CN113707304A - Triage data processing method, device, equipment and storage medium - Google Patents
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Abstract
The invention relates to the technical field of artificial intelligence, and discloses a diagnosis data processing method, a diagnosis data processing device, a diagnosis data processing equipment and a diagnosis data processing storage medium, wherein the method comprises the following steps: receiving a patient visit request, and acquiring the visit information which comprises patient information, symptom description and inspection data in the visit request; carrying out treatment type identification on the symptom description and the inspection data to determine the treatment type of the patient; performing department recommendation based on the patient information to obtain a department recommendation result; carrying out disease map construction and multidimensional conversion on patient information, symptom description, treatment type, department recommendation result and inspection data to obtain a multidimensional diagnosis-waiting map; acquiring a multi-dimensional image map associated with the doctor identification in each department recommendation result; and matching the multi-dimensional image to be diagnosed with each multi-dimensional image to obtain a triage result, and recommending the triage result to the patient. Therefore, the invention realizes the determination of departments and doctors required for triage of patients. The invention is suitable for the field of artificial intelligence and can further promote the construction of intelligent medical treatment.
Description
Technical Field
The invention relates to the technical field of artificial intelligence intelligent decision making, in particular to a diagnosis data processing method, device, equipment and storage medium.
Background
With the progress and development of medical science, hospitals are more specialized in department setting, the problem brought with the professional selection is that a user brings certain difficulty, and in order to solve the problem, each large hospital is additionally provided with a diagnosis guide link, including a diagnosis guide person and an autonomous diagnosis guide service, which mainly helps patients to recommend diagnosis departments.
At present, when a patient goes to a hospital for a diagnosis, the patient firstly needs to go to a diagnosis platform for manual diagnosis, a large amount of queuing time is consumed for the patient in the process, and the depth and the breadth of professional knowledge of a diagnosis guide staff of the diagnosis platform are higher, because the patient often carries the examination contents (or examination reports) of other hospitals for the diagnosis, in the prior art, when the patient is subjected to the diagnosis, the diagnosis guide staff usually needs to firstly read the examination contents (or the examination reports) and then carry out the diagnosis, at the moment, if the diagnosis guide staff gives the patient the wrong diagnosis, the patient needs to be subjected to the diagnosis again, the time of the patient is greatly wasted, the patient experience is seriously affected, and in addition, a reasonable diagnosis department or doctor is often difficult to give out when the diagnosis is wrong, and the patient experience can be further reduced.
Disclosure of Invention
The invention provides a triage data processing method, a device, computer equipment and a storage medium, which can automatically, quickly and accurately determine departments and doctors required by triage of a patient, improve the accuracy of treatment and improve the experience of the patient.
A triage data processing method, comprising:
receiving a patient treatment request, and acquiring treatment information in the treatment request; the visit information comprises patient information, symptom description and inspection data;
carrying out treatment type identification on the symptom description and the inspection data to determine the treatment type of the patient;
performing department recommendation on the visit type based on the patient information to obtain a department recommendation result;
carrying out disease map construction and multidimensional conversion on the patient information, the symptom description, the treatment type, the department recommendation result and the inspection data to obtain a multidimensional diagnosis-waiting map;
acquiring a multi-dimensional image map associated with the doctor identification in each department recommendation result;
and matching the multi-dimensional image to be diagnosed with each acquired multi-dimensional image to obtain a triage result, and recommending the triage result to the patient.
A triage data processing apparatus comprising:
the receiving module is used for receiving a treatment request of a patient and acquiring treatment information in the treatment request; the visit information comprises patient information, symptom description and inspection data;
the identification module is used for carrying out treatment type identification on the symptom description and the inspection data and determining the treatment type of the patient;
the recommending module is used for recommending departments to the treatment types based on the patient information to obtain department recommending results;
the construction module is used for carrying out disease map construction and multidimensional conversion on the patient information, the symptom description, the treatment type, the department recommendation result and the inspection data to obtain a multidimensional diagnostic map;
the acquisition module is used for acquiring a multi-dimensional image map associated with the doctor identification in each department recommendation result;
and the matching module is used for matching the multi-dimensional image to be diagnosed with each acquired multi-dimensional image to obtain a triage result and recommending the triage result to the patient.
A computer device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, said processor implementing the steps of the triage data processing method described above when executing said computer program.
A computer-readable storage medium, which stores a computer program that, when executed by a processor, implements the steps of the triage data processing method described above.
