CN112883712B - Intelligent input method and device for electronic medical record - Google Patents

Intelligent input method and device for electronic medical record Download PDF

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CN112883712B
CN112883712B CN202110162692.XA CN202110162692A CN112883712B CN 112883712 B CN112883712 B CN 112883712B CN 202110162692 A CN202110162692 A CN 202110162692A CN 112883712 B CN112883712 B CN 112883712B
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library
sentences
similarity
frequency
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CN112883712A (en
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姜志辉
杨晨
张宏斌
陈新
李康
黄涛阳
邹江冰
黄雪峰
俞庆华
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Southern Theater Command General Hospital of PLA
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/211—Syntactic parsing, e.g. based on context-free grammar [CFG] or unification grammars
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/30—Information retrieval; Database structures therefor; File system structures therefor of unstructured textual data
    • G06F16/35—Clustering; Classification
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/10—Text processing
    • G06F40/194—Calculation of difference between files
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F40/00—Handling natural language data
    • G06F40/20—Natural language analysis
    • G06F40/205—Parsing
    • G06F40/216—Parsing using statistical methods
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
    • G16H50/70—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for mining of medical data, e.g. analysing previous cases of other patients
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management

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Abstract

The invention provides an intelligent input method and device for electronic medical records, comprising the following steps: classifying the massive electronic medical record text data according to disease types; similarity calculation is carried out on sentences under each disease, and sentences are clustered according to the similarity; extracting keywords or words within the first few words of the similar sentences as sentence heads, establishing a sentence head library, taking other parts of the similar sentences except the sentence heads as sentence ends, establishing a sentence end library, counting the occurrence frequency of the similar sentence ends, and establishing a sentence frequency library; inputting specific keywords or words, determining sentence clustering categories after matching a sentence head library, calling a sentence end library and a sentence frequency library, performing sentence end complementation, and displaying a plurality of sentence ends with frequency ordering from high to low in the sentence frequency library on a display device for selection by a user; and acquiring the sentence end selected by each user and updating the sentence frequency library in real time. The medical record input device can effectively improve the medical record input efficiency of medical staff, further allow more time for treating patients and improve the medical quality.

Description

Intelligent input method and device for electronic medical record
Technical Field
The invention relates to the technical field of electronic medical records, in particular to an intelligent input method and device for electronic medical records.
Background
Electronic medical records (EMR, electronic Medical Record) are digitized patient medical records entered, stored, managed, transmitted with electronic equipment. With the development of society, the electronic medical record almost replaces the handwriting paper medical record in China at present. However, along with popularization of electronic medical records, a great amount of medical record input burden becomes a difficult problem of occupying time of a clinician, so that the time spent by the doctor in the process of treating a patient is reduced, and finally, the medical quality is influenced.
Disclosure of Invention
The invention aims to provide an intelligent input method and device for electronic medical records, which aim to solve the problem of input efficiency of the electronic medical records;
the invention provides an intelligent input method of an electronic medical record, which is characterized by comprising the following steps:
s1, classifying massive electronic medical record text data according to disease types;
s2, similarity calculation is carried out on sentences under each disease, and sentences are clustered according to the similarity;
s3, extracting keywords or words within the first few words of the similar sentences as sentence heads, establishing a sentence head library, taking other parts of the similar sentences except the sentence heads as sentence ends, establishing a sentence end library, counting the occurrence frequency of the similar sentence ends, and establishing a sentence frequency library;
s4, inputting specific keywords or words, after matching the sentence head library, determining sentence clustering categories, calling a sentence end library and a sentence frequency library, performing sentence end complementation, and displaying a plurality of sentence ends with frequency ordering from high to low in the sentence frequency library on a display device for selection by a user;
s5, obtaining the sentence end selected by the user each time, and updating the sentence frequency library in real time.
