CN110752027A - Electronic medical record data pushing method and device, computer equipment and storage medium - Google Patents
Electronic medical record data pushing method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN110752027A CN110752027A CN201911000771.XA CN201911000771A CN110752027A CN 110752027 A CN110752027 A CN 110752027A CN 201911000771 A CN201911000771 A CN 201911000771A CN 110752027 A CN110752027 A CN 110752027A
- Authority
- CN
- China
- Prior art keywords
- electronic medical
- medical record
- similar
- record set
- disease
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- 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
- G16H50/20—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for computer-aided diagnosis, e.g. based on medical expert systems
-
- 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
-
- 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
- G16H70/00—ICT specially adapted for the handling or processing of medical references
-
- 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
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
Abstract
The application relates to a method, a device, computer equipment and a storage medium for pushing the data of the electronic medical record, wherein the method comprises the following steps: the method comprises the steps of obtaining an electronic medical record set, screening the electronic medical record set to obtain a similar electronic medical record set aiming at the current electronic medical record, obtaining corresponding medical institution characteristic fields based on electronic medical record identification marks in the similar electronic medical record set, constructing a disease-treating doctor-treating institution relation model, matching the relation model with the similar electronic medical record set based on the relation model, and carrying out sorting and pushing on matching results by adopting a sorting rule of most similar sorting, difficult and complicated disease sorting or most authoritative sorting. In the whole process, the matching result of the electronic medical records is accurately obtained by learning the data in the electronic medical record set, and accurate diagnosis data support can be provided for different focuses by adopting sequencing push in three different directions of similarity, disease rarity or diagnosis authority.
Description
Technical Field
The present application relates to the field of electronic medical record technologies, and in particular, to a method and an apparatus for pushing electronic medical record data, a computer device, and a storage medium.
Background
With the development of medical technology and internet technology, electronic medical records are popularized and applied in more and more hospitals at present. Specifically, electronic medical records, also known as computerized medical record systems or computer-based patient records, are digitized medical records of patients that are stored, managed, transmitted, and reproduced electronically (computers, health cards, etc.) in place of handwritten paper medical records.
The popularization and application of the electronic medical records bring convenience to people, and researches of new technologies are derived at present based on the electronic medical records, for example, big data analysis and processing are carried out based on the electronic medical records, and related pushing of medical record data and the like are realized.
The traditional big data processing based on the electronic medical record adopts a mode of diagnosing through analysis or machine learning of data inside a system and adding some disease public data, and although the related pushing mode based on the big data can push some data of related electronic medical records to a user, the data analysis and machine learning process is directly transplanted from the big data analysis in other fields and is not optimized according to the characteristics of the electronic medical record data, so that the pushed data has different accuracy and the pushed electronic medical record data cannot provide reliable data support for diagnosis of doctors.
Disclosure of Invention
In view of the foregoing, it is desirable to provide an accurate electronic medical record data pushing method, apparatus, computer device and storage medium.
An electronic medical record data pushing method, the method comprising:
acquiring an electronic medical record set of a hospital;
screening electronic medical records similar to the current electronic medical record from the electronic medical record set to obtain a similar electronic medical record set;
identifying the identification marks of the electronic medical records in the similar electronic medical record set, acquiring the characteristic fields of the medical institution corresponding to the identification marks, and constructing a relation model of a disease-treating doctor-treating institution;
and matching the relation model with the similar electronic medical record set, and sequencing and pushing matching results according to a preset sequencing rule, wherein the preset sequencing rule comprises most similar sequencing, difficult and complicated disease sequencing or most authoritative sequencing.
In one embodiment, the acquiring the set of electronic medical records of the hospital includes:
acquiring an initial electronic medical record set of a hospital III;
and hiding or desensitizing personal field information of the initial electronic medical record set to obtain an electronic medical record set, wherein the personal field information comprises names, professions, work units, telephones and addresses.
In one embodiment, the screening electronic medical records from the electronic medical record collection that are similar to the current electronic medical record to obtain a similar electronic medical record collection includes:
and screening the electronic medical records similar to the current electronic medical record from the electronic medical record set according to the characteristic fields to obtain a similar electronic medical record set, wherein the characteristic fields comprise an age field, a symptom field and a disease name field.
In one embodiment, the screening, according to the characteristic field, electronic medical records similar to the current electronic medical record from the electronic medical record collection to obtain a similar electronic medical record collection includes:
processing the electronic medical record set by adopting a natural language processing method according to the characteristic field, and obtaining a characteristic vector of the electronic medical record in the electronic medical record set according to a preset medical symptom knowledge graph;
performing semantic analysis on the current electronic medical record, and extracting symptom fields to obtain feature vectors of the feature fields corresponding to the current electronic medical record;
and matching the characteristic vector of the electronic medical record in the electronic medical record set with the characteristic vector of the current electronic medical record to obtain a similar electronic medical record set.
In one embodiment, the processing the electronic medical record collection by using a natural language processing method according to the characteristic field, and obtaining the characteristic vector of the electronic medical record in the electronic medical record collection according to the preset medical symptom knowledge graph includes:
performing text word segmentation processing on the electronic medical record set according to the characteristic field to obtain word segmentation results;
and performing part-of-speech recognition on the word segmentation result, and obtaining a feature vector of the electronic medical record in the electronic medical record set according to the word segmentation result, the part-of-speech recognition result and a preset medical symptom knowledge map.
In one embodiment, the matching the feature vector of the electronic medical record in the electronic medical record set with the feature vector of the current electronic medical record to obtain a similar electronic medical record set includes:
calculating the Euclidean distance between the characteristic vector of the electronic medical record in the electronic medical record set and the characteristic vector of the current electronic medical record;
and obtaining a similar electronic medical record set according to the Euclidean distance.
