CN111105852B - Electronic medical record recommendation method, device, terminal and storage medium - Google Patents

Electronic medical record recommendation method, device, terminal and storage medium Download PDF

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CN111105852B
CN111105852B CN201911214460.3A CN201911214460A CN111105852B CN 111105852 B CN111105852 B CN 111105852B CN 201911214460 A CN201911214460 A CN 201911214460A CN 111105852 B CN111105852 B CN 111105852B
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electronic medical
medical record
candidate
target
similarity
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CN111105852A (en
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李想
沈宏
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Shanghai United Imaging Intelligent Healthcare Co Ltd
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Shanghai United Imaging Intelligent Healthcare Co Ltd
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    • 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

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  • Biomedical Technology (AREA)
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Abstract

The invention discloses a method, a device, a terminal and a storage medium for recommending electronic medical records, wherein the method for recommending the electronic medical records comprises the following steps: determining a first disease type and at least one first disease corresponding to a target electronic medical record to be processed; matching a second disease type corresponding to the history electronic medical record in a preset history electronic medical record library with the first disease type, and determining a matched candidate history electronic medical record to obtain a candidate history electronic medical record set; determining the disorder similarity corresponding to the candidate historical electronic medical records according to the similarity between at least one second disorder corresponding to the candidate historical electronic medical records and the at least one first disorder; and determining at least one candidate historical electronic medical record with the disease similarity meeting the preset condition in the candidate historical electronic medical record set as a historical electronic medical record to be recommended, and displaying the historical electronic medical record to be recommended. The recommendation efficiency of the electronic medical record is improved.

Description

Electronic medical record recommendation method, device, terminal and storage medium
Technical Field
The invention relates to the technical field of medical treatment, in particular to a method, a device, a terminal and a storage medium for recommending electronic medical records.
Background
With the intelligent development of medical systems, electronic medical records have been widely used for the preservation of medical data, which may include information in many aspects such as complaints, medical history, examination, diagnosis, and the like.
At present, when a doctor diagnoses diseases, a treatment scheme of the diseases needs to be used for referencing a past diagnosis record, and then current treatment suggestions are filled in an electronic medical record, so that a computer system needs to recommend a historical electronic medical record. In the related art, when the history electronic medical record is recommended, the recommendation process is complex and time-consuming, so that the recommendation efficiency of the electronic medical record is low.
Disclosure of Invention
In order to solve the problems in the prior art, the embodiment of the invention provides a method, a device, a terminal and a storage medium for recommending electronic medical records. The technical scheme is as follows:
in one aspect, an electronic medical record recommendation method is provided, and the method includes:
determining a first disease type and at least one first disease corresponding to a target electronic medical record to be processed;
matching a second disease type corresponding to the history electronic medical record in a preset history electronic medical record library with the first disease type, and determining a matched candidate history electronic medical record to obtain a candidate history electronic medical record set;
Determining the disorder similarity corresponding to the candidate historical electronic medical records according to the similarity between at least one second disorder corresponding to the candidate historical electronic medical records in the candidate historical electronic medical records and the at least one first disorder;
and determining at least one candidate historical electronic medical record, of which the symptom similarity meets a preset condition, in the candidate historical electronic medical record set as a historical electronic medical record to be recommended, and displaying the historical electronic medical record to be recommended.
In another aspect, an electronic medical record recommendation apparatus is provided, the apparatus including:
the first determining module is used for determining a first disease type and at least one first disease corresponding to the target electronic medical record to be processed;
the second determining module is used for matching a second disease type corresponding to the history electronic medical record in the preset history electronic medical record library with the first disease type, determining a matched candidate history electronic medical record and obtaining a candidate history electronic medical record set;
a third determining module, configured to determine a disorder similarity corresponding to the candidate historical electronic medical record according to a similarity between at least one second disorder corresponding to the candidate historical electronic medical record in the candidate historical electronic medical record set and the at least one first disorder;
And the recommending module is used for determining at least one candidate historical electronic medical record, of which the symptom similarity meets a preset condition, in the candidate historical electronic medical record set as a historical electronic medical record to be recommended, and displaying the historical electronic medical record to be recommended.
As an optional implementation manner, the apparatus further includes a construction module for constructing the preset history electronic medical record library, where the construction module includes:
the first acquisition module is used for acquiring a plurality of historical electronic medical records;
a fourth determining module, configured to determine a second disease type and at least one second condition corresponding to each of the plurality of historical electronic medical records;
a first relation establishing module, configured to establish a mapping relation between the historical electronic medical record and index information, where the index information corresponds to the historical electronic medical record and the second disease type and at least one second disease;
and the creation module is used for creating the preset history electronic medical record library according to the mapping relation.
As an alternative embodiment, the first determining module includes:
the second acquisition module is used for acquiring text content of the target electronic medical record to be processed;
The classification processing module is used for carrying out classification processing on the text content based on a disease classification model to obtain a first disease type corresponding to the target electronic medical record;
the named entity recognition module is used for carrying out named entity recognition on the text content based on a named entity recognition model to obtain at least one named entity;
the first display module is used for displaying the at least one named entity;
and a fifth determining module, configured to determine a target named entity according to a selection instruction for a named entity in the at least one named entity, where the target named entity is used as the at least one first disorder.
As an optional embodiment, the third determining module includes:
the first mapping module is used for mapping the at least one first disorder into first word vectors respectively to obtain a first word vector set;
the second mapping module is used for mapping at least one second condition corresponding to each candidate history electronic medical record in the candidate history electronic medical records into a second word vector to obtain a second word vector set;
the first calculation module is used for calculating the similarity between the first word vector in the first word vector set and the second word vector in the second word vector set to obtain a similarity set corresponding to the candidate historical electronic medical record;
The second calculation module is used for calculating the average value of the similarity in the similarity set to obtain average similarity; and the average similarity is used as the disease similarity corresponding to the candidate historical electronic medical records.