The invention provides a triage data processing method, a device, computer equipment and a storage medium, wherein the method comprises the steps of receiving a patient treatment request, and acquiring treatment information which comprises patient information, symptom description and inspection data in the treatment request; carrying out treatment type identification on the symptom description and the inspection data to determine the treatment type of the patient; performing department recommendation on the visit type based on the patient information to obtain a department recommendation result; carrying out disease map construction and multidimensional conversion on the patient information, the symptom description, the treatment type, the department recommendation result and the inspection data to obtain a multidimensional diagnosis-waiting map; acquiring a multi-dimensional image map associated with the doctor identification in each department recommendation result; the multi-dimensional image to be diagnosed is matched with the acquired multi-dimensional image maps to obtain triage results, and the triage results are recommended to the patient, so that the department and the doctor required to be triaged by the patient can be automatically, quickly and accurately determined by the method of diagnosis type identification, department recommendation and multi-dimensional image map matching, the diagnosis accuracy is improved, and the patient experience is improved.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments of the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without inventive labor.
FIG. 1 is a schematic diagram of an application environment of a triage data processing method according to an embodiment of the present invention;
FIG. 2 is a flow chart of a triage data processing method according to an embodiment of the present invention;
FIG. 3 is a flowchart of step S20 of the triage data processing method according to an embodiment of the present invention;
FIG. 4 is a flowchart of step S40 of the triage data processing method according to an embodiment of the present invention;
FIG. 5 is a flowchart illustrating step S50 of the triage data processing method according to an embodiment of the present invention;
FIG. 6 is a flowchart of step S60 of the triage data processing method according to an embodiment of the present invention;
FIG. 7 is a functional block diagram of a triage data processing apparatus according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a computer device in an embodiment of the invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The triage data processing method provided by the invention can be applied to the application environment shown in fig. 1, wherein a client (computer equipment or terminal) communicates with a server through a network. The client (computer device or terminal) includes, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and portable wearable devices. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), a big data and artificial intelligence platform, and the like.
In an embodiment, as shown in fig. 2, a method for processing triage data is provided, which mainly includes the following steps S10-S60:
s10, receiving a patient treatment request, and acquiring treatment information in the treatment request; the visit information includes patient information, symptom descriptions, and test data.
Understandably, the patient identification is a unique identification code that the patient registers for login in the application platform, the patient needs to log onto the application platform with the patient identification before issuing the visit request, the visit request is a request sent by the patient at the application platform, the patient symptom information is information which is input by the patient and is related to the patient's symptoms and a test report, the patient may enter the symptom description and the test data on an application interface provided by the application platform, or selecting relevant symptoms from all symptom sets provided on the application program interface as the symptom description of the patient, taking a picture of a paper file report of the test as the test data of the patient, such that the request for a visit is triggered after the symptom description and the test data have been entered.
Wherein the patient information is information related to the patient, such as: the system comprises positioning information of a patient, expected time of visit, duration information and the like, wherein the positioning information of the patient is the current longitude and latitude position or the selected longitude and latitude position of the patient, the expected time of the visit is the time range that the patient wishes to visit, the duration information is the duration of a disease discovered from the patient at present, the symptom is described as description related to the symptom of the patient, and the inspection data is electronic data of an inspection report after the patient completes related inspection, namely imaging data of a paper file of the inspection report after the patient completes related inspection and is photographed.
And S20, performing treatment type identification on the symptom description and the inspection data, and determining the treatment type of the patient.
Understandably, the diagnosis type recognition is a process of recognizing a medical type to which a symptom belongs according to the needle-shaped description and the inspection data, the diagnosis type recognition may be a process of performing condition feature extraction on the symptom description, recognizing a medical condition category to which the symptom description relates according to the extracted condition feature, performing image-text recognition on the inspection data, recognizing a medical condition category to which the inspection data relates, and determining a diagnosis type concentrated after classification by combining the medical conditions to which the two relate, where the diagnosis type is a medical condition category, for example: skin allergies, fractures, abnormal body temperature, etc.
In an embodiment, as shown in fig. 3, the step S20 of performing the visit type identification on the symptom description and the test data to determine the visit type of the patient includes:
s201, extracting symptom features of the symptom description through a symptom extraction model, and identifying according to the extracted symptom features to obtain a first identification result.
Understandably, the symptom extraction model is a trained machine learning model for extracting keywords related to symptoms in an input text, the symptom descriptions are subjected to Word vector conversion through the symptom extraction model, the Word vectors are converted into a conversion process of mapping words or words in each symptom description into N-dimensional Word vectors by using the Word2Vec algorithm, the Word2Vec algorithm is a process of predicting vectors corresponding to the words or words by using a shallow Word2Vec model, extracting the keywords or words of the symptom descriptions after the Word vector conversion, extracting the keywords or words related to the symptom features, performing text matching on the extracted keywords or words, and matching the related medical symptom categories to obtain the first recognition result.
S202, image-text recognition is carried out on the inspection data through the unstructured disease recognition model, and a second recognition result is obtained.
