CN113782125A

CN113782125A - Clinic scoring method and device based on artificial intelligence, electronic equipment and medium

Info

Publication number: CN113782125A
Application number: CN202111095015.7A
Authority: CN
Inventors: 陈朝海
Original assignee: Ping An International Smart City Technology Co Ltd
Current assignee: Shenzhen Ping An Smart Healthcare Technology Co ltd
Priority date: 2021-09-17
Filing date: 2021-09-17
Publication date: 2021-12-10
Anticipated expiration: 2041-09-17
Also published as: CN113782125B

Abstract

The application relates to the technical field of artificial intelligence, and provides a clinic scoring method, device, electronic equipment and medium based on artificial intelligence, wherein the method comprises the following steps: extracting evaluation words from the user comment text of the target clinic based on the natural language processing model to obtain the evaluation words; determining a score value based on the evaluation weight value tree, and calculating a first score value according to the score value; obtaining diagnostic record data based on the diagnostic data of the target clinic, and determining a second score value based on the diagnostic record data, wherein the diagnostic record data comprises the disease condition of the patient and the medication of the patient corresponding to the disease condition of the patient; determining a plurality of environment dimensions corresponding to the hospitalizing environment data based on the hospitalizing environment data of the target clinic; determining a third scoring value according to a plurality of environment dimensions; calculating a fusion score value based on the first score value, the second score value and the third score value; based on the fused score values, the score of the target clinic is determined. The application improves efficiency and accuracy of scoring the clinics.

Description

Clinic scoring method and device based on artificial intelligence, electronic equipment and medium

Technical Field

The application relates to the technical field of artificial intelligence, in particular to a clinic scoring method and device based on artificial intelligence, electronic equipment and a medium.

Background

At present, the quantity of large and small clinics in China is huge, but medical hardware conditions of each clinic and technical levels of medical staff are not uniform, more authoritative evaluation standards and comprehensive authentication systems do not exist in the aspects of medical hardware facilities, operation capacity, operation flow, medical quality and the like of the clinics, and people have no objective channel to know the real medical level of the clinics when selecting the clinics to see the doctor.

Currently, the scoring of the clinics is usually accomplished by manual review, such as obtaining self-scoring data obtained by self-scoring through filling out a questionnaire by the relevant personnel of the clinics, performing authentication according to the self-scoring data, or obtaining the scores of the clinics corresponding to a plurality of patients on a line, and determining the scores of the clinics based on the scores of the patients. However, manual review cannot make flexible comparative analysis between different clinics, and the accuracy of the questionnaire filled by the relevant personnel of the clinic is difficult to guarantee, so that the real medical level of the clinic cannot be accurately scored, and meanwhile, the score of the clinic is determined based on the score of the patient, and the dimensionality is single, so that the determined score cannot represent the real medical level of the clinic.

Disclosure of Invention

In view of the above, there is a need for an artificial intelligence based clinic scoring method, apparatus, electronic device and medium, which can score the medical level of a clinic from multiple dimensions based on artificial intelligence, and improve efficiency and accuracy of scoring the clinic.

In a first aspect, the present application provides an artificial intelligence based clinic scoring method, the method comprising:

acquiring a user comment text of a target clinic, and extracting evaluation words from the user comment text based on a trained natural language processing model to obtain evaluation words;

determining a score value corresponding to the evaluation word based on a preset evaluation weight tree, and calculating a first score value corresponding to the target clinic according to the score value;

obtaining diagnosis record data of a patient based on the diagnosis data of the target clinic, and determining a second scoring value based on the diagnosis record data, wherein the diagnosis record data comprises a patient disease and patient medication corresponding to the patient disease;

determining a plurality of environment dimensions corresponding to the hospitalizing environment data based on the hospitalizing environment data of the target clinic; determining a third scoring value according to the plurality of environment dimensions;

calculating a fused score value corresponding to the target clinic based on the first score value, the second score value and the third score value;

determining a score for the target clinic based on the fused score value.

According to an optional embodiment of the present application, the extracting evaluation words from the user comment text based on the trained natural language processing model to obtain the evaluation words includes:

a sub-word coding module based on a natural language processing model codes the user comment text to obtain a plurality of sub-words, and each sub-word is characterized through a characterization module of the natural language processing model to obtain a characterization vector corresponding to each sub-word;

determining a mark of each subword according to the characterization vector, and aligning the plurality of subwords according to each subword mark to obtain a characterization sequence;

and calculating the characterization sequence to obtain the matching degree of each sub-word, and determining an evaluation word according to the matching degree.