As an alternative embodiment, the recommendation module includes:
the arrangement module is used for sequentially arranging the candidate historical electronic medical records according to the disorder similarity;
and the third acquisition module is used for acquiring the candidate historical electronic medical records of the preset arrangement positions to obtain the historical electronic medical records to be recommended.
As an alternative embodiment, the apparatus further comprises:
a fourth obtaining module, configured to obtain target text information in the history electronic medical record to be recommended;
the word segmentation processing module is used for carrying out word segmentation processing on the target text information to obtain a word set;
a sixth determining module, configured to determine a target word whose word frequency of the words in the word set exceeds a preset word frequency, to obtain a target word set;
and the second relation establishing module is used for establishing the association relation between the target word set and the target electronic medical record.
As an alternative embodiment, the apparatus further comprises:
The first response module is used for responding to target processing of the target electronic medical record and acquiring the target word set according to the association relation;
the second display module is used for displaying the target words in the target word set;
the second response module is used for responding to the selection instruction of the target word and determining the word of interest;
and the result generation module is used for generating a processing result of the target processing according to the interesting words.
In another aspect, a terminal is provided, including a processor and a memory, where at least one instruction, at least one program, a code set, or an instruction set is stored in the memory, where the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the electronic medical record recommendation method described above.
In another aspect, a computer readable storage medium having stored therein at least one instruction, at least one program, code set, or instruction set loaded and executed by a processor to implement an electronic medical record pushing method as described above is provided.
According to the embodiment of the invention, the candidate historical electronic medical record set matched with the target electronic medical record to be processed is determined through the matching of the disease types, then the disease similarity corresponding to each candidate historical electronic medical record is determined based on the similarity between the disease of the candidate historical electronic medical record and the disease of the target electronic medical record, further the historical electronic medical record to be recommended, the disease similarity of which meets the preset condition, is screened out from the candidate historical electronic medical record set, and the historical electronic medical record to be recommended is displayed.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a method for recommending electronic medical records according to an embodiment of the present invention;
FIG. 2 is a flow chart of a method for building a library of preset historic electronic medical records according to an embodiment of the present invention;
FIG. 3 is a flowchart of another electronic medical record recommendation method according to an embodiment of the present invention;
FIG. 4 is a flowchart of another electronic medical record recommendation method according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of an electronic medical record recommendation device according to an embodiment of the present invention;
fig. 6 is a schematic structural diagram of another electronic medical record recommendation apparatus according to an embodiment of the present invention;
fig. 7 is a block diagram of a hardware structure of a terminal according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or server that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Referring to fig. 1, a flowchart of an electronic medical record recommendation method provided by an embodiment of the present invention is shown, and the electronic medical record recommendation method of the embodiment of the present invention may be applied to an electronic medical record recommendation apparatus of the embodiment of the present invention, where the electronic medical record recommendation apparatus may be configured in an electronic device such as a terminal or a server. The terminal can be a mobile phone, a tablet personal computer, a personal digital assistant and other hardware devices with various operating systems; the server may comprise a single independently operating server, or a distributed server, or a server cluster consisting of a plurality of servers.
Furthermore, it should be noted that the present specification provides method operational steps as described in the examples or flowcharts, but may include more or fewer operational steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. In actual system or product execution, the methods illustrated in the embodiments or figures may be performed sequentially or in parallel (e.g., in a parallel processor or multi-threaded processing environment).
The electronic medical record recommendation method will be described in detail below with reference to specific embodiments from the perspective of a terminal, and as shown in fig. 1, the method may include:
s101, determining a first disease type and at least one first disease corresponding to a target electronic medical record to be processed.
The target electronic medical record to be processed is an electronic medical record which needs to be perfected by doctors, and can be but not limited to the electronic medical record which comprises treatment suggestions to be filled in.
Disorders refer to the appearance of an abnormal state in the body that is associated with a particular disease type. In the embodiments of the present disclosure, the first disorder may be any disorder corresponding to an associated disease type, preferably, the first disorder may be a critical disorder or an important disorder corresponding to an associated disease type, where the critical disorder or the important disorder refers to a main external appearance feature when an abnormal state occurs in an organism, and is a disorder with a strong association with a disease type. For example, the disease type is nephritis and the corresponding condition may include, but is not limited to, edema, proteinuria, hypertension, pregnancy, and the like, wherein the critical or important condition may be hypertension, pregnancy, and the like.
In the embodiment of the present disclosure, the first disease type of the target electronic medical record may be determined based on a pre-trained disease classification model, and the first condition of the target electronic medical record may be determined based on a pre-trained named entity model, so that, before step S101, the method further includes the steps of training the disease classification model and training the named entity model.
For training a disease classification model, in practical application, a classification training sample set may be determined before training, where the classification training sample set includes a plurality of classification training samples, each classification training sample includes a historical electronic medical record and a disease type corresponding to the historical electronic medical record, for example, the training sample set is recorded as x= { X 1 ,x 2 ……x n X, where x 1 Representing a training sample. The historical electronic medical records in the classification training sample can be any electronic medical record with specific disease types in the electronic medical record library.
The disease classification model to be trained can comprise a text processing word vector embedding layer, a bidirectional cyclic neural network layer, a pooling layer and an output layer which are connected in sequence, wherein the text processing word vector embedding layer is used for carrying out word vector coding on text contents of an input historical electronic medical record to obtain a corresponding word vector e (w); the bidirectional cyclic neural network layer is used for acquiring context coding information in the text content of the historical electronic medical record according to the word vector e (w) to respectively obtain a vector R left Sum vector R right Wherein the vector R left Corresponding to the above encoded information, R vector right corresponds to the followingEncoding information; the pooling layer is used for performing downsampling operation, i.e. returning the maximum value in the sampling window as downsampling output, and in particular implementation, the word vector e (w) and the vector R can be used left Sum vector R right And splicing to obtain a spliced vector, and taking the spliced vector as the input of the pooling layer. In the disease classification model, since each layer output is a linear function of the previous layer input, considering that in practical application data is not always linearly separable, nonlinear factors can be introduced by adding an activation function, namely adding an activation function layer before pooling the layers, since the Sigmoid activation function is at [ -1,1 ] in the input]In between, the function value is sensitive, and loses sensitivity once approaching or exceeding the interval, and is in a saturated state, so in the embodiment of the present specification, the tanh activation function can be adopted as the activation function of the activation function layer.