Understandably, the unstructured disorder recognition model is a trained model for recognizing medical disorder categories related to disorders in unstructured data, and the image-text recognition process is a process of extracting a test image and a test text from the test data by using an OCR technology, performing human body part recognition on the test image, recognizing a disorder part, and performing disorder association recognition according to the test text and the disorder part to obtain a second recognition result, wherein the extraction process is a process of recognizing a region containing an image in the test data by using an OCR technology, copying the region from the test data to obtain a test image, recognizing all characters in the test data, and splicing all recognized characters to obtain the test text.
In an embodiment, in step S202, the performing image-text recognition on the test data through the unstructured disease recognition model to obtain a second recognition result includes:
and extracting a checking image and a checking text from the checking data by using an OCR technology.
Understandably, the OCR (Optical Character recognition) technology refers to a process in which an electronic device (e.g., a scanner or a digital camera) inspects characters printed on paper, determines the shape thereof by detecting dark and light patterns, then cuts out images of non-characters, and translates the shape into computer words by a Character recognition method.
And carrying out human body part identification on the inspection image to identify the disease part.
Understandably, the human body part is recognized as a process of extracting human body features in an input image, and recognizing a part of the human body to which the human body belongs according to the extracted human body features, thereby determining the recognized part of the human body to which the human body belongs as a diseased part.
And carrying out disease association identification on the test text and the disease part to obtain a second identification result.
Understandably, the association recognition is a process of recognizing the text related to the disease part from the test text, classifying the medical disease category according to the recognized text, and obtaining the second recognition result of the related medical disease category, wherein the second recognition result embodies the medical disease category related in the test data.
The invention realizes that the inspection image and the inspection text are extracted from the inspection data by applying the OCR technology; carrying out human body part identification on the inspection image to identify a disease part; and performing disease association recognition on the inspection text and the disease part to obtain a second recognition result, so that an OCR (optical character recognition) technology can be used for extracting an inspection image and an inspection text from unstructured inspection data information, and the medical disease category related in the inspection data is automatically recognized through human body part recognition and disease association recognition, so that the accuracy and the reliability are improved for subsequent triage processing.
S203, carrying out treatment classification on the first recognition result and the second recognition result, and determining the treatment type.
Understandably, the medical condition categories in the first identification result and the second identification result are aggregated, and finally the medical condition category corresponding to the most dense aggregation point is obtained and recorded as the visit type.
The symptom description is subjected to symptom feature extraction through a symptom extraction model, and identification is carried out according to the extracted symptom features to obtain a first identification result; carrying out image-text recognition on the inspection data through an unstructured disease recognition model to obtain a second recognition result; the first recognition result and the second recognition result are classified to determine the diagnosis types, so that the diagnosis types can be automatically classified by using text matching and image-text recognition methods, accurate diagnosis types are provided for subsequent triage, the diagnosis direction is determined, the condition of wrong triage is avoided, and the triage accuracy is improved.
And S30, performing department recommendation on the visit type based on the patient information to obtain a department recommendation result.
Understandably, conducting department recommendation on the treatment type through the trained deep learning department recommendation model, wherein the department recommendation process includes the steps of extracting mapping features between symptoms and departments through the deep learning department recommendation model, identifying departments related to the treatment type according to the extracted mapping features between the symptoms and the departments, searching all doctor identifications related to the identified departments within a preset range according to positioning information in the patient information, and taking the departments related to the treatment type and the searched results as the department recommendation results.
And S40, performing disease map construction and multidimensional conversion on the patient information, the symptom description, the treatment type, the department recommendation result and the inspection data to obtain a multidimensional diagnosis-waiting map.
Understandably, the process of constructing the disease pattern is that firstly, keywords for disease quantification are extracted from the symptom description and the test text extracted from the test data, and a quantification result is identified; secondly, identifying the degree grade of the inspection text extracted from the inspection data corresponding to the diagnosis type, and identifying a grade result; finally, according to the identified quantitative result, the grade result, the visit type, the department recommendation result, and the duration information and location information in the patient information, a patient condition map of the patient is constructed, where the patient condition map is a picture that associates multiple attributes with the patient as a center, where the multiple attributes include attributes related to conditions, such as the quantitative result, the grade result, the visit type, the department recommendation result, and the duration information and location information in the patient information, and the multidimensional conversion is performed to perform a conversion process of mapping a preset-dimension diagram according to interrelations among the attributes in the patient condition map, and the preset dimension may be set according to requirements, for example: and five dimensions which respectively comprise a place dimension, a time dimension, a department dimension, a treatment direction dimension and a weight dimension, so as to obtain the multi-dimensional image to be treated.
In an embodiment, as shown in fig. 4, in the step 40, performing disorder map construction and multidimensional transformation on the patient information, the symptom description, the visit type, the department recommendation result, and the inspection data to obtain a multidimensional diagnosis-waiting map includes:
s401, disease condition quantitative recognition is carried out on the symptom description and the test text to obtain a quantitative result.