According to an optional implementation manner of the present application, the determining, according to the token vector, a token of each subword, and aligning the plurality of subwords according to the token of each subword to obtain a token sequence includes:

determining a characterization vector corresponding to a first sub-word as a mark of the first sub-word;

calculating a vector difference value between the characterization vector corresponding to the next subword and the characterization vector corresponding to the previous subword to obtain a mark of the next subword;

calculating the number of marks of each sub-word, and judging whether the number of the marks is preset with a threshold value or not;

when the number of the marks is larger than a preset threshold value, performing convolution operation on the marks to obtain target marks;

determining the marks as target marks when the number of the marks is equal to a preset threshold value;

and obtaining a characterization sequence according to the target marker.

According to an optional embodiment of the present application, the determining, based on a preset evaluation weight tree, a score value corresponding to the evaluation term includes:

according to the evaluation word traversal evaluation weight value tree, determining a node corresponding to the evaluation word;

determining a node level corresponding to the node and a corresponding node value;

and determining a scoring value corresponding to the evaluation word based on the node level and the node value.

According to an alternative embodiment of the present application, said determining a second score value based on said diagnostic record data comprises:

determining medication characteristic information corresponding to the medication of the patient based on the medicine information corresponding to the medication of the patient;

calculating the medication characteristic information and the patient disease state based on a cosine similarity algorithm to obtain the matching degree of the medication characteristic information and the patient disease state;

determining a disorder grade based on the patient disorder;

determining a second score value based on the degree of match and the disorder rating.

According to an optional embodiment of the present application, the determining a third score value according to the plurality of environmental dimensions comprises:

determining a plurality of environment dimensions corresponding to the hospitalizing environment data;

determining dimension information corresponding to each environment dimension from the hospitalizing environment data according to the plurality of environment dimensions;

determining a measurement unit of each environment dimension, and based on the measurement unit, performing standardization processing on dimension information corresponding to each environment dimension to obtain a standard value;

determining the dimension type of each environment dimension;

generating a fusion standard value of the hospitalizing environment data based on the dimension type and the standard value;

generating an environment curve corresponding to the hospitalizing environment data according to the fusion standard value;

and calculating the similarity between the environment curve and a preset curve, and determining a third scoring value based on the similarity.

According to an optional embodiment of the present application, the calculating the corresponding fused score value of the target clinic based on the first score value, the second score value and the third score value comprises:

constructing a scoring triangle based on the first, second, and third score values;

calculating the gravity center of the scoring triangle, and obtaining a fusion scoring coordinate corresponding to the target clinic based on the gravity center;

and determining a fusion score value corresponding to the target clinic based on the fusion score coordinates.

In a second aspect, the present application provides an artificial intelligence based clinic scoring apparatus, the apparatus comprising:

the clinic evaluation processing module is used for acquiring a user comment text of a target clinic and extracting evaluation words from the user comment text based on the trained natural language processing model to obtain the evaluation words;

the first score calculating module is used for determining a score value corresponding to the evaluation word based on a preset evaluation weight value tree and calculating a first score value corresponding to the target clinic according to the score value;

the second score calculation module is used for obtaining diagnosis record data of the patient based on the diagnosis data of the target clinic and determining a second score value based on the diagnosis record data, wherein the diagnosis record data comprises the patient diseases and the patient medicines corresponding to the patient diseases;

the third scoring calculation module is used for determining a plurality of environment dimensions corresponding to the hospitalizing environment data based on the hospitalizing environment data of the target clinic; determining a third scoring value according to the plurality of environment dimensions;

a fusion score calculation module, configured to calculate a fusion score value corresponding to the target clinic based on the first score value, the second score value, and the third score value;

a clinic score calculation module for determining the score of the target clinic based on the fused score value.

In a third aspect, the present application provides an electronic device comprising a processor and a memory, the processor being configured to implement the artificial intelligence based clinic scoring method when executing a computer program stored in the memory.

In a fourth aspect, the present application provides a computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the artificial intelligence based clinic scoring method.

In summary, according to the clinic scoring method, the clinic scoring device, the electronic device and the medium based on artificial intelligence, the user comment text of the target clinic is obtained, the evaluation words are extracted from the user comment text based on the trained natural language processing model, the evaluation words are obtained, and the natural language processing model is used for extracting the evaluation words from the user comment text, so that the efficiency and the accuracy of determining the evaluation words can be improved; then, based on a preset evaluation weight value tree, obtaining a score value corresponding to each evaluation word, calculating a first score value corresponding to the target clinic according to the score values, and traversing the evaluation weight tree to improve the accuracy and efficiency of determining the score values, so that the determined first score values are more accurate; obtaining diagnosis record data of a patient based on the diagnosis data of the target clinic, determining a second score value based on the diagnosis record data, wherein the diagnosis record data comprises a patient disease and patient medication corresponding to the patient disease, acquiring hospitalizing environment data of the target clinic, and determining a third score value based on the hospitalizing environment data; and then, calculating a fusion score value corresponding to the target clinic based on the first score value, the second score value and the third score value, determining the score of the target clinic based on the fusion score value, and obtaining the fusion score value through the score values of three dimensions, wherein the fusion score value can include richer clinic information, so that the determined score is more accurate and more in line with the actual situation, and the scoring effectiveness is improved.