The output layer can identify the disease category of the historical electronic medical record and output corresponding category labels, the output layer can adopt a softmax function to output category labels, and the softmax function comprises a nonlinear classifier to perform category classification training on the historical electronic medical record.
In the disease classification model training process, the training target is that the predicted class label output by the model is as close as possible to the disease type (namely, the labeling class label) of the corresponding historical electronic medical record in the input training sample, based on the predicted class label output and the disease type (namely, the labeling class label) input, the loss value of the disease classification model can be determined according to the predicted class label output and the disease type input, and the parameters of the disease classification model can be adjusted based on the loss value until the training ending condition is met. The training ending condition may be that the loss value of the disease classification model is in a convergence state, or that the loss value is smaller than a preset value, and may be specifically set according to actual requirements.
After the disease classification model is trained, when the first disease type corresponding to the target electronic medical record to be processed is determined, text content of the target electronic medical record to be processed can be obtained, and then classification processing is carried out on the text content based on the trained disease classification model, so that the first disease type corresponding to the target electronic medical record is obtained. The text content of the target electronic medical record may be all record information currently contained in the target electronic medical record.
Named entity recognition (Named Entity Recognition, simply called NER), also referred to as "private name recognition," refers to the recognition of entities in text that have a specific meaning, and may include, but is not limited to, name of person, place name, disorder name, organization name, work noun, network vocabulary of a specific meaning, other proper nouns, and the like.
For training a named entity recognition model, in practical application, a named entity recognition training sample set can be determined before training, the named entity recognition training sample set comprises a large number of disease words in the medical field, the named entity recognition model to be trained is pre-trained through the large number of disease words, model parameters in convergence are obtained, and the named entity recognition model containing the model parameters in convergence is the trained named entity recognition model used in the embodiment of the specification.
The named entity recognition model to be trained can be, but is not limited to, a BERT (Bidirectional Encoder Representation from Transformers) network model, and the BERT model can adopt a 12-layer bidirectional transducer encoder as a feature extractor.
After training the named entity recognition model, when determining at least one first condition corresponding to the target electronic medical record to be processed, inputting text content of the target electronic medical record into the named entity recognition model to perform named entity recognition on the text content to obtain at least one named entity, displaying the at least one named entity, and determining a target named entity according to a selection instruction of the named entity in the at least one named entity, wherein the target named entity is the at least one first condition corresponding to the target electronic medical record. In practical application, the user can select at least one first condition corresponding to the target electronic medical record of the first disease type from the displayed at least one named entity according to experience.
And S103, matching a second disease type corresponding to the history electronic medical record in the preset history electronic medical record library with the first disease type, and determining matched candidate history electronic medical records to obtain a candidate history electronic medical record set.
In the embodiment of the present disclosure, the history electronic medical record library is preset to obtain an electronic medical record library with index information after processing the history electronic medical record. Based on this, in the embodiment of the present disclosure, before step S103, the method may further include a step of building a preset history electronic medical record library, as shown in fig. 2, where the building of the preset history electronic medical record library may include the following steps S1031 to S1037:
s1031, obtaining a plurality of historical electronic medical records.
The plurality of history electronic medical records used for constructing the preset history electronic medical record library may be electronic medical records containing processing results corresponding to the to-be-processed operations, for example, the plurality of history electronic medical records may be electronic medical records containing a treatment plan, in consideration of the subsequent need to obtain relevant processing results of the target electronic medical record based on analysis of the plurality of history electronic medical records.
S1033, determining a second disease type and at least one second disease corresponding to each of the plurality of historical electronic medical records.
Wherein the second condition may be any condition corresponding to the associated disease type, preferably the second condition may be a critical or important condition corresponding to the associated disease type.
The method for determining the disease type and the disease symptoms of each historical electronic medical record in the plurality of historical electronic medical records is similar to that of a target electronic medical record to be processed, and the text content of each historical electronic medical record can be obtained; classifying the text content based on the trained disease classification model to obtain a second disease type of each historical electronic medical record; and carrying out named entity recognition on the text content based on the trained named entity recognition model to obtain at least one named entity corresponding to each historical electronic medical record, displaying the at least one named entity, and determining a target named entity according to a selection instruction of the named entity in the at least one named entity, wherein the target named entity is the second disorder of the corresponding historical electronic medical record. As shown in fig. 2, in view of the fact that there is a strong correlation between the critical condition or important condition and the disease type, in order to improve the recommendation efficiency and recommendation accuracy of the electronic medical record, it is preferable to determine at least one second critical condition of the historical electronic medical record based on the named entity recognition model.
S1035, taking the second disease type and at least one second disease as index information corresponding to the historical electronic medical record, and establishing a mapping relation between the historical electronic medical record and the index information.
S1037, creating the preset history electronic medical record library according to the mapping relation.
Through the processing, each history electronic medical record in the preset history electronic medical record library is provided with index information corresponding to the history electronic medical record, the index information comprises the disease type and at least one second disease of the history electronic medical record, namely, the index information reflects the association relation between the disease type and the disease, and matching is carried out only according to the index information when the electronic medical record is recommended subsequently, so that the matching efficiency is improved.
Based on the above description, when the step S103 is performed, the matching of the disease types may be performed based on the index information, the index information of the second disease type having the same type as the first disease type may be determined, and the candidate index information may be obtained, and generally, a plurality of candidate index information may be screened, where the history electronic medical records corresponding to the plurality of candidate index information form a candidate history electronic medical record set, for example, the candidate history electronic medical record set is represented as { C' 1 ,C′ 2 ,……,C′ q And (c) wherein q represents the number of candidate historic electronic medical records in the set of candidate historic electronic medical records. In a specific implementation, the first disease type and the second disease type can be converted into type vectors through the word embedding network, and matching is performed in a type vector mode, so that matching efficiency is improved.