Understandably, the disease quantitative recognition is to splice the disease description and the test text to form a section of text, perform quantifier recognition on the section of text through a quantifier detection model, recognize quantifiers in the section of text, and classify the sections of text according to the recognized times to obtain a quantitative result of the severity.
S402, performing degree grade identification corresponding to the diagnosis type on the inspection image to obtain a grade result.
Understandably, a grade image identification model corresponding to the diagnosis type is obtained, lesion region identification is carried out on the inspection image through the obtained grade image identification model, grade features corresponding to the diagnosis type are extracted from the lesion region, risk assessment is carried out according to the extracted grade features, and the grade result of the risk grade corresponding to the inspection image is predicted, wherein the risk grade comprises high, medium and low.
And S403, constructing a patient disease map of the patient according to the quantification result, the grade result, the clinic consultation type, the department recommendation result and the time length information and the positioning information in the patient information.
Understandably, centering on the patient identifier, the quantitative result, the grade result, the type of visit, the department recommendation result, the duration information, and the positioning information are taken as attributes, and all the attributes are associated to the patient identifier.
S404, carrying out multi-dimensional conversion treatment on the disease symptoms of the patient through a multi-dimensional image conversion model to obtain a multi-dimensional image to be diagnosed corresponding to the disease symptoms of the patient.
Understandably, the multi-dimensional diagnostic graph conversion model is a graph model which is learned through a large amount of historical data and maps each attribute in the patient disease state graph to the value of each dimension in the multi-dimensional diagnostic graph according to the correlation between the attributes, and the preset dimension can be set according to requirements, for example: and five dimensions respectively including a site dimension, a time dimension, a department dimension, a treatment direction dimension and a weight dimension, measuring the value of the weight dimension through the incidence relation among the quantization result, the grade result and the duration information, measuring the value of the time dimension through the current time and the duration information, measuring the value of the department dimension through the department recommendation result and the treatment type, measuring the value of the treatment direction dimension through the treatment type, the quantization result and the grade result, and determining the value of the site dimension through the positioning information.
The invention realizes quantitative identification of the disease condition of the symptom description and the test text to obtain a quantitative result; carrying out degree grade identification corresponding to the diagnosis type on the inspection image to obtain a grade result; constructing a patient disease map of the patient according to the quantitative result, the grade result, the clinic consultation type, the department recommendation result and the duration information and the positioning information in the patient information; the patient disease atlas is subjected to multidimensional conversion processing through the multidimensional to-be-diagnosed graph conversion model to obtain the multidimensional to-be-diagnosed graph corresponding to the patient disease atlas, so that the multidimensional to-be-diagnosed graph of the patient can be automatically generated through quantitative disease identification, degree grade identification, patient disease atlas construction and multidimensional conversion methods, the current disease state of the patient can be accurately, objectively and quickly measured, a data base is provided for subsequent triage results, and accuracy and reliability of triage are improved.
And S50, acquiring a multi-dimensional image map associated with the doctor identification in each department recommendation result.
Understandably, each doctor identifier in the department recommendation result is searched from an image library, the doctor identifier is an identification code which is unique to each doctor, the multi-dimensional image map which is associated with the searched doctor identifier is obtained, the multi-dimensional image map represents the multi-dimensional image which is obtained after the corresponding doctor identifier is subjected to one-to-one dimensional image drawing with the dimension in the multi-dimensional image to be diagnosed, and the image drawing process can be a process of combining the doctor visit point, the doctor visit state table and the doctor record data which are associated with the doctor identifier, confirming the image drawing and the diagnosis point, constructing the multi-dimensional image map, drawing an index map in the multi-dimensional image map and recording the diagnosis point in the multi-dimensional image map.
Wherein the image gallery stores the multi-dimensional image associated with all the doctor identifiers.
In an embodiment, as shown in fig. 5, before the step S50, that is, before the obtaining the multi-dimensional image map associated with the doctor identifier in each department recommendation result, the method includes:
and S501, acquiring a point of visit, a state table of the visit and doctor record data associated with the doctor identifier.
Understandably, the point of visit is a longitude and latitude position of a doctor's visit corresponding to the doctor identifier, the state table of the visit is a date and time table of the doctor's visit corresponding to the doctor identifier, the doctor resume data is a doctor's job title, a visit record and a history resume corresponding to the doctor identifier, the doctor's job title is a grade reflecting the research degree of the medical field of the doctor, the visit record is the history visit record of the doctor, the visit record includes a score of each visit record, and the doctor resume data is data related to the medical research direction, such as a prize, a journal, a paper and the like of the doctor.
S502, doctor portrait is carried out on the doctor record data and the doctor state table which are related to the doctor identification, and an index map of the multi-dimensional portrait which is related to the doctor identification is constructed.