Drawings

FIG. 1 is a flowchart of an artificial intelligence based clinic scoring method according to an embodiment of the present application.

Fig. 2 is a block diagram of an artificial intelligence based clinic scoring apparatus provided in the second embodiment of the present application.

Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present application.

Detailed Description

In order that the above objects, features and advantages of the present application can be more clearly understood, a detailed description of the present application will be given below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.

Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing an example in an alternative implementation and is not intended to be limiting of the present application.

The clinic scoring method based on artificial intelligence provided by the embodiment of the application is executed by the electronic equipment, and accordingly, the clinic scoring device based on artificial intelligence runs in the electronic equipment. The electronic device may include a mobile phone, a tablet computer, a notebook computer, a desktop computer, a personal digital assistant, a wearable device, and the like.

The medical image processing method and the medical image processing device can process the medical image based on an artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technique and application system that simulates, extends and expands human Intelligence using a digital computer or a machine controlled by a digital computer, senses the environment, acquires knowledge and uses the knowledge to obtain the best result.

The artificial intelligence infrastructure generally includes technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technologies, operation/interaction systems, mechatronics, and the like. The artificial intelligence software technology mainly comprises a computer vision technology, a robot technology, a biological recognition technology, a voice processing technology, a natural language processing technology, machine learning/deep learning and the like.

Example one

FIG. 1 is a flowchart of an artificial intelligence based clinic scoring method according to an embodiment of the present application. The artificial intelligence based clinic scoring method specifically comprises the following steps, and the sequence of the steps in the flow chart can be changed and some steps can be omitted according to different requirements.

And S11, obtaining the user comment text of the target clinic, and extracting the evaluation words of the user comment text based on the trained natural language processing model to obtain the evaluation words.

For example, a target website may be determined, and user evaluation text corresponding to the target clinic may be obtained at the target website. For example, web crawler technology may be used to collect user rating texts corresponding to target clinics in multiple target websites. The destination website may be a medical website. The user evaluation text corresponding to the target clinic is collected from the target website, and the collection efficiency of the user evaluation text can be improved.

The evaluation word is a word for indicating the evaluation of the user, such as good, not good, satisfactory, dissatisfied, not good, disliked, favorite, annoying, and the like. The content corresponding to the evaluation word may be set according to actual conditions, and is not limited herein.

The natural language processing model may be a BERT (bidirectional Encoder expressions from transformations) model, or a modified version of the BERT model, a RoBERTA (Robuslyoptimized BERT prediction approach) model. The RoBERTa model achieves more advanced effects by improving training tasks and data generation methods, training longer, using larger batches, using more data, etc.

In an optional embodiment, before performing comment word extraction on the user comment text based on the trained natural language processing model, the method further includes the following steps:

obtaining a user comment sample, wherein the user comment sample comprises comment words and comment labels corresponding to the comment words; training a natural language processing model based on the user comment sample, and judging whether the comment word extraction rate of the natural language processing model meets a preset expected value or not, wherein the natural language processing model is used for extracting comment words in the user comment sample; if the comment word extraction rate of the natural language processing model does not meet a preset expected value, adjusting model parameters of the natural language processing model; and iteratively training the natural language processing model by using the user comment samples until the comment word extraction rate of the natural language processing model is judged to meet a preset expected value, stopping training the natural language processing model, and obtaining the trained natural language processing model.

Illustratively, the natural language processing model may include an input layer, a hidden layer, and an output layer. The input layer is used for inputting comment texts to be recognized, such as user comment samples; the hidden layer is used for performing text recognition on the input comment text and extracting comment words in the comment text; the output layer is used for outputting the comment words.

In an optional implementation manner, the extracting evaluation words from the user comment text based on the trained natural language processing model to obtain the evaluation words includes:

Inputting a user comment text to an input layer of a trained natural language processing model, inputting the user comment text to a subword coding module by the input layer for coding, and outputting a plurality of subwords (subwords) in the user comment text through the subword coding module. The sub-word Encoding module inputs the plurality of sub-words to the representation module for further Encoding, and outputs a representation vector of each sub-word through the representation module, wherein the representation vector is used for uniquely representing the sub-word, and the representation module encodes the sub-word by adopting a Byte-Pair Encoding (BPE) technology.

The characterization module firstly maps each sub-word into a 768-dimensional word vector according to a word list; mapping the position into a 768-dimensional position vector according to the position of each subword in the user comment text; and finally, adding each element in the word vector and the position vector to be used as a characterization vector corresponding to the sub-word. The representation module can not only effectively capture longer-distance semantic dependency relationship, but also acquire bidirectional context information to generate vector representation with richer semantic information.