In one embodiment, the identifying the identification of the electronic medical records in the similar electronic medical record set, obtaining the medical institution characteristic field corresponding to the identification, and constructing the relationship model of the disease-treating doctor-treating institution includes:
identifying the electronic medical record filing ID in the similar electronic medical record set, and extracting the disease name field of the electronic medical record in the similar electronic medical record set;
searching the working age, the department and the hospital name of the doctor corresponding to the filing ID;
and constructing a relation model of the disease-treating doctor-treating mechanism according to the disease name field and the searched working age of the doctor, the department and the name of the hospital.
An electronic medical record data pushing device, the device comprising:
the medical record acquisition module is used for acquiring an electronic medical record set of a hospital;
the similar screening module is used for screening the electronic medical records similar to the current electronic medical record from the electronic medical record set to obtain a similar electronic medical record set;
the identification module is used for identifying the identification of the electronic medical records in the similar electronic medical record set, acquiring the characteristic field of the medical institution corresponding to the identification, and constructing a relation model of a disease-treating doctor-treating institution;
and the matching sorting module is used for matching the relation model with the similar electronic medical record set and sorting and pushing matching results according to a preset sorting rule, wherein the preset sorting rule comprises most similar sorting, difficult and complicated disease sorting or most authoritative sorting.
A computer device comprising a memory storing a computer program and a processor implementing the steps of the method as described above when executing the computer program.
A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as described above.
According to the electronic medical record data pushing method, the electronic medical record data pushing device, the computer equipment and the storage medium, a similar electronic medical record set is obtained by screening from the electronic medical record set aiming at the current electronic medical record, the corresponding medical institution characteristic field is obtained based on the electronic medical record identity in the similar electronic medical record set, a relation model of a disease-treating doctor-treating institution is constructed, matching is carried out on the relation model and the similar electronic medical record set, and the matching result is subjected to sequencing pushing by adopting a sequencing rule of most similar sequencing, difficult and complicated disease sequencing or most authoritative sequencing. In the whole process, the matching result of the electronic medical records is accurately obtained by learning the data in the electronic medical record set, and the sequencing push in three different directions of the similarity (most similar sequencing), the disease rarity (difficult and complicated disease sequencing) or the diagnosis authority (most authority sequencing) is adopted, so that accurate diagnosis data support can be provided for different side points.
Drawings
FIG. 1 is a diagram of an application environment of a method for pushing electronic medical record data according to an embodiment;
FIG. 2 is a flowchart illustrating a method for pushing electronic medical record data according to an embodiment;
FIG. 3 is a flowchart illustrating a method for pushing electronic medical record data according to another embodiment;
FIG. 4 is a flow chart of a method for pushing electronic medical record data in an embodiment;
FIG. 5 is a block diagram showing the structure of an electronic medical record data pushing apparatus according to an embodiment;
FIG. 6 is a diagram illustrating an internal structure of a computer device according to an embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
The electronic medical record data pushing method provided by the application can be applied to the application environment shown in fig. 1. Wherein the terminal 102 communicates with the server 104 via a network. The server 104 can be specifically an electronic medical record management system server, the terminal 102 records the current electronic medical record data into the server 104, the server 104 acquires an electronic medical record set of a hospital, and selects an electronic medical record similar to the current electronic medical record from the electronic medical record set to obtain a similar electronic medical record set; identifying the identity identification of the electronic medical records in the similar electronic medical record set, acquiring the characteristic field of the medical institution corresponding to the identity identification, and constructing a relation model of a disease-treating doctor-treating institution; and matching the relational model with the similar electronic medical record set, sorting and pushing matching results according to a preset sorting rule, wherein the preset sorting rule comprises most similar sorting, difficult and complicated disease sorting or most authoritative sorting, and the terminal 102 receives sorted related data pushed by the server 104 and displays the data to a user (doctor) so as to provide data support in diagnosis. The terminal 102 may be, but not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices, and the server 104 may be implemented by an independent server or a server cluster formed by a plurality of servers.
In one embodiment, as shown in fig. 2, an electronic medical record data pushing method is provided, which is described by taking the method as an example applied to the server 104 in fig. 1, and includes the following steps:
s100: an electronic medical record set of a hospital is obtained.
The electronic medical record sets of the hospitals can be electronic medical record sets of all hospitals in a certain area, for example, electronic medical record sets of all hospitals in a certain city, even store sub-medical record sets of all hospitals in a certain province, which requires data intercommunication and sharing of electronic medical record sets of hospitals in the area, and preferably requires that electronic medical record systems of all hospitals are built based on the same architecture and have an intercommunication function. The electronic medical record data of the hospital can also be an electronic medical record set of a certain hospital historical record, such as an electronic medical record set of a city center hospital, an electronic medical record set of a provincial and civilian hospital, and the like. Because the electronic medical records relate to more privacy and sensual information, the acquired electronic medical record set needs to be subjected to personal information desensitization or hiding treatment to protect the privacy of each patient, and only data which are carried in the electronic medical records and are related to diseases are used as the basis for subsequent learning treatment.
S200: and screening the electronic medical records similar to the current electronic medical record from the electronic medical record set to obtain a similar electronic medical record set.
The current electronic medical record refers to a currently generated electronic medical record, and the server needs to find an electronic medical record similar to the current electronic medical record from the electronic medical record set obtained in step S100. Taking Zhang San of a diabetic patient as an example, a doctor diagnoses Zhang San of the patient, and records the examination and diagnosis data of Zhang San into an electronic medical record system to generate a Zhang San electronic medical record, wherein the current electronic medical record is the Zhang San electronic medical record. Furthermore, the doctor can input the examination and diagnosis data of Zhang III into the electronic medical record system at the office computer (terminal) of the doctor, and the electronic medical record system server screens the electronic medical records similar to the Zhang III electronic medical records from the electronic medical record set to obtain the Zhang III-diabetes-similar electronic medical record set.