S105, determining the disorder similarity corresponding to the candidate historical electronic medical record according to the similarity between at least one second disorder corresponding to the candidate historical electronic medical record and the at least one first disorder in the candidate historical electronic medical record set.
It can be understood that, in step S103, a plurality of candidate index information is obtained, and each candidate index information includes a corresponding second condition, so that the similarity calculation in step S105 is substantially performed directly based on the plurality of candidate index information, and the obtained condition similarity can also be understood as the condition similarity corresponding to the candidate index information.
In a specific embodiment, step S105 may include the steps of:
(1) And mapping at least one first disorder into first word vectors respectively to obtain a first word vector set. Specifically, the first condition may be mapped to a first word vector by a word embedding network such as word2vec, respectively, e.g., the first set of word vectors is represented as { s } 1 ,s 2 ,……,s m And m represents the number of first word vectors in the first word vector set, namely the number of first symptoms.
(2) And mapping at least one second condition corresponding to the candidate history electronic medical records into a second word vector for each candidate history electronic medical record in the candidate history electronic medical records respectively to obtain a second word vector set. Specifically, the second condition included in each candidate index information may be mapped to a second word vector through word embedding network word2vec, for example, candidate history electronic medical record C' 1 The corresponding second set of word vectors is denoted as { C' 1 |s′ 1 ,s′ 2 ,……,s′ n N represents a candidate historic electronic medical record C' 1 Number of corresponding second disorders.
(3) And calculating the similarity between the first word vector in the first word vector set and the second word vector in the second word vector set to obtain a similarity set corresponding to the candidate historical electronic medical record.
In a specific embodiment, for the second word vector set of each candidate historical electronic medical record, the second word vector in the second word vector set can be traversed, in the traversing process, the similarity between the second word vector and each first word vector in the first word vector set is calculated, then the largest similarity is selected as the similarity corresponding to the current second word vector, and after the traversing is finished, the similarity set corresponding to each candidate historical electronic medical record can be obtained.
For example, the second set of word vectors is { C' 1 |s′ 1 ,s′ 2 ,……,s′ n The first word vector set is { s } 1 ,s 2 ,……,s m Traversal { C' 1 |s′ 1 ,s′ 2 ,……,s′ n Each second word vector in }, e.g. for the second word vector s' 1 Calculate s' 1 And { s } 1 ,s 2 ,……,s m Similarity of each first word vector in the sequence of "sim (s' 1 ,s i ) I= … … m, and then the maximum similarity is chosen as s' 1 Corresponding similarity, i.ei= … … m, and so on, for s' 2 ……s′ n Respectively execute the similar s' 1 To obtain candidate historical electronic medical record C 'by similarity calculation' 1 For example expressed as +.>
It should be noted that, in the embodiment of the present disclosure, the similarity between two word vectors may be, but not limited to, characterized by a cosine included angle, a euclidean distance, and a manhattan distance, and may further include other quantized values capable of characterizing the similarity degree of the two word vectors in practical applications. Taking cosine included angle as an example to represent the similarity between two word vectors, the calculation formula is as follows:
(4) Calculating the average value of the similarity in the similarity set to obtain average similarity; and the average similarity is used as the disease similarity corresponding to the candidate historical electronic medical records. For example, for a similarity setIts average similarity- >The average similarity->Namely, candidate history electronic medical record C' 1 Disorder similarity of (c).
It can be appreciated that the foregoing is only an example of calculating the similarity of the symptoms of the candidate historical electronic medical records, and other methods may be used in practical applications as required.
And S107, determining at least one candidate historical electronic medical record, of which the disease similarity meets a preset condition, in the candidate historical electronic medical record set as a historical electronic medical record to be recommended, and displaying the historical electronic medical record to be recommended.
It can be understood that in the actual implementation process of step S107, at least one candidate index information that the disorder similarity satisfies the preset condition is obtained first, and then at least one candidate historical electronic medical record that the disorder similarity satisfies the preset condition is obtained based on the mapping relationship between the index information and the historical electronic medical record.
The preset conditions can be set according to actual needs, and the condition similarity can be set to be larger than the preset condition similarity; the preset condition may also be set to be a candidate historical electronic medical record corresponding to a preset arrangement position in the candidate historical electronic medical records arranged according to the disorder similarity sequence, where the sequence arrangement may include a descending sequence arrangement or an ascending sequence arrangement.
Based on the above description, determining at least one candidate historical electronic medical record in the candidate historical electronic medical record set, in which the disorder similarity satisfies the preset condition, as the historical electronic medical record to be recommended may include: sequentially arranging the candidate historical electronic medical records according to the disorder similarity; and acquiring candidate historical electronic medical records corresponding to the preset arrangement positions, and obtaining the historical electronic medical records to be recommended.
In a specific embodiment, the candidate historical electronic medical records can be arranged in descending order according to the disorder similarity from large to small, and then a preset number of candidate historical electronic medical records arranged in front are obtained, wherein the preset number of candidate historical electronic medical records arranged in front are the historical electronic medical records to be recommended.
In another specific embodiment, the candidate historical electronic medical records can be arranged in ascending order according to the disorder similarity from small to large, and then a preset number of candidate historical electronic medical records arranged later are obtained, and the preset number of candidate historical electronic medical records arranged later are the historical electronic medical records to be recommended.
The preset number may be set according to actual needs, and may be set to a value of 3 or 5, for example.
When the history electronic medical record duration to be recommended is displayed, the history electronic medical record duration to be recommended can be displayed in a large-to-small arrangement according to the similarity of symptoms, specifically, only index information of the history electronic medical record to be recommended can be displayed, and when a user clicks certain index information, the corresponding history electronic medical record can be displayed based on a mapping relation in a preset history electronic medical record library.