Understandably, the doctor portrait drawing process is to predict the time matching degree based on the expected time of the doctor visit and the visit state table in the patient information, and meanwhile, perform department portrait drawing and specialty portrait drawing based on the doctor job title and the visit record associated with the doctor identification to determine the adept department and the specialty direction; grading the grade based on the doctor job title and the doctor resume data associated with the doctor identifier to obtain a grade score; and a process of creating the index map by integrating the time matching degree, the expert department, the specialty direction and the grade score, wherein the index map is a map which represents index values corresponding to the time matching degree, the expert department, the specialty direction and the grade score in a one-to-one manner through a quadrangle.
In one embodiment, the doctor history data includes doctor job title, visit record and history record, understandably, the doctor job title is a grade reflecting research degree of medical field of doctor, the visit record is history visit record of doctor, the visit record includes score of each visit record, and the doctor history data is data related to medical research direction of doctor awards, periodicals, papers and the like.
In step S502, the constructing an index map of the multidimensional map associated with the doctor identifier by performing doctor mapping on the doctor history data and the doctor status table associated with the doctor identifier includes:
and predicting the diagnosis state table associated with the doctor identifier based on the expected time of seeing a doctor in the patient information to obtain the time matching degree associated with the doctor identifier.
Understandably, the expected time of seeing a doctor is the time that the patient expects to see a doctor, the process of predicting the diagnosis state table associated with the doctor identifier identifies the time that the patient is expected to see a doctor from the diagnosis state table, and the process of determining the time matching degree of seeing a doctor of the patient according to the amount of appointment-available number corresponding to the identified time, wherein the time matching degree represents the degree that the patient and the doctor can be matched in the time dimension.
And according to the doctor job title and the visit record which are associated with the doctor identification, department portrait and specialty portrait are carried out, and the adequacy department and the specialty direction which are associated with the doctor identification are obtained.
Understandably, extracting the characteristics related to departments and the characteristics related to the direction of expertise by performing text extraction on the doctor titles and the visit records, and weighting features associated with departments and features associated with directions of expertise based on the scores in the visit records, performing department portrait on the weighted characteristics related to departments, and performing speciality portrait on the weighted characteristics related to the speciality direction, the department portrayal is obtained by clustering the weighted characteristics related to the departments on each department, the department ranked first after clustering is taken as the expert department, the specialty portrait is chosen in each research direction under the expert department according to the weighted feature related to the specialty direction, and the research direction with the most or most votes is chosen as the specialty direction after the choice.
Wherein the expert department represents the department in which the doctor is most adept at visiting in the dimension of the department, and the specialty direction represents the medical research direction in which the doctor is most deeply researched in the dimension of the visiting direction.
And grading according to the doctor job title and the doctor resume data associated with the doctor identifier to obtain a grade score associated with the doctor identifier.
Understandably, the grade score is determined by determining a grade value corresponding to the doctor job title from the doctor job title and the doctor resume data, and the grade score is determined by correspondingly scoring each piece of data in the doctor resume data on the basis of the grade value, wherein the grade score represents an index of risk grade of a doctor on the degree of importance in the dimension of importance.
Creating the index map associated with the doctor identification according to the time matching degree, the good department, the specialty direction and the grade score associated with the doctor identification.
The invention realizes that the time matching degree associated with the doctor identifier is obtained by predicting the diagnosis state table associated with the doctor identifier based on the expected time of seeing a doctor in the patient information; according to the doctor job title and the visit record which are associated with the doctor identification, department portrait and specialty portrait are carried out, and the adequacy department and the specialty direction which are associated with the doctor identification are obtained; according to the doctor job title and the doctor resume data which are associated with the doctor identification, carrying out grade grading to obtain a grade score which is associated with the doctor identification; according to the time matching degree, the expert department, the specialty direction and the grade score which are associated with the doctor identification, the index map which is associated with the doctor identification is created, so that indexes of the doctor in a time dimension, a department dimension, a research direction dimension and a light and heavy dimension can be intuitively and objectively embodied, the comprehensive ability of the doctor is rapidly achieved through the index map, and the accuracy is improved for subsequent diagnosis.
S503, determining the diagnosis point associated with the doctor identifier as the triage point of the multi-dimensional image map associated with the doctor identifier.
The invention realizes that the doctor identification is associated with the doctor identification through obtaining the doctor point, the doctor state table and the doctor resume data; performing doctor portrait on the doctor record data and the doctor state table associated with the doctor identification, and constructing an index map of the multi-dimensional portrait associated with the doctor identification; the doctor identification is used for identifying the doctor identification, and the doctor identification is used for identifying the doctor identification and the doctor visiting point.
And S60, matching the multi-dimensional image to be diagnosed with each acquired multi-dimensional image to obtain a triage result, and recommending the triage result to the patient.