Because the representation module encodes each word by Byte-Pair Encoding (BPE) technology, the representation vectors generated for each subword are different, the sequence length of the representation vectors of some subwords is the same as the sequence length corresponding to the subwords, and the sequence length of the representation vectors of some subwords is longer than the sequence length corresponding to the subwords, so that the length of the coded sequence obtained based on the representation vectors of a plurality of subwords is longer than the length of the input text sequence, by calculating the token (token) of each subword and aligning the representation vectors of the plurality of subwords according to the token of each subword, the sequence length of the coded sequence of the aligned representation vectors corresponding to each subword can be the same, so that the length of the coded sequence obtained based on the representation vectors of the plurality of subwords is consistent with the length of the comment text of the user, thereby facilitating the subsequent use of the fully-connected layer to calculate the degree of match of each subword.

In an optional implementation manner, the determining, according to the token vector, a token of each subword, and performing alignment processing on the plurality of subwords according to the token of each subword to obtain a token sequence includes:

and obtaining a characterization sequence according to the target marker.

Wherein the first subword is a word located at a first position in the user comment text. The preset threshold may be set to 1, and no limitation is imposed on the preset threshold.

In some embodiments, the length of the token vector extracted through the natural language processing model is greater than the length of the user comment text, and the mark of each sub-word is calculated through the corresponding token of each sub-word, so that the token size of each sub-word in the token vector can be quickly determined, and finally, the simplification of the token of the sub-word corresponding to the mark is realized by performing convolution operation on the marks of which the number of all the marks is greater than a preset threshold value, so that the length of the token sequence obtained according to the target mark corresponding to each sub-word can be effectively ensured to be consistent with the length of the user comment text.

For example, the token sequence may be calculated based on a connection layer of the natural language processing model, and the matching degree of each subword is obtained. For example, the full-link layer may convert the sub-word representation containing the context information in the token sequence into a 2-dimensional representation, and combine the sub-words into word groups, thereby obtaining a score of whether each word group is an evaluation word. The score is used for representing the matching degree of the phrase as the evaluation word. The greater the matching degree is, the greater the possibility of indicating that the phrase is an evaluation word is, and the smaller the matching degree is, the less the possibility of indicating that the phrase is an evaluation word is.

For example, the multiple sub-words may be sorted in a reverse order based on the matching degree, and a preset number of sub-words in the multiple sub-words sorted in the reverse order may be obtained as the evaluation word.

S12, determining the score value corresponding to the evaluation word based on a preset evaluation weight value tree, and calculating a first score value corresponding to the target clinic according to the score value.

Illustratively, a plurality of evaluation words are acquired, and an evaluation grade corresponding to each evaluation word is determined, wherein the evaluation grade is used for representing the satisfaction degree of the user. For example, the higher the rating level, the higher the satisfaction degree corresponding to the user, and the lower the rating level, the lower the satisfaction degree corresponding to the user. The evaluation level corresponding to the evaluation word can be determined according to the emotional color corresponding to the evaluation word. For example, the evaluation word "best", "best bar" corresponds to a higher evaluation level than the evaluation word "worst" and "worst".

And constructing an evaluation weight value tree according to the evaluation grades corresponding to the plurality of evaluation words. The evaluation weight value tree comprises a root node and a plurality of leaf nodes, and each node comprises an evaluation word corresponding to the node and a node value corresponding to the node. The node corresponding to the evaluation word with high evaluation level is closer to the root node of the evaluation weight value tree, the higher the node level corresponding to the node is, the larger the node value corresponding to the node is; and the node corresponding to the evaluation word with low evaluation level is far away from the root node of the evaluation weight value tree, and the lower the node level corresponding to the node is, the smaller the node value corresponding to the node is.

Different weight values can be set according to node levels corresponding to different nodes. For example, the score value corresponding to the evaluation word may be determined based on the weight value of the node hierarchy corresponding to the node multiplied by the node value corresponding to the node.

For example, if there are a plurality of assessment words, a plurality of score values may be determined based on a preset assessment weight tree, and a first score value corresponding to the target clinic may be calculated based on an average value of the plurality of score values.

In an optional embodiment, the determining, based on a preset evaluation weight tree, a score value corresponding to the evaluation word includes:

Different weight values can be set according to node levels corresponding to different leaf nodes. For example, the scoring value corresponding to the evaluation word may be determined based on a weight value of a node level corresponding to a leaf node multiplied by a node value corresponding to the leaf node.

And S13, obtaining diagnosis record data of the patient based on the diagnosis data of the target clinic, and determining a second score value based on the diagnosis record data, wherein the diagnosis record data comprises the patient disease and the patient medication corresponding to the patient disease.

The diagnostic data includes diagnostic record data for the patient, such as a patient's condition and patient medication corresponding to the patient's condition, and a second score value is determined based on the diagnostic data.