The technical scheme of the embodiment of the invention utilizes the association relation between the disease type and the symptoms, combines specific disease types and symptoms to search and similar match, realizes characteristic comparison of directionality, shortens matching time and greatly improves the recommendation efficiency of electronic medical records.
In order to improve the processing efficiency of the user on the target electronic medical record in the practical application, the embodiment of the invention also provides another electronic medical record recommending method, as shown in fig. 3, the method may further include:
s109, acquiring target text information in the history electronic medical record to be recommended.
Wherein the target text information is associated with an operation type of the operation to be processed, which may include, but is not limited to, filling out treatment advice, patient response, and the like. When the pending operation is to fill in a treatment recommendation, the target text information may include treatment plan related content in the historic electronic medical record to be recommended.
S111, word segmentation processing is carried out on the target text information, and a word set is obtained.
Specifically, word segmentation can be performed on the target text information by using a word segmentation algorithm to obtain a word set. The word segmentation algorithm may include a dictionary-based word segmentation algorithm and a statistical-based word segmentation algorithm, among others.
When word segmentation processing is carried out on target text information by adopting a word segmentation algorithm based on a dictionary, the target text information can be matched with entries stored in the dictionary, if the matching with a certain entry is successful, the word corresponding to the entry can be determined to be one word in the word set, and thus, each word in the word set corresponding to the target text information can be determined.
When word segmentation is performed by adopting a word segmentation algorithm based on statistics, the number of times of simultaneous occurrence of adjacent words in the input target text information can be counted, and it can be understood that the more the number of times of simultaneous occurrence of the adjacent words is, the higher the probability that the adjacent words can form words is, so that each word in the word set corresponding to the target text information can be determined by the counted probability that the words are adjacent to each other.
It should be noted that, in practical application, other word segmentation algorithms may be used to perform word segmentation on the target text information to obtain a corresponding word set, which is not limited in the present invention.
S113, determining target words with word frequencies exceeding a preset word frequency of words in the word set to obtain a target word set.
The term frequency refers to the frequency of occurrence of a certain term in a term set, and can be determined by the ratio of the total occurrence frequency of the certain term in the term set to the total number of terms contained in the term set. The preset word frequency can be set according to actual needs, the value range of the preset word frequency is between 0 and 1, preferably, in order to make the target words in the target word set be the high-frequency words in the word set, the value range of the preset word frequency can be set to be between 0.5 and 1.0, for example, can be set to be 0.8 and the like.
S115, establishing an association relationship between the target word set and the target electronic medical record.
It can be appreciated that steps S109 to S115 may be performed before the history electronic medical record to be recommended is displayed, or may be performed after the history electronic medical record to be recommended is displayed.
By establishing the association relation between the target word set and the target electronic medical record, the high-frequency words appearing in the existing treatment scheme can be associated with the target electronic medical record to be processed, and the high-frequency words are most likely to be adopted in the processing process of the target electronic medical record. When the processing of the high-frequency words is needed, the target word set can be displayed based on the association relation, and the user can select the interested target words according to the needs, so that the processing efficiency of the user on the target electronic medical record is improved, and the standardization of the processing of the electronic medical record is facilitated.
Based on the above description, in another specific embodiment, as shown in fig. 4, after step S107, the method may further include:
s117, responding to target processing of the target electronic medical record, and acquiring the target word set according to the association relation.
Wherein the target treatment may include, but is not limited to, filling out a treatment recommendation.
And S119, displaying the target words in the target word set.
S121, responding to a selection instruction of the target word, and determining the word of interest.
When the user is interested in one or more displayed target words, the user can click on the target words, the terminal receives a selection instruction of the corresponding target words, and the clicked target words are determined to be interested words.
S123, generating a processing result of the target processing according to the interested words.
In one specific example, the target process is to fill out a treatment suggestion, and the corresponding process result is a suggested text containing the term of interest.
According to the embodiment of the invention, the association relation between the high-frequency vocabulary in the target text information and the target electronic medical record to be processed is established through processing the target text information in the history electronic medical record to be recommended, and the automatic generation of the target processing result is realized based on the association relation.
Corresponding to the electronic medical record recommending method provided by the above embodiments, the embodiment of the present invention further provides an electronic medical record recommending device, and since the electronic medical record recommending device provided by the embodiment of the present invention corresponds to the electronic medical record recommending method provided by the above embodiments, the implementation of the foregoing electronic medical record recommending method is also applicable to the electronic medical record recommending device provided by the embodiment, and will not be described in detail in the embodiment.
Referring to fig. 5, a schematic structural diagram of an electronic medical record recommendation device provided by an embodiment of the present invention is shown, where the device has a function of implementing the electronic medical record recommendation method in the above method embodiment, and the function may be implemented by hardware or implemented by executing corresponding software by hardware. As shown in fig. 5, the apparatus may include:
a first determining module 510, configured to determine a first disease type and at least one first condition corresponding to a target electronic medical record to be processed;
a second determining module 520, configured to match a second disease type corresponding to a history electronic medical record in a preset history electronic medical record library with the first disease type, determine a candidate history electronic medical record that is matched, and obtain a candidate history electronic medical record set;
A third determining module 530, configured to determine a disorder similarity corresponding to the candidate historical electronic medical record according to a similarity between at least one second disorder corresponding to the candidate historical electronic medical record in the candidate historical electronic medical record set and the at least one first disorder;
and a recommending module 540, configured to determine at least one candidate historical electronic medical record in the candidate historical electronic medical record set, where the disorder similarity meets a preset condition, as a historical electronic medical record to be recommended, and display the historical electronic medical record to be recommended.
