, and the condition that one data cannot be matched with the corresponding data for comparison due to the fact that the number of the diagnostic data documents or the number of the clinical practice data is odd is avoided;
s5, integrating the diagnosis data literature and the clinical practice data preferentially compared in the steps S2 and S4 to obtain the diagnosis data literature and the clinical practice data, simulating the treatment effect through a computer to obtain an optimal treatment scheme, and then perfectly combining the diagnosis data literature and the clinical practice data to make treatment measures for the patient according to the will and the value of the patient;
and S6, checking the condition of the treated patient regularly, recording and diagnosing the coefficient indexes of the check, and uploading the recorded and diagnosed data to a evidence-based medical literature library and a data sharing platform.
Referring to fig. 2, the computer-based connection end is provided with a network server, and the connection end of the network server is respectively connected with the evidence-based medical document repository and the data sharing platform, and the connection end of the data sharing platform is connected with the hospital module, wherein the hospital module is set as a third-level hospital, a second-level hospital, a community health hospital, a women and children health care hospital and other medical institutions to realize mutual recognition with data interconnection and intercommunication of the data sharing platform, in addition, the computer connecting end is provided with an input module for inputting information, the input module is wirelessly connected with the evidence-based medical document library and the data sharing platform through a computer and a network server, through the arrangement of the input module, the diagnosis data and the clinical practice data are favorably input into a computer for judgment according to the will and the price of the patient, and the treatment measures of the patient are made by perfectly combining the diagnosis data literature and the clinical practice data.
A calling module, a comparison module, a judgment and preference module, an integration module, a simulation module and a diagnosis and treatment scheme module are arranged in a computer, the output ends of the calling module and the comparison module are sequentially connected, a keyword capturing and marking module is arranged between the calling module and the comparison module, a deletion module is arranged between the judgment and preference module and the integration module, the calling module in the computer respectively calls a diagnosis data document and clinical practice data from a evidence-based medical document library and a data sharing platform through a network server, manual marking of keywords is carried out through the keyword capturing and marking module, two-to-two comparison is carried out through the comparison module, selection of superior or inferior is carried out through the judgment and preference module, the diagnosis data document or the clinical practice data with poor quality is deleted through the deletion module, the integration module is used for integration with good quality, and pathology simulation is carried out through the simulation module, therefore, the expert can use the input module to input the information of the patient and intelligently make the optimal treatment measure of the patient in the diagnosis and treatment scheme module.
According to the embodiment of the invention, a storage module for storing various data is further arranged in the computer, and the storage module is connected with the computer through a USB interface.
According to another embodiment of the present invention, each item of data of the calling program and the calculating program in the computer depends on the existing mature programmable PLC, the operator performs corresponding program compilation according to specific application scenarios and keywords, and program compilation is a means commonly used by the existing programmer, and therefore, the present invention is not limited further.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. An intelligent data extraction and quality evaluation method for evidence-based medicine RCT is characterized by comprising the following specific steps:
s1, firstly, calling diagnosis data documents of related disease conditions from a evidence-based medical document library through a computer, carrying out different disease condition classification arrangement on different diagnosis data documents, and carrying out manual marking on related keywords appearing in the diagnosis data documents while carrying out classification arrangement;
s2, comparing every two manually marked related keywords appearing in the step S1, comparing preferred data, and repeating the comparison process, or deleting the data until the number of the diagnosis data documents reaches a certain value;
s3, calling clinical practice data of relevant doctors in a data sharing platform formed by hospital modules through a computer, and manually marking keywords similar to the diagnosis data in the step S1;
s4, performing random pairwise comparison on the manually marked keywords appearing in the step S3, comparing the preferred data, and repeating the comparison process, otherwise, deleting the data until the number of the clinical practice data reaches a certain value;
s5, integrating the diagnosis data literature and the clinical practice data preferentially compared in the steps S2 and S4 to obtain the diagnosis data literature and the clinical practice data, simulating the treatment effect through a computer to obtain an optimal treatment scheme, and then perfectly combining the diagnosis data literature and the clinical practice data to make treatment measures for the patient according to the will and the value of the patient;
and S6, checking the condition of the treated patient regularly, recording and diagnosing the coefficient indexes of the check, and uploading the recorded and diagnosed data to a evidence-based medical literature library and a data sharing platform.
2. The intelligent data extraction and quality evaluation method of evidence-based medicine (RCT) according to claim 1, wherein: the computer-based connection end is provided with a network server, the connection end of the network server is respectively connected with the evidence-based medical document library and the data sharing platform, the connection end of the data sharing platform is connected with the hospital module, in addition, the computer connection end is provided with an entry module, and the entry module is in wireless connection with the evidence-based medical document library and the data sharing platform through the computer and the network server.
3. The intelligent data extraction and quality evaluation method of evidence-based medicine (RCT) according to claim 2, wherein: the hospital module is set to be medical institutions such as a third-level hospital, a second-level hospital, a community health hospital and a maternal and child health care hospital to achieve mutual recognition with data interconnection and intercommunication of the data sharing platform.
4. The intelligent data extraction and quality evaluation method of evidence-based medicine (RCT) according to claim 2, wherein: the computer is internally provided with a calling module, a comparison module, a judgment and preference module, an integration module, a simulation module and a diagnosis and treatment scheme module, the output ends of the calling module and the comparison module are sequentially connected, a keyword capturing and marking module is arranged between the calling module and the comparison module, and a deletion module is arranged between the judgment and preference module and the integration module.
5. The intelligent data extraction and quality evaluation method of evidence-based medicine (RCT) according to claim 1, wherein: the steps S2 and S4 also require defining the number of the diagnostic data literature and the clinical practice data, and ensuring that the number of the diagnostic data literature and the clinical practice data is equal to or less than five.
6. The intelligent data extraction and quality evaluation method of evidence-based medicine (RCT) according to claim 1, wherein: the random pairwise comparison in steps S2 and S4 adopts the rand () function in C language, that is:
(rand()％m)+1；
wherein m is the number of diagnostic data documents or the number of clinical practice data;
m≥5。
7. the intelligent data extraction and quality evaluation method of evidence-based medicine (RCT) according to claim 6, wherein: and m is 2n, wherein n is an integer.
8. The intelligent data extraction and quality evaluation method of evidence-based medicine (RCT) according to claim 2, wherein: the computer is also provided with a storage module for storing various data, and the storage module is connected with the computer through a USB interface.
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Application publication date: 20200818 |