Understandably, the matching process may be to use an euclidean distance method to calculate a distance between a positioning point in the multi-dimensional image to be diagnosed and a triage point in each of the multi-dimensional image maps, and determine a distance matching value corresponding to a mapping of the segmentation range according to a segmentation range in which the distance falls, that is, a range of which segment the distance value falls (where, the range of each segment may be set as required, for example, a first segment is 0 to 200m corresponding to 100%, a second segment is 200m to 500m corresponding to 95%, and the like), from a percentage corresponding to the falling range; then, calculating the coincidence degree of the index image in the multi-dimensional image to be diagnosed and the image in each multi-dimensional image, and outputting the coincidence value of the multi-dimensional image to be diagnosed and each multi-dimensional image; combining the distance matching value and the coincidence value to measure the matching degree of the multi-dimensional image to be diagnosed and each multi-dimensional image; and finally, selecting the multidimensional image with the highest matching degree as the triage result, and recommending the multidimensional image to the patient, so that the situations of hanging wrong numbers, running wrong hospitals, running more hospitals, seeing wrong doctors and the like can be greatly reduced.
The invention realizes that the treatment information containing the patient information, the symptom description and the inspection data in the treatment request is obtained by receiving the treatment request of the patient; carrying out treatment type identification on the symptom description and the inspection data to determine the treatment type of the patient; performing department recommendation on the visit type based on the patient information to obtain a department recommendation result; carrying out disease map construction and multidimensional conversion on the patient information, the symptom description, the treatment type, the department recommendation result and the inspection data to obtain a multidimensional diagnosis-waiting map; acquiring a multi-dimensional image map associated with the doctor identification in each department recommendation result; the multi-dimensional image to be diagnosed is matched with the acquired multi-dimensional image maps to obtain triage results, and the triage results are recommended to the patient, so that the department and the doctor required to be triaged by the patient can be automatically, quickly and accurately determined by the method of diagnosis type identification, department recommendation and multi-dimensional image map matching, the diagnosis accuracy is improved, and the patient experience is improved.
In an embodiment, as shown in fig. 6, in the step S60, the matching the multi-dimensional image to be diagnosed with each acquired multi-dimensional image to obtain a triage result includes:
s601, calculating the distance between the positioning point in the multi-dimensional image to be diagnosed and the diagnosis dividing point in each multi-dimensional image map by using a Euclidean distance method to obtain the distance matching value between the multi-dimensional image to be diagnosed and each multi-dimensional image map.
Understandably, the euclidean distance method is an euclidean distance algorithm or an euclidean metric algorithm, and also refers to an algorithm of a real distance between two points in a multidimensional space or a natural length of a vector (i.e. a distance between the point and an origin), a euclidean distance algorithm is used to calculate a value of a distance between a positioning point in the multidimensional diagnostic image and a diagnosis point in the multidimensional image, the calculated value may be inverted and converted into a percentage, so as to obtain a distance matching value between the multidimensional diagnostic image and the multidimensional image, or a range of segments in which the distance falls may be determined according to a segment range in which the distance falls (where the range of each segment may be set as required, for example, the first segment is 0 to 200m corresponding to 100%, the second segment is 200m to 500m corresponding to 95%, and the like), a percentage corresponding to the falling range is determined to be a distance matching value corresponding to the segment range, the distance matching value can measure the matching degree of the positioning points in the multi-dimensional image to be diagnosed nearby.
S602, calculating the coincidence degree of the index map in the multi-dimensional image to be diagnosed and the image map in each multi-dimensional image map, and outputting the coincidence value of the multi-dimensional image to be diagnosed and each multi-dimensional image map.
Understandably, the coincidence degree is calculated by aligning the origin of each dimension in the multi-dimensional image to be diagnosed with the origin of the corresponding dimension in the multi-dimensional image, placing the multi-dimensional image to be diagnosed and the multi-dimensional image on the same plane, calculating the intersection area between the multi-dimensional image to be diagnosed and the multi-dimensional image, and the area of the multi-dimensional image to be diagnosed, dividing the intersection area with the area of the multi-dimensional image to be diagnosed to obtain the percentage of the intersection area in the multi-dimensional image to be diagnosed, and recording the percentage as the coincidence value of the multi-dimensional image to be diagnosed and the multi-dimensional image, wherein the coincidence value represents the coincidence degree between the multi-dimensional image to be diagnosed and the multi-dimensional image.
S603, obtaining a final matching value of the multi-dimensional image to be diagnosed and each multi-dimensional image according to the distance matching value and the coincidence value of the multi-dimensional image to be diagnosed and each multi-dimensional image.