As an optional implementation manner, the apparatus further includes a construction module for constructing the preset history electronic medical record library, where the construction module may include:
the first acquisition module is used for acquiring a plurality of historical electronic medical records;
a fourth determining module, configured to determine a second disease type and at least one second condition corresponding to each of the plurality of historical electronic medical records;
a first relation establishing module, configured to establish a mapping relation between the historical electronic medical record and index information, where the index information corresponds to the historical electronic medical record and the second disease type and at least one second disease;
And the creation module is used for creating the preset history electronic medical record library according to the mapping relation.
As an alternative embodiment, the first determining module 510 may include:
the second acquisition module is used for acquiring text content of the target electronic medical record to be processed;
the classification processing module is used for carrying out classification processing on the text content based on a disease classification model to obtain a first disease type corresponding to the target electronic medical record;
the named entity recognition module is used for carrying out named entity recognition on the text content based on a named entity recognition model to obtain at least one named entity;
the first display module is used for displaying the at least one named entity;
and a fifth determining module, configured to determine a target named entity according to a selection instruction for a named entity in the at least one named entity, where the target named entity is used as the at least one first disorder.
As an alternative embodiment, the third determining module 530 may include:
the first mapping module is used for mapping the at least one first disorder into first word vectors respectively to obtain a first word vector set;
the second mapping module is used for mapping at least one second condition corresponding to each candidate history electronic medical record in the candidate history electronic medical records into a second word vector to obtain a second word vector set;
The first calculation module is used for calculating the similarity between the first word vector in the first word vector set and the second word vector in the second word vector set to obtain a similarity set corresponding to the candidate historical electronic medical record;
the second calculation module is used for calculating the average value of the similarity in the similarity set to obtain average similarity; and the average similarity is used as the disease similarity corresponding to the candidate historical electronic medical records.
As an alternative embodiment, the recommendation module 540 may include:
the arrangement module is used for sequentially arranging the candidate historical electronic medical records according to the disorder similarity;
and the third acquisition module is used for acquiring the candidate historical electronic medical records of the preset arrangement positions to obtain the historical electronic medical records to be recommended.
As an alternative embodiment, another electronic medical record recommendation apparatus as provided in fig. 6, the apparatus may further include:
a fourth obtaining module 550, configured to obtain target text information in the history electronic medical record to be recommended;
the word segmentation processing module 560 is configured to perform word segmentation processing on the target text information to obtain a word set;
a sixth determining module 570, configured to determine a target word whose word frequency of the words in the word set exceeds a preset word frequency, to obtain a target word set;
And a second relationship establishing module 580, configured to establish an association relationship between the target word set and the target electronic medical record.
As an alternative embodiment, with continued reference to fig. 6, the apparatus may further include:
the first response module 610 is configured to obtain the target word set according to the association relationship in response to target processing of the target electronic medical record;
a second display module 620, configured to display the target words in the target word set;
a second response module 630, configured to determine a word of interest in response to a selection instruction of the target word;
and the result generating module 640 is used for generating a processing result of the target processing according to the interesting words.
It should be noted that, in the apparatus provided in the foregoing embodiment, when implementing the functions thereof, only the division of the foregoing functional modules is used as an example, in practical application, the foregoing functional allocation may be implemented by different functional modules, that is, the internal structure of the device is divided into different functional modules, so as to implement all or part of the functions described above. In addition, the apparatus and the method embodiments provided in the foregoing embodiments belong to the same concept, and specific implementation processes of the apparatus and the method embodiments are detailed in the method embodiments and are not repeated herein.
The electronic medical record recommending device provided by the embodiment of the invention utilizes the association relation between the disease type and the symptoms, and combines specific disease types and symptoms to search and similar match, so that characteristic comparison of directionality is realized, matching time is shortened, and recommending efficiency of the electronic medical record is greatly improved.
In addition, the embodiment of the invention establishes the association relation between the high-frequency vocabulary in the target text information and the target electronic medical record to be processed through the processing of the target text information in the history electronic medical record to be recommended, realizes the automatic generation of the target processing result based on the association relation, can realize the automatic completion of simple phrases in the similar history electronic medical record when the user needs to fill in the treatment suggestion in the target electronic medical record, saves the time of manual input of the user, simultaneously avoids subjectivity of text expression and standardizes the filling of the electronic medical record.
The embodiment of the invention provides a terminal, which comprises a processor and a memory, wherein at least one instruction, at least one section of program, a code set or an instruction set is stored in the memory, and the at least one instruction, the at least one section of program, the code set or the instruction set is loaded and executed by the processor to realize the electronic medical record recommending method provided by the embodiment of the method.
The memory may be used to store software programs and modules that the processor executes to perform various functional applications and electronic medical record recommendations by running the software programs and modules stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for functions, and the like; the storage data area may store data created according to the use of the device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide access to the memory by the processor.
The method embodiments provided by the embodiments of the present invention may be executed in a computer terminal, a server, or similar computing device. Taking the operation on a terminal as an example, fig. 7 is a block diagram of a hardware structure of a terminal for operating an electronic medical record recommendation method according to an embodiment of the present invention, specifically:
the terminal can include RF (Radio Frequency) circuitry 710, memory 720 including one or more computer-readable storage media, input unit 730, display unit 740, sensor 750, audio circuitry 760, wiFi (wireless fidelity ) module 770, processor 780 including one or more processing cores, and power supply 790, among other components. It will be appreciated by those skilled in the art that the terminal structure shown in fig. 7 is not limiting of the terminal and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components. Wherein:
The RF circuit 710 may be used for receiving and transmitting signals during a message or a call, and in particular, after receiving downlink information of a base station, the downlink information is processed by one or more processors 780; in addition, data relating to uplink is transmitted to the base station. Typically, RF circuitry 710 includes, but is not limited to, an antenna, at least one amplifier, a tuner, one or more oscillators, a Subscriber Identity Module (SIM) card, a transceiver, a coupler, an LNA (Low Noise Amplifier ), a duplexer, and the like. In addition, the RF circuit 710 may also communicate with networks and other terminals through wireless communication. The wireless communication may use any communication standard or protocol including, but not limited to, GSM (Global System of Mobile communication, global system for mobile communications), GPRS (General Packet Radio Service ), CDMA (Code Division Multiple Access, code division multiple access), WCDMA (Wideband Code Division Multiple Access ), LTE (Long Term Evolution, long term evolution), email, SMS (Short Messaging Service, short message service), and the like.