S300: and identifying the identification marks of the electronic medical records in the similar electronic medical record set, acquiring the characteristic fields of the medical institution corresponding to the identification marks, and constructing a relation model of the disease-treating doctor-treating institution.
The identity identification mark of the electronic medical record is used for distinguishing the identity of the electronic medical record, and further the storage position of the electronic medical record can be identified. Generally, in an electronic medical record system, electronic medical records are classified and documented according to a disease name field, a diagnosis disease name, a medicine use field, a treatment use field, and the like, for example, the electronic medical records can be classified and documented in a tree-like manner, and an identification identifier of a specific electronic medical record can be a profiling ID. The medical institution characteristic field may specifically include field information of the name of the attending physician, the job title of the attending physician, the working age of the physician, the department and the hospital. After the processing, the server can obtain the disease name field carried in the electronic medical record, inquire the information related to the treating doctor and the information related to the treating organization obtained by the filing ID, and construct a relation model of the disease-treating doctor-treating organization based on the information, wherein the relation model can be simply understood as a simple corresponding relation model between the disease-treating doctor and the treating organization, namely, the disease-treating doctor-treating organization are related in the model, so that other two items of information can be obtained based on any one item of information. For example, identifying that the filing ID of the lie four electronic medical records in the similar electronic medical record set is 5678, the medical characteristic field corresponding to the filing ID5678 is wang five main treating doctors, the practitioner is 40 years, the treatment institution is the hospital of people of X province, the name of the disease carried in the lie four electronic medical records is diabetes, the relationship between the disease-treating doctor and the treatment institution constructed based on the data is diabetes-wang five-X province people hospital, it can be understood that the relationship between the disease-treating doctor and the treatment institution corresponding to each electronic medical record in the similar electronic medical record set can be obtained by repeating the above process, and based on the corresponding relationships, the corresponding relationship model is optimized, and finally the relationship model between the disease-treating doctor and the treatment institution can be obtained.
S400: and matching the relation model with the similar electronic medical record set, and sequencing and pushing the matching results according to a preset sequencing rule, wherein the preset sequencing rule comprises most similar sequencing, difficult and complicated disease sequencing or most authoritative sequencing.
The disease-treating doctor-treating mechanism relation model carries associated data of three dimensions of a disease name, a treating doctor and a treating mechanism, matching is carried out on the basis of the relation model and a similar electronic interior set, the matching process specifically comprises matching of the three dimensions of the disease name, the treating doctor and the treating mechanism, sorting pushing is carried out on matching results in a preset sorting rule mode, and the matching results need to be sorted on the basis of three-dimensional data matching results and a preset sorting rule. Specifically, the preset ordering rule comprises a most similar ordering, a difficult and complicated disease ordering or a most authoritative ordering, wherein the most similar ordering is performed by taking the disease name as a main dimension and specifically taking the similarity of symptoms and disease description as a core matching index; the difficult and complicated disease sequencing is performed by taking the disease name as a main dimension and finding related rare possible diseases as indexes based on the disease name and corresponding medical record description, and aims to prompt doctors that some symptoms are not serious but are possibly the characteristics of rare diseases and need to be noticed and eliminated by the doctors; the most authoritative ordering is the ordering from hospital level to attending physician level in the association set of similar medical records with treating physician and treating institution as main dimensions, and aims to give the current physician the diagnosis scheme given by the authoritative specialist for referring to the disease.
The electronic medical record data pushing method includes the steps of screening a similar electronic medical record set from the electronic medical record set aiming at the current electronic medical record, obtaining corresponding medical institution characteristic fields based on electronic medical record identification marks in the similar electronic medical record set, constructing a relation model of a disease-treating doctor-treating institution, matching the relation model with the similar electronic medical record set based on the relation model, and conducting sorting pushing on matching results by adopting a sorting rule of most similar sorting, difficult and complicated disease sorting or most authoritative sorting. In the whole process, the matching result of the electronic medical records is accurately obtained by learning the data in the electronic medical record set, and the sequencing push in three different directions of the similarity (most similar sequencing), the disease rarity (difficult and complicated disease sequencing) or the diagnosis authority (most authority sequencing) is adopted, so that accurate diagnosis data support can be provided for different side points.
As shown in fig. 3, in one embodiment, step S100 includes:
s120: an initial set of electronic medical records for a hospital, three, is obtained.
S140: and hiding or desensitizing personal field information of the initial electronic medical record set to obtain the electronic medical record set, wherein the personal field information comprises name, occupation, work unit, telephone and address.
The third hospital is a medical institution level classified according to the regulations of the current "hospital hierarchical management method" and the like in China, and is the highest level in the classification levels of "three levels, six levels and the like" for hospitals in China. The main items of the three hospitals reporting and assessing include medical service and management, medical quality and safety, technical level and efficiency. Generally, doctors in the third hospital have more medical records and relatively more authoritative and accurate diagnosis results, so that in order to ensure the accuracy and completeness of original data, an initial electronic medical record set of the third hospital is selected and acquired. In addition, since the electronic medical record carries more significant personal information, in order to protect the privacy of the user (patient), the personal field information is required to be hidden or desensitized, and the personal field information mainly includes the aspects of name, occupation, work unit, telephone, address, and the like. For example, the following information, name, is originally recorded in a certain electronic medical record: three kings and one occupation: subway driver, work unit: the hiding treatment is to hide all the names, the professions and the working units directly; the desensitization treatment may be performed by replacing specific characters with general symbols, for example, after the desensitization treatment, the name: king XX, occupation: XX drivers, work units: group XX. In the embodiment, on one hand, the electronic medical record of the hospital three is selected as an original data set, so that the original data is more authoritative, comprehensive and accurate; on the other hand, the obvious personal information carried in the electronic medical record is hidden or desensitized, so that the individual privacy of the patient is protected.