In an alternative embodiment, said determining a second score value based on said diagnostic record data comprises:

determining a disorder grade based on the patient disorder;

The medication characteristic information is used for indicating the applicable symptoms corresponding to the medication of the patient. For example, a medicine database may be preset, and a mapping relationship between the medicine and the medication characteristic information is recorded in the medicine database. Querying the drug database based on the drug information may determine medication characteristic information corresponding to the drug.

Calculating the medication characteristic information and the patient disease state based on a cosine similarity algorithm to obtain the matching degree of the medication characteristic information and the patient disease state, so as to determine whether the medication of the patient is consistent with the patient disease state, wherein the higher the matching degree is, the more the medication of the patient is consistent with the patient disease state; the lower the matching degree, the more inconsistent the patient's medication and the patient's disease, the more likely a medical accident occurs. Based on the patient's condition, a corresponding condition rating for the patient is calculated, the condition rating indicating the severity of the disease, the higher the condition rating, the more refractory the disease.

Illustratively, the calculating the medication characteristic information and the patient disease state based on a cosine similarity algorithm to obtain the matching degree of the medication characteristic information and the patient disease state comprises: acquiring the representation vectors of the medication characteristic information and the patient disease state two texts, calculating cosine similarity of the two representation vectors, and determining the matching degree of the medication characteristic information and the patient disease state based on the similarity. The greater the cosine similarity, the more similar the medication characteristic information is to the patient disease condition, and the greater the matching degree of the medication characteristic information is.

The second score value may be calculated according to a weight value corresponding to the degree of matching and the disease grade. For example, the matching degree may be multiplied by a weight value corresponding to the disease level to obtain a second score value. Wherein, the weight value corresponding to the disease grade can be set according to the actual situation. For example, the higher the disease level, the smaller the corresponding weight may be set.

For example, if there is diagnostic data corresponding to a plurality of patients, a plurality of score values may be determined according to the matching degree and the disease grade, and the average value corresponding to the plurality of score values is determined as the second score value.

S14, determining a plurality of environment dimensions corresponding to the hospitalizing environment data based on the hospitalizing environment data of the target clinic; and determining a third score value according to the plurality of environmental dimensions.

The hospitalization environment data is used to represent the quality of the clinic environment and may include clinic temperature, clinic oxygen content, clinic carbon dioxide content, and clinic humidity, among others.

In an optional embodiment, the determining a third score value according to the plurality of environmental dimensions comprises:

determining the dimension type of each environment dimension;

The environmental dimensions may include a temperature dimension, an oxygen content dimension, a carbon dioxide content dimension, a humidity dimension, and the like. Determining dimension information corresponding to each environment dimension from the hospitalizing environment data, such as determining temperature measurement data corresponding to the temperature dimension, oxygen measurement data corresponding to the oxygen content dimension, carbon dioxide measurement data corresponding to the carbon dioxide content dimension, and humidity measurement data corresponding to the humidity dimension.

Based on the measurement unit corresponding to each environment dimension, the dimension information corresponding to each environment dimension is subjected to standardization processing, and the standard value is obtained by using the data of the dimension information corresponding to each environment dimension in the same format.

Different dimension types can be set with different weight values, and a fusion standard value of the hospitalizing environment data is generated based on the weight value corresponding to the dimension type and the standard value. For example, a plurality of dimension types exist, the weighted value corresponding to each dimension type is multiplied by the standard value corresponding to the dimension type to obtain a plurality of calculated values, and the average value of the plurality of calculated values is determined as the fusion standard value of the hospitalization environment data.

For example, the environment curve corresponding to the medical environment data may be generated according to a fusion standard value and a time corresponding to the fusion standard value. The time may be a date, an hour, etc.

And S15, calculating a fused score value corresponding to the target clinic based on the first score value, the second score value and the third score value.

For example, an average of the first score value, the second score value, and the third score value may be calculated, and the average may be determined as the fused score value corresponding to the target clinic.

In an optional embodiment, the calculating a fused score value corresponding to the target clinic based on the first score value, the second score value, and the third score value comprises:

For example, a first weight value corresponding to the first score value, a second weight value corresponding to the second score value, and a third weight value corresponding to the third score value may be preset. And calculating coordinates corresponding to each scoring value by using the weight corresponding to each scoring value, and constructing a scoring triangle based on the coordinates. For example, based on the first weight, the coordinate corresponding to the first score value is determined, and if a numerical value corresponding to the first score value is determined as X, a value obtained by multiplying the numerical value corresponding to the first score value by the first weight value is determined as Y, the coordinate corresponding to the first score value is obtained.