Understandably, the distance matching values and the coincidence values of the multidimensional waiting-to-be-diagnosed image and the multidimensional image map are subjected to weighted summation to calculate a final matching value of the multidimensional waiting-to-be-diagnosed image and the multidimensional image map, wherein the weight of the distance matching values and the weight of the coincidence values can be set according to requirements, and can also be output according to historical statistics, for example: the weight of the distance matching value is 0.4, the weight of the coincidence value is 0.6, and the sum of the weight of the distance matching value and the weight of the coincidence value is one, so that the final matching value of the multi-dimensional image to be diagnosed and each multi-dimensional image map can be obtained, and the final matching value measures the matching degree between the multi-dimensional image to be diagnosed and each multi-dimensional image map.
S604, sequencing all the final matching values, and determining the multi-dimensional portrait image corresponding to the sequenced final matching values as the triage result.
Understandably, all the final matching values are sorted in a descending manner, that is, the largest final matching value is arranged in the first sequence, and the multi-dimensional portrait corresponding to the sorted final matching value is determined as the triage result, for example, the multi-dimensional portrait corresponding to the sorted final matching value in a preset number before is determined as the triage result.
The invention realizes that the distance between the positioning point in the multi-dimensional image to be diagnosed and the diagnosis point in each multi-dimensional image map is calculated by using the Euclidean distance method to obtain the distance matching value of the multi-dimensional image to be diagnosed and each multi-dimensional image map; calculating the coincidence degree of an index image in the multi-dimensional image to be diagnosed and an image in each multi-dimensional image, and outputting the coincidence value of the multi-dimensional image to be diagnosed and each multi-dimensional image; obtaining a final matching value of the multi-dimensional image to be diagnosed and each multi-dimensional image according to the distance matching value and the coincidence value of the multi-dimensional image to be diagnosed and each multi-dimensional image; all the final matching values are sequenced, the multi-dimensional image corresponding to the sequenced final matching values is determined as the triage result, so that the final matching values are calculated through a Euclidean distance method and a coincidence degree calculation method, the final matching values are sequenced to obtain the best matching triage result, the triage result which is most suitable for doctors of patients can be matched accurately, and the accuracy and the correctness of triage are improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
In an embodiment, a triage data processing apparatus is provided, and the triage data processing apparatus corresponds to the triage data processing method in the above embodiment one to one. As shown in fig. 7, the triage data processing apparatus includes a receiving module 11, an identifying module 12, a recommending module 13, a constructing module 14, an obtaining module 15, and a matching module 16. The functional modules are explained in detail as follows:
the receiving module 11 is configured to receive a visit request of a patient, and acquire visit information in the visit request; the visit information comprises patient information, symptom description and inspection data;
the identification module 12 is used for carrying out treatment type identification on the symptom description and the inspection data and determining the treatment type of the patient;
the recommending module 13 is configured to perform department recommendation on the visit type based on the patient information to obtain a department recommendation result;
the construction module 14 is configured to perform disorder map construction and multidimensional conversion on the patient information, the symptom description, the visit type, the department recommendation result, and the inspection data to obtain a multidimensional diagnosis-waiting map;
an obtaining module 15, configured to obtain a multi-dimensional image map associated with a doctor identifier in each department recommendation result;
and the matching module 16 is used for matching the multi-dimensional image to be diagnosed with each acquired multi-dimensional image to obtain a triage result, and recommending the triage result to the patient.
For specific limitations of the triage data processing apparatus, reference may be made to the above limitations on the triage data processing method, which are not described herein again. The modules in the triage data processing device can be wholly or partially 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 client or a server, and its internal structure diagram may be as shown in fig. 8. 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 readable storage medium and an internal memory. The readable 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 readable storage medium. 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 implement a method of triage data processing.
In one embodiment, a computer device is provided, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the computer program, the triage data processing method in the above embodiments is implemented.
In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements the triage data processing method in the above-described embodiments.
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, databases, or other media used in embodiments provided herein may include non-volatile and/or volatile memory. 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).
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present 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 solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present invention, and are intended to be included within the scope of the present invention.
Claims (10)
1. A method for processing triage data, comprising:
receiving a patient treatment request, and acquiring treatment information in the treatment request; the visit information comprises patient information, symptom description and inspection data;
carrying out treatment type identification on the symptom description and the inspection data to determine the treatment type of the patient;
performing department recommendation on the visit type based on the patient information to obtain a department recommendation result;
carrying out disease map construction and multidimensional conversion on the patient information, the symptom description, the treatment type, the department recommendation result and the inspection data to obtain a multidimensional diagnosis-waiting map;
acquiring a multi-dimensional image map associated with the doctor identification in each department recommendation result;
and matching the multi-dimensional image to be diagnosed with each acquired multi-dimensional image to obtain a triage result, and recommending the triage result to the patient.