In one embodiment, the step of screening the electronic medical records similar to the current electronic medical record from the electronic medical record set to obtain a similar electronic medical record set comprises the steps of: and screening the electronic medical records similar to the current electronic medical record from the electronic medical record set according to the characteristic fields to obtain a similar electronic medical record set, wherein the characteristic fields comprise an age field, a symptom field and a disease name field.
The characteristic field comprises an age field, a symptom field and a disease name field, wherein the age field specifically refers to the age, the symptom field is used for describing the disease symptoms and clinical manifestations of the patient, and the disease name field specifically refers to the disease name recorded in the diagnosis result. Taking Zhang III as an example, the characteristic field comprises an age field of 45 years, a symptom field of emaciation, large food intake, high urine sugar content and the like, and a disease name field of diabetes. And according to the characteristic fields, screening the electronic medical records similar to the previous electronic medical records in the electronic medical record set, wherein the electronic medical records are similar to the previous electronic medical records. Specifically, the method can be applied to screening from three index levels of an age field, a symptom field and a disease name field, for example, selecting electronic medical records with similar ages, similar symptoms and the same or similar or associated disease names as subsets in a set of similar electronic medical records.
In one embodiment, the screening, according to the characteristic field, electronic medical records similar to the current electronic medical record from the electronic medical record set to obtain a similar electronic medical record set includes:
processing the electronic medical record set by adopting a natural language processing method according to the characteristic field, and obtaining a characteristic vector of the electronic medical record in the electronic medical record set according to a preset medical symptom knowledge graph; performing semantic analysis on the current electronic medical record, and extracting symptom fields to obtain feature vectors of the feature fields corresponding to the current electronic medical record; and matching the characteristic vector of the electronic medical record in the electronic medical record set with the characteristic vector of the current electronic medical record to obtain a similar electronic medical record set.
Natural language processing is an important direction in the fields of computer science and artificial intelligence. It studies various theories and methods that enable efficient communication between humans and computers using natural language. Natural language processing is a science integrating linguistics, computer science and mathematics. Therefore, the research in this field will relate to natural language, i.e. the language that people use everyday, so it is closely related to the research of linguistics, but has important difference. The preset medical symptom knowledge map is a pre-constructed indication map, and the knowledge map can directly adopt the existing medical symptom knowledge map. Specifically, the natural language processing method comprises text word segmentation and other processing, a feature vector is obtained based on data processed by the natural language processing method and a preset medical symptom knowledge map, the electronic medical record in the electronic medical record set is processed, on the other hand, semantic analysis is firstly carried out on the electronic medical record, a symptom field is extracted based on a semantic word segmentation result, the feature vector of the current electronic medical record is obtained according to the extracted symptom field and a feature field corresponding to the current electronic medical record and comprising an age field, a symptom field and a disease name field, and a similar electronic medical record set is constructed by matching the two feature vectors. In addition, the extracted symptom field can be specifically an extracted significant symptom field, and when a plurality of significant symptom fields exist, the significant symptom fields are sorted according to a medical symptom training model to obtain a feature vector of the feature field of the current electronic medical record.
Further, the Euclidean distance between the feature vector of the electronic medical record in the electronic medical record set and the feature vector of the current electronic medical record can be calculated; and obtaining a similar electronic medical record set according to the Euclidean distance. Specifically, different medical record sets are constructed according to the Euclidean distance, and a set with a close distance is selected as a similar medical record set.
In one embodiment, the processing the electronic medical record set by using a natural language processing method according to the characteristic field, and obtaining the characteristic vector of the electronic medical record in the electronic medical record set according to the preset medical symptom knowledge graph comprises:
performing text word segmentation processing on the electronic medical record set according to the characteristic field to obtain word segmentation results; and performing part-of-speech recognition on the word segmentation result, and obtaining the feature vector of the electronic medical record in the electronic medical record set according to the word segmentation result, the part-of-speech recognition result and a preset medical symptom knowledge map.
And performing text word segmentation processing on the electronic medical record according to the characteristic field, performing part-of-speech recognition and part-of-speech tagging on the word segmentation result, and obtaining a characteristic vector according to the word segmentation result, the part-of-speech recognition result and the medical symptom knowledge map. Taking the electronic medical record of the Wang five in the electronic medical record set as an example, performing word segmentation processing on the electronic medical record of the Wang five according to the characteristic fields to obtain age field word segmentation of the Wang five, symptom field word segmentation of the Wang five, chest distress and dizziness, disease name field word segmentation of the Wang five and coronary heart disease, performing part-of-speech recognition part-speech including name, verb, quantifier and the like on a word segmentation result, obtaining the characteristic vector of the Wang five by means of a preset medical symptom knowledge graph according to the word segmentation result and the part-of-speech recognition result of the Wang five, and repeating the above processes aiming at the electronic medical records of other people in the electronic medical record set to obtain the characteristic vector of the electronic medical record in the electronic medical record set.
In one embodiment, identifying the identification identifiers of the electronic medical records in the similar electronic medical record set, obtaining the characteristic fields of the medical institution corresponding to the identification identifiers, and constructing the relationship model of the disease-treating doctor-treating institution comprises:
identifying the electronic medical record filing ID in the similar electronic medical record set, and extracting the disease name field of the electronic medical record in the similar electronic medical record set; searching the working age, the department and the hospital name of the doctor corresponding to the filing ID; and constructing a relation model of the disease-treating doctor-treating mechanism according to the disease name field and the searched working age of the doctor, the department and the name of the hospital.
Specifically, the relation model of the disease-treating doctor-treating institution carries the associated data of the three dimensions of the disease name, the treating doctor and the treating institution, and the construction of the relation model can obtain the data of the working age of the doctor, the department and the hospital name based on the electronic medical record filing ID by the filing ID, and then the relation model of the three dimensions of the disease name, the treating doctor and the treating institution is constructed by combining the disease name field obtained before.