Preferably, the calculating the similarity of sentences under each disease and the clustering of sentences specifically includes:
s21, taking the 1 st sentence and the 2 nd sentence to calculate the similarity, if the similarity is more than or equal to 75%, classifying the two sentences into one type, otherwise, calculating the similarity of the 1 st sentence and the 3 rd sentence;
s22, if the 1 st sentence and the 2 nd sentence are classified into one type, the 3 rd sentence needs to be subjected to similarity calculation with the 1 st sentence and the 2 nd sentence, if the 2 similarities are more than or equal to 75%, the 3 sentences are gathered into one type, otherwise, the similarity between the 1 st sentence and the 4 th sentence and the similarity between the 2 nd sentence and the 4 th sentence are calculated, and the method is performed in a similar manner;
s23, obtaining a cluster library, and repeating S21-S22 to obtain other cluster libraries.
Preferably, the similarity calculation of sentences under each disease type includes:
and carrying out similarity calculation on sentences under each disease according to the following similarity calculation formula:
s=1-the shorter sentence becomes the number of chinese characters required to be changed in duplicate with the longer sentence/the total number of chinese characters of the longer sentence.
Preferably, the similarity calculation of sentences under each disease type includes:
and carrying out similarity calculation on sentences under each disease according to the following similarity calculation formula:
s=1-the shorter sentence becomes the number of chinese characters that need to be increased and the number of chinese characters replaced/the total number of chinese characters of the longer sentence in duplicate with the longer sentence.
The embodiment of the invention also provides an intelligent input device of the electronic medical record, which comprises the following modules:
and a classification module: the method is used for classifying massive electronic medical record text data according to disease types;
the calculation module: the sentence clustering method comprises the steps of performing similarity calculation on sentences under each disease, and clustering the sentences according to the similarity;
and (3) a library building module: extracting keywords or words within the first few words of the similar sentences as sentence heads, establishing a sentence head library, taking other parts of the similar sentences except the sentence heads as sentence ends, establishing a sentence end library, counting the occurrence frequency of the similar sentence ends, and establishing a sentence frequency library;
and (3) a complement module: inputting specific keywords or words, determining sentence clustering categories after matching a sentence head library, calling a sentence end library and a sentence frequency library, performing sentence end complementation, and displaying a plurality of sentence ends with frequency ordering from high to low in the sentence frequency library on a display device for selection by a user;
and an updating module: the method is used for acquiring the sentence end selected by each user and updating the sentence frequency library in real time.
Preferably, the computing module is specifically configured to:
taking the 1 st sentence and the 2 nd sentence to calculate the similarity, if the similarity is more than or equal to 75%, classifying the two sentences into one type, otherwise, calculating the similarity of the 1 st sentence and the 3 rd sentence;
if the 1 st sentence and the 2 nd sentence are classified into one type, the 3 rd sentence needs to be subjected to similarity calculation with the 1 st sentence and the 2 nd sentence, if the 2 similarities are more than or equal to 75%, the 3 sentences are clustered into one type, otherwise, the similarity between the 1 st sentence and the 4 th sentence and the similarity between the 2 nd sentence and the 4 th sentence are calculated, and the method is performed in an analogical way;
and obtaining a cluster library, and repeating the steps to obtain other cluster libraries.
Preferably, the computing module is specifically configured to:
and carrying out similarity calculation on sentences under each disease according to the following similarity calculation formula:
s=1-the shorter sentence becomes the number of chinese characters required to be changed in duplicate with the longer sentence/the total number of chinese characters of the longer sentence.
Preferably, the computing module is specifically configured to:
and carrying out similarity calculation on sentences under each disease according to the following similarity calculation formula:
s=1-the shorter sentence becomes the number of chinese characters that need to be increased and the number of chinese characters replaced/the total number of chinese characters of the longer sentence in duplicate with the longer sentence.
The embodiment of the invention also provides an intelligent input device of the electronic medical record, which is characterized by comprising: the system comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the computer program realizes the steps of the intelligent input method of the electronic medical record when being executed by the processor.
The embodiment of the invention also provides a computer readable storage medium, which is characterized in that the computer readable storage medium stores an information transmission implementation program, and the program is executed by a processor to implement the steps of the intelligent input method of the electronic medical record.