Illustratively, barycentric coordinates corresponding to the barycenter of the scoring triangle are determined, and the barycentric coordinates are determined as fusion scoring coordinates corresponding to the target clinic. For example, the value of the fusion score coordinate on the X-axis may be determined as the fusion score value corresponding to the target clinic; or calculating a distance value between the fusion scoring coordinate and the origin coordinate based on the X value and the Y value of the fusion scoring coordinate, and determining the distance value as a fusion scoring value corresponding to the target clinic.

S16, determining the score of the target clinic based on the fusion score value.

For example, the fused score value may be numerically converted, and the converted value may be determined as the score of the target clinic, and the numerical conversion may include a percent conversion, a five-point conversion, a level conversion, and the like.

According to the clinic scoring method based on artificial intelligence, the user comment text of the target clinic is obtained, the evaluation words are extracted from the user comment text based on the trained natural language processing model, the evaluation words are obtained, and the natural language processing model is used for extracting the evaluation words from the user comment text, so that the efficiency and the accuracy of determining the evaluation words can be improved; then, based on a preset evaluation weight value tree, obtaining a score value corresponding to each evaluation word, calculating a first score value corresponding to the target clinic according to the score values, and traversing the evaluation weight tree to improve the accuracy and efficiency of determining the score values, so that the determined first score values are more accurate; obtaining diagnosis record data of a patient based on the diagnosis data of the target clinic, determining a second score value based on the diagnosis record data, wherein the diagnosis record data comprises a patient disease and patient medication corresponding to the patient disease, acquiring hospitalizing environment data of the target clinic, and determining a third score value based on the hospitalizing environment data; and then, calculating a fusion score value corresponding to the target clinic based on the first score value, the second score value and the third score value, determining the score of the target clinic based on the fusion score value, and obtaining the fusion score value through the score values of three dimensions, wherein the fusion score value can include richer clinic information, so that the determined score is more accurate and more in line with the actual situation, and the scoring effectiveness is improved.

Example two

In some embodiments, the artificial intelligence based clinic scoring apparatus 20 may comprise a plurality of functional modules comprised of computer program segments. The computer programs of the various segments in artificial intelligence based clinic scoring apparatus 20 may be stored in a memory of an electronic device and executed by at least one processor to perform the functions of the artificial intelligence based clinic scoring method (described in detail in figure 1).

In this embodiment, the artificial intelligence based clinic scoring apparatus 20 may be divided into a plurality of functional modules according to the functions performed by the apparatus. The functional module may include: a clinic evaluation processing module 201, a first score calculating module 202, a second score calculating module 203, a third score calculating module 204, a fusion score calculating module 205 and a clinic score calculating module 206. A module as referred to herein is a series of computer program segments capable of being executed by at least one processor and capable of performing a fixed function and is stored in a memory. In the present embodiment, the functions of the modules will be described in detail in the following embodiments.

And the clinic evaluation processing module 201 is configured to acquire a user comment text of a target clinic, and extract an evaluation word from the user comment text based on the trained natural language processing model to obtain an evaluation word.

In an optional embodiment, the clinic evaluation processing module 201 performs evaluation word extraction on the user comment text based on the trained natural language processing model, and obtaining an evaluation word includes:

In an optional embodiment, the clinic evaluation processing module 201 determines a label of each subword according to the characterization vector, and aligns the plurality of subwords according to the label of each subword to obtain a characterization sequence includes:

and obtaining a characterization sequence according to the target marker.

Wherein the first subword is a word located at a first position in the user comment text.

The preset threshold may be set to 1, and no limitation is imposed on the preset threshold.

The first score calculating module 202 is configured to determine a score value corresponding to the evaluation word based on a preset evaluation weight value tree, and calculate a first score value corresponding to the target clinic according to the score value.

In an optional embodiment, the determining, by the first score calculating module 202, a score value corresponding to the term under evaluation based on a preset evaluation weight value tree includes:

And the second score calculation module 203 is used for obtaining diagnosis record data of the patient based on the diagnosis data of the target clinic and determining a second score value based on the diagnosis record data, wherein the diagnosis record data comprises the patient diseases and the patient medicines corresponding to the patient diseases.

In an alternative embodiment, the second score calculation module 203 determines the second score value based on the diagnostic record data comprising:

determining a disorder grade based on the patient disorder;

Calculating the medication characteristic information and the patient disease state based on a cosine similarity algorithm to obtain the matching degree of the medication characteristic information and the patient disease state, so as to determine whether the medication of the patient is consistent with the patient disease state, wherein the higher the matching degree is, the more the medication of the patient is consistent with the patient disease state; the lower the matching degree, the more inconsistent the patient's medication and the patient's disease, the more likely a medical accident occurs.

Based on the patient's condition, a corresponding condition rating for the patient is calculated, the condition rating indicating the severity of the disease, the higher the condition rating, the more refractory the disease.

A third score calculation module 204, configured to determine, based on the hospitalization environment data of the target clinic, a plurality of environment dimensions corresponding to the hospitalization environment data; and determining a third score value according to the plurality of environmental dimensions.