In order to further explain the technical solution of the method for pushing electronic medical record data in detail, a specific application example will be adopted below, and is described in detail with reference to fig. 4. In one application example, the electronic medical record data pushing method comprises the following steps:
step S1, an electronic medical record set A of the electronic medical records of the hospital is obtained, wherein the electronic medical records have the obvious personal information fields of name, occupation, work unit, telephone, address and the like which are hidden or desensitized.
Step S2, screening medical records similar to the medical record, and searching for similar electronic medical record sets obtained by matching according to the fields including but not limited to age field, symptom field and disease name field. The step S2 specifically includes the following substeps S21 to S24.
Step S21, processing the age field, symptom field and disease name field in the electronic medical record set A of Hospital by natural language processing method, for example, segmenting the text, labeling the segmentation result by part of speech, and establishing the feature vector according to the segmentation, part of speech and medical symptom knowledge map.
And step S22, performing semantic analysis on the current electronic medical record, extracting representative significant symptom fields, sorting according to the medical symptom training model if a plurality of symptoms exist, and establishing the age, symptom and disease name feature vectors of the current electronic medical record.
Step S23, calculating the Euclidean distance between the feature vector obtained from the step S21 of the electronic medical record set A of the hospital and the feature vector obtained from the step S22 of the age, symptom and disease name of the current electronic medical record.
And step S24, constructing different medical record sets according to the Euclidean distance, wherein the sets with similar distances are used as a similar medical record set.
And step S3, identifying the filing ID of the similar electronic medical record, checking the working age, the department where the doctor is located, the hospital name and the constructed model of the doctor with the ID, namely, determining the information of the doctor and the hospital which can treat the diseases related to the medical record, and establishing the relationship model of the disease-treating doctor-treating institution.
And step S4, matching the relation model constructed in the step S3 with each electronic medical record in the similar electronic medical record set in the step S2 to form a recommendation sequencing result of the medical records.
Step S5, during sorting, three sort results are produced: the most similar sorting, the difficult and complicated sorting and the most authoritative sorting are provided for doctors of the current electronic medical records to check so as to obtain high-quality medical experience and reduce misdiagnosis accidents caused by lack of experience of the doctors in small hospitals. The following three sorting tables are finally generated by the 3 sorting modes: recommending a ranking list based on the symptom and the disease similarity of the similarity; a list is sorted based on rare miscellaneous disease (rare disease) special cases; and (4) sorting the list of the diseases based on the authority according to the professional authority of the hospital and the doctor.
It should be understood that although the various steps in the flow charts of fig. 2-4 are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and described, and may be performed in other orders, unless explicitly stated otherwise. Moreover, at least some of the steps in fig. 2-4 may include multiple sub-steps or multiple stages that are not necessarily performed at the same time, but may be performed at different times, and the order of performance of the sub-steps or stages is not necessarily sequential, but may be performed in turn or alternating with other steps or at least some of the sub-steps or stages of other steps.
In addition, as shown in fig. 5, the present application further provides an electronic medical record data pushing apparatus, which includes:
a medical record acquisition module 100, configured to acquire an electronic medical record set of a hospital;
the similar screening module 200 is used for screening electronic medical records similar to the current electronic medical record from the electronic medical record set to obtain a similar electronic medical record set;
the identification module 300 is used for identifying the identification identifiers of the electronic medical records in the similar electronic medical record set, acquiring the characteristic fields of the medical institution corresponding to the identification identifiers, and constructing a relation model of a disease-treating doctor-treating institution;
and the matching sorting module 400 is used for matching the relationship model with the similar electronic medical record set and sorting and pushing the matching results according to a preset sorting rule, wherein the preset sorting rule comprises most similar sorting, difficult and complicated disease sorting or most authoritative sorting.
The electronic medical record data pushing device screens similar electronic medical record sets from the electronic medical record sets aiming at the current electronic medical record, acquires corresponding medical institution characteristic fields based on the electronic medical record identification marks in the similar electronic medical record sets, constructs a relation model of a disease-treating doctor-treating institution, matches the similar electronic medical record sets based on the relation model, and conducts sorting and pushing on matching results by adopting sorting rules of most similar sorting, difficult and complicated disease sorting or most authoritative sorting. In the whole process, the matching result of the electronic medical records is accurately obtained by learning the data in the electronic medical record set, and the sequencing push in three different directions of the similarity (most similar sequencing), the disease rarity (difficult and complicated disease sequencing) or the diagnosis authority (most authority sequencing) is adopted, so that accurate diagnosis data support can be provided for different side points.
In one embodiment, the medical record acquiring module 100 is further configured to acquire an initial set of electronic medical records of a hospital; and hiding or desensitizing personal field information of the initial electronic medical record set to obtain the electronic medical record set, wherein the personal field information comprises name, occupation, work unit, telephone and address.
In one embodiment, the similarity screening module 200 is further configured to screen an electronic medical record similar to the current electronic medical record from the electronic medical record set according to the characteristic fields, so as to obtain a similar electronic medical record set, where the characteristic fields include an age field, a symptom field, and a disease name field.
In one embodiment, the similarity screening module 200 is further configured to process the electronic medical record set by using a natural language processing method according to the characteristic field, and obtain a characteristic vector of the electronic medical record in the electronic medical record set according to a preset medical symptom knowledge graph; performing semantic analysis on the current electronic medical record, and extracting symptom fields to obtain feature vectors of the feature fields corresponding to the current electronic medical record; and matching the characteristic vector of the electronic medical record in the electronic medical record set with the characteristic vector of the current electronic medical record to obtain a similar electronic medical record set.
In one embodiment, the similarity screening module 200 is further configured to perform text word segmentation processing on the electronic medical record set according to the characteristic field to obtain a word segmentation result; and performing part-of-speech recognition on the word segmentation result, and obtaining the feature vector of the electronic medical record in the electronic medical record set according to the word segmentation result, the part-of-speech recognition result and a preset medical symptom knowledge map.