By adopting the embodiment of the invention, the medical record input efficiency of medical staff is effectively improved, more time is reserved for treating patients, and the medical quality is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an intelligent input method of an electronic medical record according to an embodiment of the invention;
FIG. 2 is a schematic diagram of an intelligent input completion updating method for an electronic medical record according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of an intelligent input device for electronic medical records according to an embodiment of the present invention;
fig. 4 is a schematic diagram of an intelligent input electronic device for electronic medical records according to an embodiment of the present invention.
Reference numerals illustrate: 301: a classification module; 302: a computing module; 303: a library building module; 304: a complement module; 305: updating a module; 410: a memory; 420: a processor.
Detailed Description
The technical solutions of the present invention will be clearly and completely described in connection with the embodiments, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In the description of the present invention, it should be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present 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 implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise. Furthermore, the terms "mounted," "connected," "coupled," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Method embodiment
According to an embodiment of the present invention, there is provided an intelligent input method of an electronic medical record, and fig. 1 is a flowchart of the intelligent input method of the electronic medical record according to the embodiment of the present invention, as shown in fig. 1, specifically including:
firstly, classifying massive electronic medical record text data according to disease types;
furthermore, similarity calculation is performed on sentences under each disease, wherein the calculation formula is that S=1-the shorter sentences are changed into the number of Chinese characters required to be increased and the number of Chinese characters to be replaced/the total number of Chinese characters of the longer sentences in a unified way, and the sentences are clustered, and the specific steps one to three are as follows:
step one, taking the 1 st sentence and the 2 nd sentence to calculate the similarity, if the similarity is more than or equal to 75%, classifying the two sentences into one type, otherwise, calculating the similarity of the 1 st sentence and the 3 rd sentence;
step two, if the 1 st sentence and the 2 nd sentence are classified into one type, the 3 rd sentence needs to be subjected to similarity calculation with the 1 st sentence and the 2 nd sentence, if the 2 similarities are more than or equal to 75%, the 3 sentences are classified into one type, otherwise, the similarity between the 1 st sentence and the 4 th sentence and the similarity between the 2 nd sentence and the 4 th sentence are calculated, and the method is performed in a similar manner;
and thirdly, obtaining a cluster library, and repeating the steps to obtain other cluster libraries.
Then, extracting keywords or words within the first few words of the similar sentences as sentence heads, establishing a sentence head library, taking other parts of the similar sentences except the sentence heads as sentence ends, establishing a sentence end library, counting the occurrence frequency of the similar sentence ends, and establishing a sentence frequency library;
after a user selects a hypertension disease, matching a sentence head library, determining sentence cluster types, calling a sentence tail library and a sentence frequency library, and fig. 2 is a schematic diagram of an intelligent input completion updating method of an electronic medical record according to an embodiment of the invention, as shown in fig. 2:
when a keyword 'double lung' is input, carrying out the complement of the remaining sentences after the 'double lung' is input according to the occurrence frequency of the sentence end, and displaying the unvoiced and wet rally with high frequency in a sentence frequency library on a display device; audible and wet sound-producing; smelling and wetting the more the sound; audible and wet sound-producing; breath sound is cleared for selection; then the user selects according to the actual situation;
and finally, adding the selected sentence end into a sentence frequency library for real-time updating.
By adopting the embodiment of the invention, the medical record input efficiency of medical staff is effectively improved, more time is reserved for treating patients, and the medical quality is improved.
Device embodiment 1
According to an embodiment of the present invention, an intelligent input device for an electronic medical record is provided, and fig. 3 is a schematic diagram of an intelligent input device module for an electronic medical record according to an embodiment of the present invention, as shown in fig. 3, and specifically includes:
classification module 301: the method is used for classifying massive electronic medical record text data according to disease types;
the calculation module 302: the sentence frequency library is used for carrying out similarity calculation on sentences under each disease type, clustering the sentences, counting the occurrence frequency of each sentence in each category and establishing a sentence frequency library;
the computing module is specifically used for:
taking the 1 st sentence and the 2 nd sentence to calculate the similarity, wherein the similarity calculation formula is as follows:
s=1-the shorter sentence becomes the number of chinese characters to be added and the number of chinese characters to be replaced/the total number of chinese characters of the longer sentence in duplicate with the longer sentence;
the same as the following calculation formula.