In an alternative embodiment, third scoring module 204 determines the third score value based on the plurality of environmental dimensions by:

determining the dimension type of each environment dimension;

A fused score calculating module 205, configured to calculate a fused score value corresponding to the target clinic based on the first score value, the second score value, and the third score value.

In an optional embodiment, calculating the fused score value corresponding to the target clinic by fused score calculating module 205 based on the first score value, the second score value and the third score value comprises:

constructing a scoring triangle based on the first, second, and third score values; calculating the gravity center of the scoring triangle, and obtaining a fusion scoring coordinate corresponding to the target clinic based on the gravity center; and determining a fusion score value corresponding to the target clinic based on the fusion score coordinates.

A clinic score calculation module 206 for determining a score for the target clinic based on the fused score value.

According to the clinic scoring device based on artificial intelligence, the user comment text of the target clinic is obtained, the evaluation words are extracted from the user comment text based on the trained natural language processing model, the evaluation words are obtained, and the natural language processing model is used for extracting the evaluation words from the user comment text, so that the efficiency and accuracy of determining the evaluation words can be improved; then, based on a preset evaluation weight value tree, obtaining a score value corresponding to each evaluation word, calculating a first score value corresponding to the target clinic according to the score values, and traversing the evaluation weight tree to improve the accuracy and efficiency of determining the score values, so that the determined first score values are more accurate; obtaining diagnosis record data of a patient based on the diagnosis data of the target clinic, determining a second score value based on the diagnosis record data, wherein the diagnosis record data comprises a patient disease and patient medication corresponding to the patient disease, acquiring hospitalizing environment data of the target clinic, and determining a third score value based on the hospitalizing environment data; and then, calculating a fusion score value corresponding to the target clinic based on the first score value, the second score value and the third score value, determining the score of the target clinic based on the fusion score value, and obtaining the fusion score value through the score values of three dimensions, wherein the fusion score value can include richer clinic information, so that the determined score is more accurate and more in line with the actual situation, and the scoring effectiveness is improved.

EXAMPLE III

The present embodiment provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps in the above-described artificial intelligence based clinic scoring method embodiments, such as S11-S16 shown in fig. 1:

s11, obtaining a user comment text of the target clinic, and extracting evaluation words from the user comment text based on the trained natural language processing model to obtain evaluation words;

s12, determining the score value corresponding to the evaluation word based on a preset evaluation weight value tree, and calculating a first score value corresponding to the target clinic according to the score value;

s13, obtaining diagnosis record data of the patient based on the diagnosis data of the target clinic, and determining a second score value based on the diagnosis record data, wherein the diagnosis record data comprises the patient disease and the patient medication corresponding to the patient disease;

s14, determining a plurality of environment dimensions corresponding to the hospitalizing environment data based on the hospitalizing environment data of the target clinic; determining a third scoring value according to the plurality of environment dimensions;

s15, calculating a fused score value corresponding to the target clinic based on the first score value, the second score value and the third score value;

Alternatively, the computer program, when executed by the processor, implements the functions of the modules/units in the above-mentioned device embodiments, for example, the

module

201 and 206 in fig. 2:

the clinic evaluation processing module 201 is used for acquiring a user comment text of a target clinic, and extracting evaluation words from the user comment text based on a trained natural language processing model to obtain evaluation words;

the first score calculating module 202 is configured to determine a score value corresponding to the evaluation word based on a preset evaluation weight value tree, and calculate a first score value corresponding to the target clinic according to the score value;

the second score calculation module 203 is configured to obtain diagnosis record data of a patient based on the diagnosis data of the target clinic, and determine a second score value based on the diagnosis record data, where the diagnosis record data includes a patient's disease and a medication amount of the patient corresponding to the patient's disease;

the third score calculation module 204 is configured to determine a plurality of environment dimensions corresponding to the hospitalization environment data based on the hospitalization environment data of the target clinic; determining a third scoring value according to the plurality of environment dimensions;

the fused score calculating module 205 is configured to calculate a fused score value corresponding to the target clinic based on the first score value, the second score value, and the third score value;

the clinic score calculation module 206 is configured to determine a score of the target clinic based on the fused score value.

Example four

Fig. 3 is a schematic structural diagram of an electronic device according to a third embodiment of the present application. In the preferred embodiment of the present application, the electronic device 3 comprises a memory 31, at least one processor 32, at least one transceiver 33, and a communication bus 34.

It will be appreciated by those skilled in the art that the configuration of the electronic device shown in fig. 3 does not constitute a limitation of the embodiments of the present application, and may be a bus-type configuration or a star-type configuration, and that the electronic device 3 may include more or less hardware or software than those shown, or a different arrangement of components.

In some embodiments, the electronic device 3 is a device capable of automatically performing numerical calculation and/or information processing according to instructions set or stored in advance, and the hardware thereof includes but is not limited to a microprocessor, an application specific integrated circuit, a programmable gate array, a digital processor, an embedded device, and the like. The electronic device 3 may also include a client device, which includes, but is not limited to, any electronic product that can interact with a client through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device, for example, a personal computer, a tablet computer, a smart phone, a digital camera, and the like.

It should be noted that the electronic device 3 is only an example, and other existing or future electronic products, such as those that can be adapted to the present application, should also be included in the scope of protection of the present application, and are included by reference.

In some embodiments, the memory 31 has stored therein a computer program that, when executed by the at least one processor 32, performs all or a portion of the steps of the artificial intelligence based clinic scoring method as described. The Memory 31 includes a Read-Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Programmable Read-Only Memory (EPROM), a One-time Programmable Read-Only Memory (OTPROM), an electronically Erasable rewritable Read-Only Memory (Electrically-Erasable Programmable Read-Only Memory (EEPROM)), an optical Read-Only disk (CD-ROM) or other optical disk Memory, a magnetic disk Memory, a tape Memory, or any other medium readable by a computer capable of carrying or storing data.

Further, the computer-readable storage medium may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function, and the like; the storage data area may store data created according to the use of the blockchain node, and the like.

The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.

In some embodiments, the at least one processor 32 is a Control Unit (Control Unit) of the electronic device 3, connects various components of the electronic device 3 by various interfaces and lines, and executes various functions and processes data of the electronic device 3 by running or executing programs or modules stored in the memory 31 and calling data stored in the memory 31. For example, the at least one processor 32, when executing the computer program stored in the memory, implements all or a portion of the steps of the artificial intelligence based clinic scoring method described in the embodiments of the present application; or implement all or part of the functionality of an artificial intelligence based clinic scoring apparatus. The at least one processor 32 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, including one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips.

In some embodiments, the at least one communication bus 34 is arranged to enable connection communication between the memory 31 and the at least one processor 32 or the like.

Although not shown, the electronic device 3 may further include a power supply (such as a battery) for supplying power to each component, and preferably, the power supply may be logically connected to the at least one processor 32 through a power management device, so as to implement functions of managing charging, discharging, and power consumption through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device 3 may further include various sensors, a bluetooth module, a Wi-Fi module, and the like, which are not described herein again.

The integrated unit implemented in the form of a software functional module may be stored in a computer-readable storage medium. The software functional module is stored in a storage medium and includes several instructions to enable a computer device (which may be a personal computer, an electronic device, or a network device) or a processor (processor) to execute parts of the methods according to the embodiments of the present application.

In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the modules is only one logical functional division, and other divisions may be realized in practice.

The modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.

In addition, functional modules in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.

It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned. Furthermore, it is obvious that the word "comprising" does not exclude other elements or that the singular does not exclude the plural. A plurality of units or means recited in the specification may also be implemented by one unit or means through software or hardware. The terms first, second, etc. are used to denote names, but not any particular order.

Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present application and not for limiting, and although the present application is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present application without departing from the spirit and scope of the technical solutions of the present application.

Claims

1. An artificial intelligence based clinic scoring method, comprising:

determining a score for the target clinic based on the fused score value.

2. The artificial intelligence based clinic scoring method according to claim 1, wherein the extracting of the evaluation terms from the user comment text based on the trained natural language processing model comprises:

3. The artificial intelligence based clinic scoring method according to claim 2, wherein the determining the label of each sub-word according to the characterization vector and aligning the plurality of sub-words according to the label of each sub-word to obtain the characterization sequence comprises:

and obtaining a characterization sequence according to the target marker.

4. The artificial intelligence based clinic scoring method according to claim 1, wherein the determining the score value corresponding to the evaluation term based on the preset evaluation weight value tree comprises:

5. The artificial intelligence based clinic scoring method according to claim 1, wherein the determining a second score value based on the diagnostic record data comprises:

determining a disorder grade based on the patient disorder;

6. The artificial intelligence based clinic scoring method according to claim 1, wherein the determining a third scoring value according to the plurality of environmental dimensions comprises:

determining the dimension type of each environment dimension;

7. The artificial intelligence based clinic scoring method according to any one of claims 1 to 6, wherein the calculating of the corresponding fused score value of the target clinic based on the first score value, the second score value and the third score value comprises:

8. An artificial intelligence based clinic scoring apparatus, the apparatus comprising:

9. An electronic device, comprising a processor and a memory, the processor being configured to implement the artificial intelligence based clinic scoring method according to any of the claims 1 to 7 when executing a computer program stored in the memory.

10. A computer-readable storage medium, having stored thereon a computer program, wherein the computer program, when executed by a processor, implements the artificial intelligence based clinic scoring method according to any of claims 1 to 7.