In one embodiment, the similarity screening module 200 is further configured to calculate an euclidean distance between a feature vector of an electronic medical record in the electronic medical record set and a feature vector of a current electronic medical record; and obtaining a similar electronic medical record set according to the Euclidean distance.
In one embodiment, the identification module 300 is further configured to identify the electronic medical record filing IDs in the similar electronic medical record sets, and extract the disease name fields of the electronic medical records in the similar electronic medical record sets; searching the working age, the department and the hospital name of the doctor corresponding to the filing ID; and constructing a relation model of the disease-treating doctor-treating mechanism according to the disease name field and the searched working age of the doctor, the department and the name of the hospital.
For specific limitations of the electronic medical record data pushing device, reference may be made to the above limitations on the electronic medical record data pushing method, which is not described herein again. All or part of the modules in the electronic medical record data pushing device can be realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as shown in fig. 6. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer equipment is used for storing data such as hospital electronic medical records in historical records. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to realize an electronic medical record data pushing method.
Those skilled in the art will appreciate that the architecture shown in fig. 6 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In one embodiment, a computer device is provided, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the following steps when executing the computer program:
acquiring an electronic medical record set of a hospital;
screening electronic medical records similar to the current electronic medical record from the electronic medical record set to obtain a similar electronic medical record set;
identifying the identity identification of the electronic medical records in the similar electronic medical record set, acquiring the characteristic field of the medical institution corresponding to the identity identification, and constructing a relation model of a disease-treating doctor-treating institution;
and matching the relation model with the similar electronic medical record set, and sequencing and pushing the matching results according to a preset sequencing rule, wherein the preset sequencing rule comprises most similar sequencing, difficult and complicated disease sequencing or most authoritative sequencing.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
acquiring an initial electronic medical record set of a hospital III; and hiding or desensitizing personal field information of the initial electronic medical record set to obtain the electronic medical record set, wherein the personal field information comprises name, occupation, work unit, telephone and address.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
and screening the electronic medical records similar to the current electronic medical record from the electronic medical record set according to the characteristic fields to obtain a similar electronic medical record set, wherein the characteristic fields comprise an age field, a symptom field and a disease name field.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
processing the electronic medical record set by adopting a natural language processing method according to the characteristic field, and obtaining a characteristic vector of the electronic medical record in the electronic medical record set according to a preset medical symptom knowledge graph; performing semantic analysis on the current electronic medical record, and extracting symptom fields to obtain feature vectors of the feature fields corresponding to the current electronic medical record; and matching the characteristic vector of the electronic medical record in the electronic medical record set with the characteristic vector of the current electronic medical record to obtain a similar electronic medical record set.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
performing text word segmentation processing on the electronic medical record set according to the characteristic field to obtain word segmentation results; and performing part-of-speech recognition on the word segmentation result, and obtaining the feature vector of the electronic medical record in the electronic medical record set according to the word segmentation result, the part-of-speech recognition result and a preset medical symptom knowledge map.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
calculating the Euclidean distance between the characteristic vector of the electronic medical record in the electronic medical record set and the characteristic vector of the current electronic medical record; and obtaining a similar electronic medical record set according to the Euclidean distance.
In one embodiment, the processor, when executing the computer program, further performs the steps of:
identifying the electronic medical record filing ID in the similar electronic medical record set, and extracting the disease name field of the electronic medical record in the similar electronic medical record set; searching the working age, the department and the hospital name of the doctor corresponding to the filing ID; and constructing a relation model of the disease-treating doctor-treating mechanism according to the disease name field and the searched working age of the doctor, the department and the name of the hospital.
In one embodiment, a computer-readable storage medium is provided, having a computer program stored thereon, which when executed by a processor, performs the steps of:
acquiring an electronic medical record set of a hospital;
screening electronic medical records similar to the current electronic medical record from the electronic medical record set to obtain a similar electronic medical record set;
identifying the identity identification of the electronic medical records in the similar electronic medical record set, acquiring the characteristic field of the medical institution corresponding to the identity identification, and constructing a relation model of a disease-treating doctor-treating institution;
and matching the relation model with the similar electronic medical record set, and sequencing and pushing the matching results according to a preset sequencing rule, wherein the preset sequencing rule comprises most similar sequencing, difficult and complicated disease sequencing or most authoritative sequencing.
In one embodiment, the computer program when executed by the processor further performs the steps of:
acquiring an initial electronic medical record set of a hospital III; and hiding or desensitizing personal field information of the initial electronic medical record set to obtain the electronic medical record set, wherein the personal field information comprises name, occupation, work unit, telephone and address.
In one embodiment, the computer program when executed by the processor further performs the steps of:
and screening the electronic medical records similar to the current electronic medical record from the electronic medical record set according to the characteristic fields to obtain a similar electronic medical record set, wherein the characteristic fields comprise an age field, a symptom field and a disease name field.
In one embodiment, the computer program when executed by the processor further performs the steps of:
processing the electronic medical record set by adopting a natural language processing method according to the characteristic field, and obtaining a characteristic vector of the electronic medical record in the electronic medical record set according to a preset medical symptom knowledge graph; performing semantic analysis on the current electronic medical record, and extracting symptom fields to obtain feature vectors of the feature fields corresponding to the current electronic medical record; and matching the characteristic vector of the electronic medical record in the electronic medical record set with the characteristic vector of the current electronic medical record to obtain a similar electronic medical record set.
In one embodiment, the computer program when executed by the processor further performs the steps of:
performing text word segmentation processing on the electronic medical record set according to the characteristic field to obtain word segmentation results; and performing part-of-speech recognition on the word segmentation result, and obtaining the feature vector of the electronic medical record in the electronic medical record set according to the word segmentation result, the part-of-speech recognition result and a preset medical symptom knowledge map.
In one embodiment, the computer program when executed by the processor further performs the steps of:
calculating the Euclidean distance between the characteristic vector of the electronic medical record in the electronic medical record set and the characteristic vector of the current electronic medical record; and obtaining a similar electronic medical record set according to the Euclidean distance.