If the similarity is greater than or equal to 75%, classifying the two sentences into one type, otherwise, calculating the similarity of the 1 st sentence and the 3 rd sentence;
if the 1 st sentence and the 2 nd sentence are classified into one type, the 3 rd sentence needs to be subjected to similarity calculation with the 1 st sentence and the 2 nd sentence, if the 2 similarities are more than or equal to 75%, the 3 sentences are clustered into one type, otherwise, the similarity between the 1 st sentence and the 4 th sentence and the similarity between the 2 nd sentence and the 4 th sentence are calculated, and the method is performed in an analogical way;
and obtaining a cluster library, and repeating the processing to obtain other cluster libraries.
Library building module 303: extracting keywords or words within the first few words of the similar sentences as sentence heads, establishing a sentence head library, taking other parts of the similar sentences except the sentence heads as sentence ends, establishing a sentence end library, and establishing a sentence frequency library;
complement module 304: inputting specific keywords or words, determining sentence clustering categories after matching a sentence head library, calling a sentence end library and a sentence frequency library, performing sentence end complementation, and displaying a plurality of sentence ends with frequency ordering from high to low in the sentence frequency library on a display device for selection by a user;
the update module 305: the sentence frequency library is used for acquiring the sentence end selected by each user and updating the sentence frequency library in real time.
The embodiment of the present invention is an embodiment of a device corresponding to the embodiment of the method, and specific operations of each module may be understood by referring to descriptions of the embodiment of the method, which are not repeated herein.
Device example two
An embodiment of the present invention provides an intelligent input device for electronic medical records, as shown in fig. 4, including: the memory 410, the processor 420 and a computer program stored in the memory 410 and executable on the processor 420, wherein the computer program is executed by the processor to implement the steps of the intelligent input method for electronic medical records in the above method embodiment.
Device example III
The embodiment of the invention provides a computer readable storage medium, on which an implementation program for information transmission is stored, and the program is executed by a processor 420 to implement the steps of an intelligent input method for electronic medical records.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims (10)

1. An intelligent input method of an electronic medical record is characterized by comprising the following steps:
s1, classifying massive electronic medical record text data according to disease types;
s2, similarity calculation is carried out on sentences under each disease, and sentences are clustered according to the similarity;
s3, extracting keywords or words within the first few words of the similar sentences as sentence heads, establishing a sentence head library, taking other parts of the similar sentences except the sentence heads as sentence ends, establishing a sentence end library, counting the occurrence frequency of the similar sentence ends, and establishing a sentence frequency library;
s4, inputting specific keywords or words, after matching the sentence head library, determining sentence clustering categories, calling a sentence end library and a sentence frequency library, performing sentence end complementation, and displaying a plurality of sentence ends with frequency ordering from high to low in the sentence frequency library on a display device for selection by a user;
s5, obtaining the sentence end selected by the user each time, and updating the sentence frequency library in real time.
2. The method according to claim 1, wherein the calculating the similarity of sentences under each disease and the clustering of sentences specifically comprises:
s21, taking the 1 st sentence and the 2 nd sentence to calculate the similarity, if the similarity is more than or equal to 75%, classifying the two sentences into one type, otherwise, calculating the similarity of the 1 st sentence and the 3 rd sentence;
s22, if the 1 st sentence and the 2 nd sentence are classified into one type, the 3 rd sentence needs to be subjected to similarity calculation with the 1 st sentence and the 2 nd sentence, if the 2 similarities are more than or equal to 75%, the 3 sentences are gathered into one type, otherwise, the similarity between the 1 st sentence and the 4 th sentence and the similarity between the 2 nd sentence and the 4 th sentence are calculated, and the method is performed in a similar manner;
s23, obtaining a cluster library, and repeating S21-S22 to obtain other cluster libraries.