The memory 720 may be used to store software programs and modules, and the processor 780 may perform various functional applications and data processing by executing the software programs and modules stored in the memory 720. The memory 720 may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for functions, and the like; the storage data area may store data created according to the use of the terminal, etc. In addition, memory 720 may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, memory 720 may also include a memory controller to provide access to memory 720 by processor 780 and input unit 730.
The input unit 730 may be used to receive input numeric or character information and to generate keyboard, mouse, joystick, optical or trackball signal inputs related to user settings and function control. In particular, the input unit 730 may include a touch-sensitive surface 731 and other input devices 732. The touch-sensitive surface 731, also referred to as a touch display screen or touch pad, may collect touch operations thereon or thereabout by a user (e.g., operations of the user on or thereabout the touch-sensitive surface 731 using any suitable object or accessory such as a finger, stylus, etc.), and actuate the corresponding connection device according to a pre-set program. Alternatively, touch-sensitive surface 731 may comprise two parts, a touch-detecting device and a touch controller. The touch detection device detects the touch azimuth of a user, detects a signal brought by touch operation and transmits the signal to the touch controller; the touch controller receives touch information from the touch detection device and converts it into touch point coordinates, which are then sent to the processor 780, and can receive commands from the processor 780 and execute them. In addition, the touch sensitive surface 731 may be implemented in a variety of types, such as resistive, capacitive, infrared, and surface acoustic waves. In addition to the touch-sensitive surface 731, the input unit 730 may also include other input devices 732. In particular, the other input devices 732 may include, but are not limited to, one or more of a physical keyboard, function keys (e.g., volume control keys, switch keys, etc.), a trackball, mouse, joystick, etc.
The display unit 740 may be used to display information input by a user or information provided to the user and various graphic user interfaces of the terminal, which may be composed of graphics, text, icons, video, and any combination thereof. The display unit 740 may include a display panel 741, and alternatively, the display panel 741 may be configured in the form of an LCD (Liquid Crystal Display ), an OLED (Organic Light-Emitting Diode), or the like. Further, the touch-sensitive surface 731 may overlay the display panel 741, and upon detection of a touch operation thereon or thereabout by the touch-sensitive surface 731, the touch-sensitive surface 731 is passed to the processor 780 for determining the type of touch event, and the processor 780 then provides a corresponding visual output on the display panel 741 based on the type of touch event. Wherein the touch-sensitive surface 731 and the display panel 741 may be two separate components for input and input functions, but in some embodiments the touch-sensitive surface 731 may be integrated with the display panel 741 for input and output functions.
The terminal may also include at least one sensor 750, such as a light sensor, a motion sensor, and other sensors. In particular, the light sensor may include an ambient light sensor that may adjust the brightness of the display panel 741 according to the brightness of ambient light and a proximity sensor that may turn off the display panel 741 and/or the backlight when the terminal is moved to the ear. As one of the motion sensors, the gravity acceleration sensor can detect the acceleration in all directions (generally three axes), and can detect the gravity and the direction when the device is stationary, and the device can be used for applications of recognizing the gesture of a terminal (such as horizontal and vertical screen switching, related games, magnetometer gesture calibration), vibration recognition related functions (such as pedometer and knocking), and the like; other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that may be configured for the terminal are not described in detail herein.
Audio circuitry 760, speaker 761, microphone 762 may provide an audio interface between a user and the terminal. The audio circuit 760 may transmit the received electrical signal converted from audio data to the speaker 761, and the electrical signal is converted into a sound signal by the speaker 761 to be output; on the other hand, microphone 762 converts the collected sound signals into electrical signals, which are received by audio circuit 760 and converted into audio data, which are processed by audio data output processor 780 for transmission to, for example, another terminal via RF circuit 710, or which are output to memory 720 for further processing. Audio circuitry 760 may also include an ear bud jack to provide communication between a peripheral ear bud and the terminal.
WiFi belongs to a short-distance wireless transmission technology, and the terminal can help a user to send and receive e-mails, browse web pages, access streaming media and the like through the WiFi module 770, so that wireless broadband Internet access is provided for the user. Although fig. 7 shows a WiFi module 770, it is understood that it does not belong to the essential constitution of the terminal, and may be omitted entirely as needed within the scope of not changing the essence of the invention.
The processor 780 is a control center of the terminal, connects various parts of the entire terminal using various interfaces and lines, and performs various functions of the terminal and processes data by running or executing software programs and/or modules stored in the memory 720 and calling data stored in the memory 720, thereby performing overall monitoring of the terminal. Optionally, the processor 780 may include one or more processing cores; preferably, the processor 780 may integrate an application processor that primarily processes operating systems, user interfaces, applications, etc., with a modem processor that primarily processes wireless communications. It will be appreciated that the modem processor described above may not be integrated into the processor 780.
The terminal also includes a power supply 790 (e.g., a battery) for powering the various components, which may preferably be logically connected to the processor 780 through a power management system, such as to provide for the management of charge, discharge, and power consumption by the power management system. Power supply 790 may also include one or more of any components, such as a dc or ac power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like.
Although not shown, the terminal may further include a camera, a bluetooth module, etc., which will not be described herein. In particular, in this embodiment, the terminal further includes a memory, and one or more programs, where the one or more programs are stored in the memory and configured to be executed by the one or more processors. The one or more programs include instructions for performing the electronic medical record recommendation provided by the method embodiments.
Embodiments of the present invention further provide a computer readable storage medium, where the storage medium may be configured in a terminal to store at least one instruction, at least one program, a code set, or an instruction set related to implementing an electronic medical record recommendation method, where the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by the processor to implement the electronic medical record recommendation method provided in the foregoing method embodiments.
Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
It should be noted that: the sequence of the embodiments of the present invention is only for description, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this specification. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments in part.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.

Claims (10)

1. An electronic medical record recommendation method, which is characterized by comprising the following steps:
determining a first disease type and at least one first disease corresponding to a target electronic medical record to be processed based on a preset model;
matching a second disease type corresponding to the history electronic medical record in a preset history electronic medical record library with the first disease type, and determining matched candidate history electronic medical records based on a mapping relation between the history electronic medical record and index information to obtain a candidate history electronic medical record set; the index information includes the second disease type and at least one second condition of the historical electronic medical record; each history electronic medical record of the preset history electronic medical record library is provided with corresponding index information;
Determining the disorder similarity corresponding to the candidate historical electronic medical record according to the similarity between the at least one second disorder corresponding to the candidate historical electronic medical record and the at least one first disorder in the candidate historical electronic medical record set;
determining at least one candidate index information of the candidate historical electronic medical records, wherein the disease similarity of the candidate historical electronic medical records meets a preset condition, determining at least one candidate historical electronic medical record corresponding to the at least one candidate index information as a historical electronic medical record to be recommended, and displaying the historical electronic medical record to be recommended.
2. The electronic medical record recommendation method according to claim 1, further comprising constructing the preset history electronic medical record library; the constructing the preset history electronic medical record library comprises the following steps:
acquiring a plurality of historical electronic medical records;
determining a second disease type and at least one second condition corresponding to each of the plurality of historical electronic medical records;
taking the second disease type and at least one second disease as the index information corresponding to the historical electronic medical record, and establishing a mapping relation between the historical electronic medical record and the index information;
And creating the preset history electronic medical record library according to the mapping relation.
3. The electronic medical record recommendation method according to claim 1, wherein the preset model includes a disease classification model and a named entity recognition model, and the determining a first disease type and at least one first condition corresponding to the target electronic medical record to be processed includes:
acquiring text content of a target electronic medical record to be processed;
classifying the text content based on the disease classification model to obtain a first disease type corresponding to the target electronic medical record;
carrying out named entity recognition on the text content based on the named entity recognition model to obtain at least one named entity;
displaying the at least one named entity;
and determining a target named entity according to the selection instruction of the named entity in the at least one named entity, wherein the target named entity is used as the at least one first disorder.
4. The electronic medical record recommendation method according to claim 1, wherein determining the disorder similarity corresponding to the candidate historical electronic medical record according to the similarity between the at least one second disorder corresponding to the candidate historical electronic medical record and the at least one first disorder in the candidate historical electronic medical record set comprises:
Mapping the at least one first disorder into first word vectors respectively to obtain a first word vector set;
for each candidate historical electronic medical record in the candidate historical electronic medical record sets, mapping at least one second condition corresponding to the candidate historical electronic medical record into a second word vector to obtain a second word vector set; calculating the similarity between the first word vector in the first word vector set and the second word vector in the second word vector set to obtain a similarity set corresponding to the candidate historical electronic medical record;
calculating the average value of the similarity in the similarity set to obtain average similarity; and the average similarity is used as the disease similarity corresponding to the candidate historical electronic medical records.
5. The electronic medical record recommendation method according to claim 1, wherein the determining at least one candidate historical electronic medical record in the candidate historical electronic medical records set for which the disorder similarity satisfies a preset condition as a historical electronic medical record to be recommended comprises:
sequentially arranging the candidate historical electronic medical records according to the disorder similarity;
and acquiring candidate historical electronic medical records corresponding to the preset arrangement positions, and obtaining the historical electronic medical records to be recommended.
6. The electronic medical record recommendation method according to claim 1, further comprising: acquiring target text information in the history electronic medical record to be recommended;
word segmentation processing is carried out on the target text information to obtain a word set;
determining target words with word frequencies exceeding a preset word frequency in the word set to obtain a target word set;
and establishing an association relation between the target word set and the target electronic medical record.
7. The electronic medical record recommendation method according to claim 6, wherein after the presenting the historical electronic medical record to be recommended, the method further comprises:
responding to target processing of the target electronic medical record, and acquiring the target word set according to the association relation; displaying the target words in the target word set;
determining a word of interest in response to a selection instruction of the target word;
and generating a processing result of the target processing according to the interesting words.
8. An electronic medical record recommendation device, the device comprising:
the first determining module is used for determining a first disease type and at least one first disease corresponding to the target electronic medical record to be processed based on a preset model;
The second determining module is used for matching a second disease type corresponding to the historical electronic medical record in the preset historical electronic medical record library with the first disease type, and determining matched candidate historical electronic medical records based on the mapping relation between the historical electronic medical record and the index information to obtain a candidate historical electronic medical record set; the index information includes the second disease type and at least one second condition of the historical electronic medical record; each history electronic medical record of the preset history electronic medical record library is provided with corresponding index information;
a third determining module, configured to determine a condition similarity corresponding to the candidate historical electronic medical record according to a similarity between the at least one second condition and the at least one first condition corresponding to the candidate historical electronic medical record in the candidate historical electronic medical record set;
and the recommending module is used for determining at least one candidate index information, in which the disorder similarity meets a preset condition, in the candidate historical electronic medical records, determining at least one candidate historical electronic medical record corresponding to the at least one candidate index information as a historical electronic medical record to be recommended, and displaying the historical electronic medical record to be recommended.
9. A terminal comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a set of codes, or a set of instructions, the at least one instruction, the at least one program, the set of codes, or the set of instructions being loaded and executed by the processor to implement the electronic medical record recommendation method of any one of claims 1-7.
10. A computer readable storage medium having stored therein at least one instruction, at least one program, code set, or instruction set, the at least one instruction, the at least one program, the code set, or instruction set being loaded and executed by a processor to implement the electronic medical record recommendation method of any one of claims 1-7.
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