In one embodiment, the computer program when executed by the processor further performs the steps of:
identifying the electronic medical record filing ID in the similar electronic medical record set, and extracting the disease name field of the electronic medical record in the similar electronic medical record set; searching the working age, the department and the hospital name of the doctor corresponding to the filing ID; and constructing a relation model of the disease-treating doctor-treating mechanism according to the disease name field and the searched working age of the doctor, the department and the name of the hospital.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by hardware instructions of a computer program, which can be stored in a non-volatile computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), Programmable ROM (PROM), Electrically Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus Direct RAM (RDRAM), direct bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).
The technical features of the above embodiments can be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the above embodiments are not described, but should be considered as the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. An electronic medical record data pushing method, the method comprising:
acquiring an electronic medical record set of a hospital;
screening electronic medical records similar to the current electronic medical record from the electronic medical record set to obtain a similar electronic medical record set;
identifying the identification marks of the electronic medical records in the similar electronic medical record set, acquiring the characteristic fields of the medical institution corresponding to the identification marks, and constructing a relation model of a disease-treating doctor-treating institution;
and matching the relation model with the similar electronic medical record set, and sequencing and pushing matching results according to a preset sequencing rule, wherein the preset sequencing rule comprises most similar sequencing, difficult and complicated disease sequencing or most authoritative sequencing.
2. The method of claim 1, wherein the acquiring a set of electronic medical records for a hospital comprises:
acquiring an initial electronic medical record set of a hospital III;
and hiding or desensitizing personal field information of the initial electronic medical record set to obtain an electronic medical record set, wherein the personal field information comprises names, professions, work units, telephones and addresses.
3. The method of claim 1, wherein the filtering the electronic medical records from the electronic medical record collection that are similar to the current electronic medical record to obtain a similar electronic medical record collection comprises:
and screening the electronic medical records similar to the current electronic medical record from the electronic medical record set according to the characteristic fields to obtain a similar electronic medical record set, wherein the characteristic fields comprise an age field, a symptom field and a disease name field.
4. The method of claim 3, wherein the filtering the electronic medical records from the electronic medical record collection that are similar to the current electronic medical record according to the characteristic field to obtain a similar electronic medical record collection comprises:
processing the electronic medical record set by adopting a natural language processing method according to the characteristic field, and obtaining a characteristic vector of the electronic medical record in the electronic medical record set according to a preset medical symptom knowledge graph;
performing semantic analysis on the current electronic medical record, and extracting symptom fields to obtain feature vectors of the feature fields corresponding to the current electronic medical record;
and matching the characteristic vector of the electronic medical record in the electronic medical record set with the characteristic vector of the current electronic medical record to obtain a similar electronic medical record set.
5. The method of claim 4, wherein the processing the electronic medical record collection by a natural language processing method according to the characteristic fields and obtaining the characteristic vectors of the electronic medical records in the electronic medical record collection according to a preset medical symptom knowledge graph comprises:
performing text word segmentation processing on the electronic medical record set according to the characteristic field to obtain word segmentation results;
and performing part-of-speech recognition on the word segmentation result, and obtaining a feature vector of the electronic medical record in the electronic medical record set according to the word segmentation result, the part-of-speech recognition result and a preset medical symptom knowledge map.
6. The method of claim 4, wherein the matching the feature vector of the electronic medical record in the electronic medical record collection with the feature vector of the current electronic medical record to obtain a similar electronic medical record collection comprises:
calculating the Euclidean distance between the characteristic vector of the electronic medical record in the electronic medical record set and the characteristic vector of the current electronic medical record;
and obtaining a similar electronic medical record set according to the Euclidean distance.
7. The method of claim 1, wherein the identifying the identification of the electronic medical records in the set of similar electronic medical records, obtaining the medical institution characteristic field corresponding to the identification, and constructing the disease-treating physician-treating institution relationship model comprises:
identifying the electronic medical record filing ID in the similar electronic medical record set, and extracting the disease name field of the electronic medical record in the similar electronic medical record set;
searching the working age, the department and the hospital name of the doctor corresponding to the filing ID;
and constructing a relation model of the disease-treating doctor-treating mechanism according to the disease name field and the searched working age of the doctor, the department and the name of the hospital.
8. An electronic medical record data pushing device, characterized in that the device comprises:
the medical record acquisition module is used for acquiring an electronic medical record set of a hospital;
the similar screening module is used for screening the electronic medical records similar to the current electronic medical record from the electronic medical record set to obtain a similar electronic medical record set;
the identification module is used for identifying the identification of the electronic medical records in the similar electronic medical record set, acquiring the characteristic field of the medical institution corresponding to the identification, and constructing a relation model of a disease-treating doctor-treating institution;
and the matching sorting module is used for matching the relation model with the similar electronic medical record set and sorting and pushing matching results according to a preset sorting rule, wherein the preset sorting rule comprises most similar sorting, difficult and complicated disease sorting or most authoritative sorting.