2. The triage data processing method of claim 1, wherein said identifying the type of encounter with the symptom description and the test data to determine the type of encounter with the patient comprises:
extracting symptom characteristics of the symptom description through a symptom extraction model, and identifying according to the extracted symptom characteristics to obtain a first identification result;
carrying out image-text recognition on the inspection data through an unstructured disease recognition model to obtain a second recognition result;
and carrying out treatment classification on the first recognition result and the second recognition result, and determining the treatment type.
3. The triage data processing method of claim 2, wherein the performing image-text recognition on the test data through the unstructured disease recognition model to obtain a second recognition result comprises:
extracting a checking image and a checking text from the checking data by using an OCR technology;
carrying out human body part identification on the inspection image to identify a disease part;
and carrying out disease association identification on the test text and the disease part to obtain a second identification result.
4. The triage data processing method of claim 1, wherein the step of performing disease map construction and multidimensional transformation on the patient information, the symptom description, the visit type, the department recommendation and the inspection data to obtain a multidimensional diagnostic map comprises:
carrying out quantitative identification on the disease condition of the symptom description and the test text to obtain a quantitative result;
carrying out degree grade identification corresponding to the diagnosis type on the inspection image to obtain a grade result;
constructing a patient disease map of the patient according to the quantitative result, the grade result, the clinic consultation type, the department recommendation result and the duration information and the positioning information in the patient information;
and carrying out multi-dimensional conversion treatment on the patient disease symptoms map through a multi-dimensional image conversion model to be diagnosed to obtain a multi-dimensional image to be diagnosed corresponding to the patient disease symptoms map.
5. The triage data processing method of claim 1, wherein prior to obtaining the multi-dimensional images associated with the physician identification in each of the department recommendations, comprising:
acquiring a point of visit, a state table of the visit and doctor record data associated with the doctor identifier;
performing doctor portrait on the doctor record data and the doctor state table associated with the doctor identification, and constructing an index map of the multi-dimensional portrait associated with the doctor identification;
and determining the diagnosis point associated with the doctor identifier as the diagnosis dividing point of the multi-dimensional image map associated with the doctor identifier.
6. The triage data processing method of claim 5, wherein the physician biographical data includes physician title, visit record and historical biographies;
the constructing an index map of the multi-dimensional image map associated with the doctor identifier by performing doctor portrait on the doctor visit state table associated with the doctor identifier and the doctor resume data comprises:
predicting the diagnosis state table associated with the doctor identifier based on expected time of seeing a doctor in the patient information to obtain time matching degree associated with the doctor identifier;
according to the doctor job title and the visit record which are associated with the doctor identification, department portrait and specialty portrait are carried out, and the adequacy department and the specialty direction which are associated with the doctor identification are obtained;
according to the doctor job title and the doctor resume data which are associated with the doctor identification, carrying out grade grading to obtain a grade score which is associated with the doctor identification;
creating the index map associated with the doctor identification according to the time matching degree, the good department, the specialty direction and the grade score associated with the doctor identification.
7. The triage data processing method of claim 1, wherein the matching of the multi-dimensional image to be diagnosed with each of the acquired multi-dimensional image maps to obtain a triage result comprises:
calculating the distance between a positioning point in the multi-dimensional image to be diagnosed and a diagnosis dividing point in each multi-dimensional image by using an Euclidean distance method to obtain a distance matching value between the multi-dimensional image to be diagnosed and each multi-dimensional image;
calculating the coincidence degree of an index image in the multi-dimensional image to be diagnosed and an image in each multi-dimensional image, and outputting the coincidence value of the multi-dimensional image to be diagnosed and each multi-dimensional image;
obtaining a final matching value of the multi-dimensional image to be diagnosed and each multi-dimensional image according to the distance matching value and the coincidence value of the multi-dimensional image to be diagnosed and each multi-dimensional image;
and sequencing all the final matching values, and determining the multi-dimensional portrait image corresponding to the sequenced final matching values as the triage result.
8. A triage data processing apparatus, comprising:
the receiving module is used for receiving a treatment request of a patient and acquiring treatment information in the treatment request; the visit information comprises patient information, symptom description and inspection data;
the identification module is used for carrying out treatment type identification on the symptom description and the inspection data and determining the treatment type of the patient;
the recommending module is used for recommending departments to the treatment types based on the patient information to obtain department recommending results;
the construction module is used for carrying out disease map construction and multidimensional conversion on the patient information, the symptom description, the treatment type, the department recommendation result and the inspection data to obtain a multidimensional diagnostic map;
the acquisition module is used for acquiring a multi-dimensional image map associated with the doctor identification in each department recommendation result;
and the matching module is used for matching the multi-dimensional image to be diagnosed with each acquired multi-dimensional image to obtain a triage result and recommending the triage result to the patient.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the triage data processing method according to any one of claims 1 to 7 when executing the computer program.
10. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the triage data processing method according to any one of claims 1 to 7.
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