3. The method of claim 2, wherein the similarity calculation of sentences under each disease type comprises:
and carrying out similarity calculation on sentences under each disease according to the following similarity calculation formula:
s=1-the shorter sentence becomes the number of chinese characters required to be changed in duplicate with the longer sentence/the total number of chinese characters of the longer sentence.
4. The method of claim 2, wherein the similarity calculation of sentences under each disease type comprises:
and carrying out similarity calculation on sentences under each disease according to the following similarity calculation formula:
s=1-the shorter sentence becomes the number of chinese characters that need to be increased and the number of chinese characters replaced/the total number of chinese characters of the longer sentence in duplicate with the longer sentence.
5. An intelligent input device for electronic medical records is characterized by comprising the following modules:
and a classification module: the method is used for classifying massive electronic medical record text data according to disease types;
the calculation module: the sentence clustering method comprises the steps of performing similarity calculation on sentences under each disease, and clustering the sentences according to the similarity;
and (3) a library building module: extracting keywords or words within the first few words of the similar sentences as sentence heads, establishing a sentence head library, taking other parts of the similar sentences except the sentence heads as sentence ends, establishing a sentence end library, counting the occurrence frequency of the similar sentence ends, and establishing a sentence frequency library;
and (3) a complement module: inputting specific keywords or words, determining sentence clustering categories after matching a sentence head library, calling a sentence end library and a sentence frequency library, performing sentence end complementation, and displaying a plurality of sentence ends with frequency ordering from high to low in the sentence frequency library on a display device for selection by a user;
and an updating module: the method is used for acquiring the sentence end selected by each user and updating the sentence frequency library in real time.
6. The apparatus of claim 5, wherein the computing module is specifically configured to:
taking the 1 st sentence and the 2 nd sentence to calculate the similarity, if the similarity is more than or equal to 75%, classifying the two sentences into one type, otherwise, calculating the similarity of the 1 st sentence and the 3 rd sentence;
if the 1 st sentence and the 2 nd sentence are classified into one type, the 3 rd sentence needs to be subjected to similarity calculation with the 1 st sentence and the 2 nd sentence, if the 2 similarities are more than or equal to 75%, the 3 sentences are clustered into one type, otherwise, the similarity between the 1 st sentence and the 4 th sentence and the similarity between the 2 nd sentence and the 4 th sentence are calculated, and the method is performed in an analogical way;
and obtaining a cluster library, and repeating the processing to obtain other cluster libraries.
7. The apparatus of claim 6, wherein the computing module is specifically configured to:
and carrying out similarity calculation on sentences under each disease according to the following similarity calculation formula:
s=1-the shorter sentence becomes the number of chinese characters required to be changed in duplicate with the longer sentence/the total number of chinese characters of the longer sentence.
8. The apparatus of claim 6, wherein the computing module is specifically configured to:
and carrying out similarity calculation on sentences under each disease according to the following similarity calculation formula:
s=1-the shorter sentence becomes the number of chinese characters that need to be increased and the number of chinese characters replaced/the total number of chinese characters of the longer sentence in duplicate with the longer sentence.
9. An intelligent input device for an electronic medical record, comprising: memory, a processor and a computer program stored on the memory and executable on the processor, which when executed by the processor, performs the steps of the intelligent input method of an electronic medical record as claimed in any one of claims 1 to 4.
10. A computer-readable storage medium, wherein a program for implementing information transfer is stored on the computer-readable storage medium, and the program, when executed by a processor, implements the steps of the intelligent input method for electronic medical records according to any one of claims 1 to 4.
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CN112016279A (en) * 2020-09-04 2020-12-01 平安科技（深圳）有限公司 Electronic medical record structuring method and device, computer equipment and storage medium

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CN111259635A (en) * 2020-01-09 2020-06-09 智业软件股份有限公司 Method and system for completing and predicting medical record written text
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