9. A computer device comprising a memory and a processor, the memory storing a computer program, wherein the processor implements the steps of the method of any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911000771.XA CN110752027B (en) | 2019-10-21 | 2019-10-21 | Electronic medical record data pushing method, device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911000771.XA CN110752027B (en) | 2019-10-21 | 2019-10-21 | Electronic medical record data pushing method, device, computer equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110752027A true CN110752027A (en) | 2020-02-04 |
| CN110752027B CN110752027B (en) | 2023-05-23 |
Family
ID=69279138
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911000771.XA Active CN110752027B (en) | 2019-10-21 | 2019-10-21 | Electronic medical record data pushing method, device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110752027B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111430037A (en) * | 2020-03-30 | 2020-07-17 | 安徽科大讯飞医疗信息技术有限公司 | Similar medical record searching method and system |
| CN111540425A (en) * | 2020-04-26 | 2020-08-14 | 吴九云 | Intelligent medical information pushing method based on artificial intelligence and electronic medical record cloud platform |
| CN111613339A (en) * | 2020-05-15 | 2020-09-01 | 山东大学 | Similar medical record searching method and system based on deep learning |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105184103A (en) * | 2015-10-15 | 2015-12-23 | 清华大学深圳研究生院 | Virtual medical expert based on medical record database |
| CN108154928A (en) * | 2017-12-27 | 2018-06-12 | 北京嘉和美康信息技术有限公司 | A kind of methods for the diagnosis of diseases and device |
| CN109215754A (en) * | 2018-09-10 | 2019-01-15 | 平安科技（深圳）有限公司 | Medical record data processing method, device, computer equipment and storage medium |
| CN109346185A (en) * | 2018-09-19 | 2019-02-15 | 北京科技大学 | A kind of aided diagnosis of traditional Chinese medicine system |
| CN109473169A (en) * | 2018-10-18 | 2019-03-15 | 安吉康尔（深圳）科技有限公司 | A kind of methods for the diagnosis of diseases, device and terminal device |
| CN109801688A (en) * | 2017-11-17 | 2019-05-24 | 深圳市前海安测信息技术有限公司 | The safe synergism action system and method for area medical electronic health record |
| CN109801690A (en) * | 2017-11-17 | 2019-05-24 | 深圳市前海安测信息技术有限公司 | Area medical electronic health record is shared to integrate inquiry system and method |
-
2019
- 2019-10-21 CN CN201911000771.XA patent/CN110752027B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105184103A (en) * | 2015-10-15 | 2015-12-23 | 清华大学深圳研究生院 | Virtual medical expert based on medical record database |
| CN109801688A (en) * | 2017-11-17 | 2019-05-24 | 深圳市前海安测信息技术有限公司 | The safe synergism action system and method for area medical electronic health record |
| CN109801690A (en) * | 2017-11-17 | 2019-05-24 | 深圳市前海安测信息技术有限公司 | Area medical electronic health record is shared to integrate inquiry system and method |
| CN108154928A (en) * | 2017-12-27 | 2018-06-12 | 北京嘉和美康信息技术有限公司 | A kind of methods for the diagnosis of diseases and device |
| CN109215754A (en) * | 2018-09-10 | 2019-01-15 | 平安科技（深圳）有限公司 | Medical record data processing method, device, computer equipment and storage medium |
| CN109346185A (en) * | 2018-09-19 | 2019-02-15 | 北京科技大学 | A kind of aided diagnosis of traditional Chinese medicine system |
| CN109473169A (en) * | 2018-10-18 | 2019-03-15 | 安吉康尔（深圳）科技有限公司 | A kind of methods for the diagnosis of diseases, device and terminal device |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111430037A (en) * | 2020-03-30 | 2020-07-17 | 安徽科大讯飞医疗信息技术有限公司 | Similar medical record searching method and system |
| CN111430037B (en) * | 2020-03-30 | 2024-04-09 | 讯飞医疗科技股份有限公司 | Similar medical record searching method and system |
| CN111540425A (en) * | 2020-04-26 | 2020-08-14 | 吴九云 | Intelligent medical information pushing method based on artificial intelligence and electronic medical record cloud platform |
| CN111613339A (en) * | 2020-05-15 | 2020-09-01 | 山东大学 | Similar medical record searching method and system based on deep learning |
| CN111613339B (en) * | 2020-05-15 | 2021-07-09 | 山东大学 | Similar medical record searching method and system based on deep learning |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110752027B (en) | 2023-05-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| KR102088980B1 (en) | System and Method for Providing personalized hospital information | |
| JP6907831B2 (en) | Context-based patient similarity methods and equipment | |
| CN109036545B (en) | Medical information processing method, apparatus, computer device and storage medium | |
| CN111180024B (en) | Data processing method and device based on word frequency and inverse document frequency and computer equipment | |
| CN112035674B (en) | Diagnosis guiding data acquisition method, device, computer equipment and storage medium | |
| CN110752027B (en) | Electronic medical record data pushing method, device, computer equipment and storage medium | |
| CN113724848A (en) | Medical resource recommendation method, device, server and medium based on artificial intelligence | |
| CN111710420A (en) | Complication morbidity risk prediction method, system, terminal and storage medium based on electronic medical record big data | |
| CN113409907A (en) | Intelligent pre-inquiry method and system based on Internet hospital | |
| CN114611735A (en) | Internet registration method, device, equipment and storage medium for hospitalizing | |
| CN109087688B (en) | Patient information acquisition method, apparatus, computer device and storage medium | |
| JP6177609B2 (en) | Medical chart system and medical chart search method | |
| CN113658712A (en) | Doctor-patient matching method, device, equipment and storage medium | |
| CN111785383A (en) | Data processing method and related equipment | |
| US10936962B1 (en) | Methods and systems for confirming an advisory interaction with an artificial intelligence platform | |
| JP6908977B2 (en) | Medical information processing system, medical information processing device and medical information processing method | |
| CN111415760B (en) | Doctor recommendation method, doctor recommendation system, computer equipment and storage medium | |
| CN113111159A (en) | Question and answer record generation method and device, electronic equipment and storage medium | |
| CN109299238B (en) | Data query method and device | |
| CN112349367A (en) | Method and device for generating simulation medical record, electronic equipment and storage medium | |
| CN112259260A (en) | Intelligent medical question and answer method, system and device based on intelligent wearable equipment | |
| US20210133627A1 (en) | Methods and systems for confirming an advisory interaction with an artificial intelligence platform | |
| CN115631823A (en) | Similar case recommendation method and system | |
| CN109522331A (en) | Compartmentalization various dimensions health data processing method and medium centered on individual | |
| CN109063507A (en) | A kind of general design model for hospital information system analysis |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |