CN112435745A - Consultation strategy recommendation method and device, electronic equipment and storage medium - Google Patents
Consultation strategy recommendation method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention relates to intelligent medical treatment and provides a method and a device for recommending a treatment strategy, electronic equipment and a storage medium. The method can extract the patient identification code and the diagnosis information, when the patient identification code does not exist in the configuration library, the disease prediction model is used for detecting the predicted disease corresponding to the diagnosis information, when the predicted disease is detected to be a chronic disease, abnormal medical data is extracted, the abnormal medical data is input into the diagnosis strategy recommendation model to obtain the target diagnosis strategy, the recommendation mode is determined, and the target diagnosis strategy is recommended in the recommendation mode. The invention can not only improve the determination efficiency and recommendation efficiency of the target treatment strategy, but also improve the accuracy of the target treatment strategy. In addition, the invention also relates to a block chain technology, and the target diagnosis strategy can be stored in the block chain.
Description
Technical Field
The invention relates to the technical field of intelligent medical treatment, in particular to a method and a device for recommending a treatment strategy, electronic equipment and a storage medium.
Background
At present, in a traditional doctor-patient communication mode, a patient needs to go to a hospital to communicate with a doctor in a face-to-face disease state, and the doctor obtains a diagnosis strategy for the patient and recommends the diagnosis strategy to the patient by referring and reasoning according to the disease state of the patient and through examination results, self experience or existing data in an expert database. However, the foregoing method performs inference analysis on the patient information in an artificial manner, which results in inefficient determination of the treatment strategy, and thus the treatment strategy cannot be recommended quickly.
Disclosure of Invention
In view of the foregoing, it is desirable to provide a method, an apparatus, an electronic device and a storage medium for recommending a treatment strategy, which can not only improve the determination efficiency and recommendation efficiency of a target treatment strategy, but also improve the accuracy of the target treatment strategy.
On one hand, the invention provides a consultation strategy recommendation method, which comprises the following steps:
extracting a patient identification code and diagnosis information from the patient information when the patient information is received;
detecting the presence of the patient identifier in a configuration repository;
when the patient identification code does not exist in the configuration library, detecting a predicted disease corresponding to the diagnosis information by using a pre-trained disease prediction model, and detecting whether the predicted disease is a chronic disease;
extracting abnormal medical data from the diagnostic information when the predicted disease is detected as the chronic disease;
inputting the abnormal medical data into a pre-trained clinic strategy recommendation model to obtain a target clinic strategy corresponding to the predicted disease;
and determining a recommendation mode according to the target visit strategy, and recommending the target visit strategy in the recommendation mode.
According to a preferred embodiment of the present invention, the extracting the patient identification code and the diagnosis information from the patient information comprises:
acquiring a first preset label and a second preset label from a configuration label table, wherein the first preset label is used for indicating an identification code, and the second preset label is used for indicating diagnosis and treatment information;
acquiring information corresponding to the first preset label from the patient information as the patient identification code;
and acquiring information corresponding to the second preset label from the patient information as the diagnosis information.
According to a preferred embodiment of the present invention, said detecting whether said patient identifier is present in said configuration repository comprises:
traversing the identification codes in the configuration library;
comparing the traversed identifier with the patient identifier;
when detecting that the patient identification code is the same as any identification code in the traversed identification codes, determining that the patient identification code exists in the configuration library; or
And when the patient identification code is detected to be different from the traversed identification code, determining that the patient identification code does not exist in the configuration library.
According to a preferred embodiment of the present invention, the detecting the predicted disease corresponding to the diagnosis information by using the pre-trained disease prediction model includes:
performing word segmentation processing on the diagnosis information to obtain diagnosis word segmentation;
vectorizing the diagnosis word segmentation to obtain a word segmentation vector;
inputting the word segmentation vectors into the disease prediction model to obtain the output probability of each output label and the transition probability of each output label, wherein the output labels are used for indicating diseases;
carrying out weighting and operation on each output probability and each transition probability to obtain the target probability of each output label;
and determining the output label with the highest target probability as the predicted disease.
According to a preferred embodiment of the present invention, the performing a word segmentation process on the diagnosis information to obtain a diagnosis word segmentation includes:
segmenting the diagnostic information by using a preset dictionary to obtain a plurality of segmentation sequences, wherein each segmentation sequence comprises segmentation participles obtained after segmenting the diagnostic information;
acquiring the weight of all segmentation participles in each segmentation sequence from the preset dictionary, and determining the segmentation weight of each segmentation sequence according to the acquired weight;
and determining the segmentation sequence with the highest segmentation weight as a target sequence, and determining all segmentation participles in the target sequence as the diagnosis participles.
According to a preferred embodiment of the invention, the method further comprises:
when the patient identification code exists in the configuration library, acquiring historical diagnosis information corresponding to the patient identification code from the configuration library, and acquiring the age and the sex of the patient corresponding to the patient identification code from the configuration library;
drawing an index trend graph according to the historical diagnosis information and the diagnosis information, and acquiring an index interval value corresponding to the age and the sex of the patient;
analyzing the index trend graph according to the index interval value to obtain an analysis result;
and when the analysis result is a target result, acquiring a diagnosis strategy of the patient identification code, and storing the diagnosis strategy, wherein the target result indicates that the index trend graph tends to the index interval value.
According to the preferred embodiment of the present invention, the determining the recommendation manner according to the target visit policy includes:
determining a visit frequency of the target visit strategy;
and determining the recommendation mode according to the visit frequency.
In another aspect, the present invention further provides a visit policy recommendation device, including:
an extraction unit for extracting a patient identification code and diagnosis information from patient information when the patient information is received;
the detection unit is used for detecting whether the patient identification code exists in a configuration library or not;
the detection unit is further configured to detect a predicted disease corresponding to the diagnosis information by using a pre-trained disease prediction model when the patient identifier does not exist in the configuration library, and detect whether the predicted disease is a chronic disease;
the extracting unit is further used for extracting abnormal medical data from the diagnosis information when the predicted disease is detected to be the chronic disease;
the input unit is used for inputting the abnormal medical data into a pre-trained clinic strategy recommendation model to obtain a target clinic strategy corresponding to the predicted disease;
and the recommending unit is used for determining a recommending mode according to the target seeing-eye strategy and recommending the target seeing-eye strategy in the recommending mode.
In another aspect, the present invention further provides an electronic device, including:
a memory storing computer readable instructions; and
a processor executing computer readable instructions stored in the memory to implement the visit policy recommendation method.
In another aspect, the present invention further provides a computer-readable storage medium, in which computer-readable instructions are stored, and the computer-readable instructions are executed by a processor in an electronic device to implement the visit policy recommendation method.
According to the technical scheme, the predicted disease can be accurately determined by using the disease prediction model, and because a large amount of inspection data does not need to be analyzed manually, the determination efficiency of the predicted disease can be improved, abnormal medical data related to the predicted disease can be extracted from the diagnosis information, the data range can be reduced, the analysis efficiency of the medical data can be improved, and the determination efficiency of a diagnosis strategy can be improved. The invention is also applied to intelligent medical scenes, thereby promoting the construction of intelligent cities.
Drawings
Fig. 1 is a flow chart of a preferred embodiment of the present invention.
FIG. 2 is a flow chart of one embodiment of the present invention for determining predicted disease.
FIG. 3 is a flow diagram of one embodiment of determining diagnostic tokens.
Fig. 4 is a functional block diagram of a preferred embodiment of the present invention.
Fig. 5 is a schematic structural diagram of an electronic device implementing a method for recommending a visit policy according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in detail with reference to the accompanying drawings and specific embodiments.
Fig. 1 is a flow chart of a preferred embodiment of the present invention of a consultation strategy recommendation method. The order of the steps in the flow chart may be changed and some steps may be omitted according to different needs.
The consultation strategy recommendation method is applied to a smart medical scene, so that the construction of a smart city is promoted. The consultation strategy recommendation method is applied to one or more electronic devices, wherein the electronic devices are devices capable of automatically performing numerical calculation and/or information processing according to computer readable instructions which are set or stored in advance, and the hardware of the electronic devices comprises but is not limited to a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device and the like.
The electronic device may be any electronic product capable of performing human-computer interaction with a user, for example, a Personal computer, a tablet computer, a smart phone, a Personal Digital Assistant (PDA), a game machine, an interactive Internet Protocol Television (IPTV), a smart wearable device, and the like.
The electronic device may include a network device and/or a user device. Wherein the network device includes, but is not limited to, a single network electronic device, an electronic device group consisting of a plurality of network electronic devices, or a Cloud Computing (Cloud Computing) based Cloud consisting of a large number of hosts or network electronic devices.
The network in which the electronic device is located includes, but is not limited to: the internet, a wide area Network, a metropolitan area Network, a local area Network, a Virtual Private Network (VPN), and the like.
S10, when the patient information is received, extracting the patient identification code and the diagnosis information from the patient information.
In at least one embodiment of the present invention, the patient information includes, but is not limited to: the patient identification code, the diagnostic information, etc.
Further, the patient identification code refers to a number that can uniquely identify the patient at visit. The patient identification code can be the identity card number of the patient, the social security number of the patient, and the mobile phone number of the patient.
Further, the diagnostic information includes, but is not limited to: blood pressure value, blood sugar value, etc.
In at least one embodiment of the present invention, the electronic device extracting the patient identification code and the diagnosis information from the patient information comprises:
acquiring a first preset label and a second preset label from a configuration label table, wherein the first preset label is used for indicating an identification code, and the second preset label is used for indicating diagnosis and treatment information;
acquiring information corresponding to the first preset label from the patient information as the patient identification code;
and acquiring information corresponding to the second preset label from the patient information as the diagnosis information.
By the embodiment, the patient identification code and the diagnosis information can be accurately acquired.
S11, detecting whether the patient identification code exists in the configuration library.
In at least one embodiment of the invention, the configuration repository stores identification codes for a plurality of patients that have been treated.
In at least one embodiment of the invention, the electronic device detecting whether the patient identification code is present in a configuration repository comprises:
traversing the identification codes in the configuration library;
comparing the traversed identifier with the patient identifier;
when detecting that the patient identification code is the same as any identification code in the traversed identification codes, determining that the patient identification code exists in the configuration library; or
And when the patient identification code is detected to be different from the traversed identification code, determining that the patient identification code does not exist in the configuration library.
By comparing the traversed identification codes with the patient identification codes one by one, whether the patient identification codes exist in the configuration library can be accurately determined.
And S12, when the patient identification code does not exist in the configuration library, detecting the predicted disease corresponding to the diagnosis information by using a pre-trained disease prediction model, and detecting whether the predicted disease is a chronic disease.
In at least one embodiment of the present invention, before detecting the predicted disease corresponding to the diagnosis information by using the pre-trained disease prediction model, the method further comprises:
acquiring historical medical information by adopting a web crawler technology, wherein the historical medical information comprises clinical data and confirmed diseases;
dividing the historical medical information to obtain a training data set and a verification data set;
training historical medical information in the training data set to obtain a disease detection learner;
and adjusting the disease detection learner according to the historical medical information in the verification data set to obtain the disease prediction model.
Specifically, the step of dividing the historical medical information by the electronic device to obtain a training data set and a verification data set includes:
randomly dividing the historical medical information into a plurality of data packets according to a preset proportion;
and determining any one data packet in the plurality of data packets as the verification data set, determining the rest data packets as the training data set, and repeating the steps until all the data packets are sequentially determined as the verification data set.
The preset ratio can be set by user, and the invention is not limited.
With the above embodiment, each piece of data in the historical medical information is involved in training and verification, thereby improving the fitness for training the disease prediction model.
Specifically, the electronic device trains the historical medical information in the training data set by using a deep learning algorithm to obtain the disease detection learner, and since the generation of the disease detection learner belongs to the prior art, the invention is not described in this specification.
Specifically, the adjusting, by the electronic device, the disease detection learner according to the historical medical information in the verification data set to obtain the disease prediction model includes:
determining an optimal hyper-parameter point from the verification data set by adopting a hyper-parameter grid searching method;
and adjusting the disease detection learner by using the optimal hyper-parameter point to obtain the disease prediction model.
Through the embodiment, the disease prediction model can predict the predicted disease more accurately.
Specifically, the electronic device determining an optimal hyper-parameter point from the verification data set by using a hyper-parameter grid search method includes:
splitting the verification data set according to a fixed step length to obtain a target subset, traversing the parameters of end points at two ends of the target subset, verifying the disease detection learner through the parameters of the end points at the two ends to obtain the learning rate of each parameter, determining the parameter with the best learning rate as a first hyper-parameter point, reducing the step length in the neighborhood of the first hyper-parameter point, and continuing traversal until the step length is a preset step length, namely the obtained hyper-parameter point is the optimal hyper-parameter point.
The preset step length is not limited by the invention.
In at least one embodiment of the present invention, the diagnostic information can be analyzed by the disease prediction model, which in turn can lead to the predicted disease.
In at least one embodiment of the present invention, the chronic disease is a chronic non-infectious disease, not specifically a disease, but rather a general name of a disease that is latent in onset, long in course, persistent in disease, lack of evidence of exact etiology of infectious organisms, complex in etiology, and has not been completely identified. The common chronic diseases mainly include cardiovascular and cerebrovascular diseases, cancers, diabetes, chronic respiratory diseases and the like.
Referring to fig. 2, fig. 2 is a flow chart of one embodiment of the present invention for determining a predicted disease. In at least one embodiment of the present invention, the electronic device detecting the predicted disease corresponding to the diagnosis information by using a pre-trained disease prediction model includes:
and S120, performing word segmentation processing on the diagnosis information to obtain diagnosis word segmentation.
The diagnosis segmentation is a word obtained by segmenting the diagnosis information.
And S121, vectorizing the diagnosis participles to obtain participle vectors.
Wherein the segmentation vector refers to a vector corresponding to the diagnostic segmentation.
And S122, inputting the word segmentation vectors into the disease prediction model to obtain the output probability of each output label and the transition probability of each output label, wherein the output labels are used for indicating diseases.
The disease prediction model is a prediction model obtained by pre-training, and is used for predicting diseases.
And S123, performing weighted sum operation on each output probability and each transition probability to obtain the target probability of each output label.
For example: the output probability is 0.3, the weight of the output probability is 0.8, the transition probability is 0.4, and the weight of the transition probability is 0.2, and the target probability is 0.32 after calculation.
And S124, determining the output label with the highest target probability as the predicted disease.
The predicted disease can be accurately determined by the output probability and the transition probability output in the disease prediction model.
Referring to fig. 3, fig. 3 is a flow chart of one embodiment of determining diagnostic tokens according to the present invention. Specifically, the performing the word segmentation processing on the diagnosis information to obtain the diagnosis word segmentation includes:
s1200, segmenting the diagnostic information by using a preset dictionary to obtain a plurality of segmentation sequences, wherein each segmentation sequence comprises segmentation participles obtained after the diagnostic information is segmented.
The preset dictionary comprises a plurality of user-defined words and a weight value of each user-defined word.
S1201, obtaining the weight of all segmentation participles in each segmentation sequence from the preset dictionary, and determining the segmentation weight of each segmentation sequence according to the obtained weight.
Wherein each segmentation sequence comprises all words in the diagnostic information.
S1202, determining the segmentation sequence with the highest segmentation weight as a target sequence, and determining all segmentation participles in the target sequence as the diagnosis participles.
Through the implementation, diagnosis participles which accord with an application scene can be accurately segmented, and the predicted diseases can be accurately determined.
In at least one embodiment of the invention, the method further comprises:
when the patient identification code exists in the configuration library, acquiring historical diagnosis information corresponding to the patient identification code from the configuration library, and acquiring the age and the sex of the patient corresponding to the patient identification code from the configuration library;
drawing an index trend graph according to the historical diagnosis information and the diagnosis information, and acquiring an index interval value corresponding to the age and the sex of the patient;
analyzing the index trend graph according to the index interval value to obtain an analysis result;
and when the analysis result is a target result, acquiring a diagnosis strategy of the patient identification code, and storing the diagnosis strategy, wherein the target result indicates that the index trend graph tends to the index interval value.
With the above embodiment, since the analysis result tends to the index interval value, it can be determined that the diagnosis policy is valid, and the diagnosis policy is stored, so that the diagnosis policy can be recommended to patients with the same diagnosis disease.
In at least one embodiment of the invention, the electronic device detecting whether the predicted disease is a chronic disease comprises:
traversing all chronic diseases in a preset chronic disease table;
comparing the predicted disease to the traversed chronic disease;
determining the predicted disease as the chronic disease when any chronic disease exists in the traversed chronic disease.
Through the embodiment, whether the predicted disease is the chronic disease can be accurately determined.
S13, extracting abnormal medical data from the diagnosis information when the predicted disease is detected as the chronic disease.
In at least one embodiment of the present invention, the abnormal medical data refers to data in the diagnostic information that does not match a normal value.
In at least one embodiment of the present invention, the manner in which the electronic device extracts the abnormal medical data from the diagnosis information belongs to the prior art, and the present invention does not describe the manner in which the abnormal medical data is extracted.
And S14, inputting the abnormal medical data into a pre-trained clinic strategy recommendation model to obtain a target clinic strategy corresponding to the predicted disease.
In at least one embodiment of the present invention, the target visit strategy refers to a treatment strategy for the predicted disease.
In at least one embodiment of the present invention, before the abnormal medical data is input into the pre-trained visit strategy recommendation model to obtain the target visit strategy corresponding to the predicted disease, the method further includes:
acquiring historical data by adopting a web crawler technology;
inputting the historical data into a forgetting gate layer to carry out forgetting processing to obtain training data;
dividing the training data into a training set and a verification set by adopting a cross verification method;
inputting the data in the training set into an input gate layer for training to obtain a learner;
and adjusting the learner according to the data in the verification set to obtain the diagnosis strategy recommendation model.
And adjusting the learner through the data in the verification set, so that the visit strategy recommendation model can be accurately obtained.
And S15, determining a recommendation mode according to the target visit strategy, and recommending the target visit strategy in the recommendation mode.
It is emphasized that the target visit strategy may also be stored in a node of a blockchain in order to further ensure privacy and security of the target visit strategy.
In at least one embodiment of the present invention, the recommendation method may include, but is not limited to: frequency of recommendations, time of recommendations, etc.
In at least one embodiment of the present invention, the determining, by the electronic device, a recommendation manner according to the target visit policy includes:
determining a visit frequency of the target visit strategy;
and determining the recommendation mode according to the visit frequency.
For example, the target visit strategy is to take diuretic antihypertensive drugs once a day, one at a time, and the recommended mode is to push every day.
Through the embodiment, the appropriate recommendation mode can be quickly determined.
According to the technical scheme, the predicted disease can be accurately determined by using the disease prediction model, and because a large amount of inspection data does not need to be analyzed manually, the determination efficiency of the predicted disease can be improved, abnormal medical data related to the predicted disease can be extracted from the diagnosis information, the data range can be reduced, the analysis efficiency of the medical data can be improved, and the determination efficiency of a diagnosis strategy can be improved. The invention is also applied to intelligent medical scenes, thereby promoting the construction of intelligent cities.
Fig. 4 is a functional block diagram of a preferred embodiment of the visit policy recommendation apparatus according to the present invention. The visit strategy recommendation device 11 includes an extraction unit 110, a detection unit 111, an input unit 112, a recommendation unit 113, an acquisition unit 114, a drawing unit 115, an analysis unit 116, a division unit 117, an adjustment unit 118, and a training unit 119. The module/unit referred to herein is a series of computer readable instruction segments that can be accessed by the processor 13 and perform a fixed function and that are stored in the memory 12. In the present embodiment, the functions of the modules/units will be described in detail in the following embodiments.
When patient information is received, the extraction unit 110 extracts a patient identification code and diagnosis information from the patient information.
In at least one embodiment of the present invention, the patient information includes, but is not limited to: the patient identification code, the diagnostic information, etc.
Further, the patient identification code refers to a number that can uniquely identify the patient at visit. The patient identification code can be the identity card number of the patient, the social security number of the patient, and the mobile phone number of the patient.
Further, the diagnostic information includes, but is not limited to: blood pressure value, blood sugar value, etc.
In at least one embodiment of the present invention, the extracting unit 110 extracts the patient identification code and the diagnosis information from the patient information includes:
acquiring a first preset label and a second preset label from a configuration label table, wherein the first preset label is used for indicating an identification code, and the second preset label is used for indicating diagnosis and treatment information;
acquiring information corresponding to the first preset label from the patient information as the patient identification code;
and acquiring information corresponding to the second preset label from the patient information as the diagnosis information.
By the embodiment, the patient identification code and the diagnosis information can be accurately acquired.
The detection unit 111 detects whether the patient identification code is present in the configuration repository.
In at least one embodiment of the invention, the configuration repository stores identification codes for a plurality of patients that have been treated.
In at least one embodiment of the present invention, the detecting unit 111 detecting whether the patient identification code exists in the configuration library comprises:
traversing the identification codes in the configuration library;
comparing the traversed identifier with the patient identifier;
when detecting that the patient identification code is the same as any identification code in the traversed identification codes, determining that the patient identification code exists in the configuration library; or
And when the patient identification code is detected to be different from the traversed identification code, determining that the patient identification code does not exist in the configuration library.
By comparing the traversed identification codes with the patient identification codes one by one, whether the patient identification codes exist in the configuration library can be accurately determined.
When the patient identification code does not exist in the configuration library, the detection unit 111 detects a predicted disease corresponding to the diagnosis information by using a pre-trained disease prediction model, and detects whether the predicted disease is a chronic disease.
In at least one embodiment of the present invention, before detecting a predicted disease corresponding to the diagnosis information by using a pre-trained disease prediction model, the obtaining unit 114 obtains historical medical information by using a web crawler technology, where the historical medical information includes clinical data and a diagnosed disease;
the dividing unit 117 divides the historical medical information to obtain a training data set and a verification data set;
the training unit 119 trains the historical medical information in the training data set to obtain a disease detection learner;
the adjusting unit 118 adjusts the disease detection learner according to the historical medical information in the verification data set, so as to obtain the disease prediction model.
Specifically, the dividing unit 117 divides the historical medical information to obtain a training data set and a verification data set, and includes:
randomly dividing the historical medical information into a plurality of data packets according to a preset proportion;
and determining any one data packet in the plurality of data packets as the verification data set, determining the rest data packets as the training data set, and repeating the steps until all the data packets are sequentially determined as the verification data set.
The preset ratio can be set by user, and the invention is not limited.
With the above embodiment, each piece of data in the historical medical information is involved in training and verification, thereby improving the fitness for training the disease prediction model.
Specifically, the training unit 119 trains the historical medical information in the training data set by using a deep learning algorithm to obtain the disease detection learner, which is not described in the present invention since the generation of the disease detection learner belongs to the prior art.
Specifically, the adjusting unit 118 adjusts the disease detection learner according to the historical medical information in the verification data set, and obtaining the disease prediction model includes:
determining an optimal hyper-parameter point from the verification data set by adopting a hyper-parameter grid searching method;
and adjusting the disease detection learner by using the optimal hyper-parameter point to obtain the disease prediction model.
Through the embodiment, the disease prediction model can predict the predicted disease more accurately.
Specifically, the determining, by the adjusting unit 118, an optimal hyper-parameter point from the verification data set by using a hyper-parameter grid search method includes:
splitting the verification data set according to a fixed step length to obtain a target subset, traversing the parameters of end points at two ends of the target subset, verifying the disease detection learner through the parameters of the end points at the two ends to obtain the learning rate of each parameter, determining the parameter with the best learning rate as a first hyper-parameter point, reducing the step length in the neighborhood of the first hyper-parameter point, and continuing traversal until the step length is a preset step length, namely the obtained hyper-parameter point is the optimal hyper-parameter point.
The preset step length is not limited by the invention.
In at least one embodiment of the present invention, the diagnostic information can be analyzed by the disease prediction model, which in turn can lead to the predicted disease.
In at least one embodiment of the present invention, the chronic disease is a chronic non-infectious disease, not specifically a disease, but rather a general name of a disease that is latent in onset, long in course, persistent in disease, lack of evidence of exact etiology of infectious organisms, complex in etiology, and has not been completely identified. The common chronic diseases mainly include cardiovascular and cerebrovascular diseases, cancers, diabetes, chronic respiratory diseases and the like.
In at least one embodiment of the present invention, the detecting unit 111, using a disease prediction model trained in advance, to detect a predicted disease corresponding to the diagnosis information includes:
and performing word segmentation processing on the diagnosis information to obtain diagnosis word segmentation.
The diagnosis segmentation is a word obtained by segmenting the diagnosis information.
Vectorizing the diagnosis word segmentation to obtain a word segmentation vector.
Wherein the segmentation vector refers to a vector corresponding to the diagnostic segmentation.
And inputting the word segmentation vectors into the disease prediction model to obtain the output probability of each output label and the transition probability of each output label, wherein the output labels are used for indicating diseases.
The disease prediction model is a prediction model obtained by pre-training, and is used for predicting diseases.
And carrying out weighted sum operation on each output probability and each transition probability to obtain the target probability of each output label.
For example: the output probability is 0.3, the weight of the output probability is 0.8, the transition probability is 0.4, and the weight of the transition probability is 0.2, and the target probability is 0.32 after calculation.
And determining the output label with the highest target probability as the predicted disease.
The predicted disease can be accurately determined by the output probability and the transition probability output in the disease prediction model.
Specifically, the detecting unit 111 performs word segmentation processing on the diagnosis information, and obtaining diagnosis words includes:
and segmenting the diagnostic information by using a preset dictionary to obtain a plurality of segmentation sequences, wherein each segmentation sequence comprises segmentation participles obtained after the diagnostic information is segmented.
The preset dictionary comprises a plurality of user-defined words and a weight value of each user-defined word.
And acquiring the weight of all segmentation participles in each segmentation sequence from the preset dictionary, and determining the segmentation weight of each segmentation sequence according to the acquired weight.
Wherein each segmentation sequence comprises all words in the diagnostic information.
And determining the segmentation sequence with the highest segmentation weight as a target sequence, and determining all segmentation participles in the target sequence as the diagnosis participles.
Through the implementation, diagnosis participles which accord with an application scene can be accurately segmented, and the predicted diseases can be accurately determined.
In at least one embodiment of the present invention, when the patient identifier exists in the configuration library, the obtaining unit 114 obtains the historical diagnosis information corresponding to the patient identifier from the configuration library, and obtains the patient age and the patient sex corresponding to the patient identifier from the configuration library;
the drawing unit draws an index trend graph according to the historical diagnosis information and the diagnosis information and acquires an index interval value corresponding to the age and the sex of the patient;
the analysis unit 116 analyzes the index trend graph according to the index interval value to obtain an analysis result;
when the analysis result is a target result indicating that the index trend graph tends toward the index interval value, the acquisition unit 114 acquires the diagnosis policy of the patient identification code and stores the diagnosis policy.
With the above embodiment, since the analysis result tends to the index interval value, it can be determined that the diagnosis policy is valid, and the diagnosis policy is stored, so that the diagnosis policy can be recommended to patients with the same diagnosis disease.
In at least one embodiment of the present invention, the detecting unit 111 detects whether the predicted disease is a chronic disease includes:
traversing all chronic diseases in a preset chronic disease table;
comparing the predicted disease to the traversed chronic disease;
determining the predicted disease as the chronic disease when any chronic disease exists in the traversed chronic disease.
Through the embodiment, whether the predicted disease is the chronic disease can be accurately determined.
When it is detected that the predicted disease is the chronic disease, the extraction unit 110 extracts abnormal medical data from the diagnostic information.
In at least one embodiment of the present invention, the abnormal medical data refers to data in the diagnostic information that does not match a normal value.
In at least one embodiment of the present invention, the manner of extracting the abnormal medical data from the diagnosis information by the extracting unit 110 is in the prior art, and the extracting manner of the abnormal medical data is not described in the present invention.
The input unit 112 inputs the abnormal medical data into a pre-trained clinic strategy recommendation model to obtain a target clinic strategy corresponding to the predicted disease.
In at least one embodiment of the present invention, the target visit strategy refers to a treatment strategy for the predicted disease.
In at least one embodiment of the present invention, before the abnormal medical data is input into the pre-trained visit strategy recommendation model to obtain the target visit strategy corresponding to the predicted disease, the method further includes:
the acquiring unit 114 acquires historical data by using a web crawler technology;
the input unit 112 inputs the historical data into a forgetting gate layer to perform forgetting processing, so as to obtain training data;
the dividing unit 117 divides the training data into a training set and a verification set by using a cross-validation method;
the input unit 112 inputs the data in the training set to an input gate layer for training to obtain a learner;
the adjusting unit 118 adjusts the learner according to the data in the verification set, so as to obtain the visit policy recommendation model.
And adjusting the learner through the data in the verification set, so that the visit strategy recommendation model can be accurately obtained.
The recommending unit 113 determines a recommending manner according to the target seeing-eye strategy, and recommends the target seeing-eye strategy in the recommending manner.
It is emphasized that the target visit strategy may also be stored in a node of a blockchain in order to further ensure privacy and security of the target visit strategy.
In at least one embodiment of the present invention, the recommendation method may include, but is not limited to: frequency of recommendations, time of recommendations, etc.
In at least one embodiment of the present invention, the determining, by the recommending unit 113, a recommending method according to the target visit policy includes:
determining a visit frequency of the target visit strategy;
and determining the recommendation mode according to the visit frequency.
For example, the target visit strategy is to take diuretic antihypertensive drugs once a day, one at a time, and the recommended mode is to push every day.
Through the embodiment, the appropriate recommendation mode can be quickly determined.
According to the technical scheme, the predicted disease can be accurately determined by using the disease prediction model, and because a large amount of inspection data does not need to be analyzed manually, the determination efficiency of the predicted disease can be improved, abnormal medical data related to the predicted disease can be extracted from the diagnosis information, the data range can be reduced, the analysis efficiency of the medical data can be improved, and the determination efficiency of a diagnosis strategy can be improved. The invention is also applied to intelligent medical scenes, thereby promoting the construction of intelligent cities.
Fig. 5 is a schematic structural diagram of an electronic device implementing a method for recommending a visit policy according to a preferred embodiment of the present invention.
In one embodiment of the present invention, the electronic device 1 includes, but is not limited to, a memory 12, a processor 13, and computer readable instructions, such as a visit policy recommendation program, stored in the memory 12 and executable on the processor 13.
It will be appreciated by a person skilled in the art that the schematic diagram is only an example of the electronic device 1 and does not constitute a limitation of the electronic device 1, and that it may comprise more or less components than shown, or some components may be combined, or different components, e.g. the electronic device 1 may further comprise an input output device, a network access device, a bus, etc.
The Processor 13 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. The processor 13 is an operation core and a control center of the electronic device 1, and is connected to each part of the whole electronic device 1 by various interfaces and lines, and executes an operating system of the electronic device 1 and various installed application programs, program codes, and the like.
Illustratively, the computer readable instructions may be partitioned into one or more modules/units that are stored in the memory 12 and executed by the processor 13 to implement the present invention. The one or more modules/units may be a series of computer readable instruction segments capable of performing specific functions, which are used for describing the execution process of the computer readable instructions in the electronic device 1. For example, the computer-readable instructions may be divided into an extraction unit 110, a detection unit 111, an input unit 112, a recommendation unit 113, an acquisition unit 114, a rendering unit 115, an analysis unit 116, a division unit 117, an adjustment unit 118, and a training unit 119.
The memory 12 may be used for storing the computer readable instructions and/or modules, and the processor 13 implements various functions of the electronic device 1 by executing or executing the computer readable instructions and/or modules stored in the memory 12 and invoking data stored in the memory 12. The memory 12 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data created according to use of the electronic device, and the like. The memory 12 may include non-volatile and volatile memories, such as: a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other storage device.
The memory 12 may be an external memory and/or an internal memory of the electronic device 1. Further, the memory 12 may be a memory having a physical form, such as a memory stick, a TF Card (Trans-flash Card), or the like.
The integrated modules/units of the electronic device 1 may be stored in a computer-readable storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow of the method according to the above embodiments may be implemented by hardware that is configured to be instructed by computer readable instructions, which may be stored in a computer readable storage medium, and when the computer readable instructions are executed by a processor, the steps of the method embodiments may be implemented.
Wherein the computer readable instructions comprise computer readable instruction code which may be in source code form, object code form, an executable file or some intermediate form, and the like. The computer-readable medium may include: any entity or device capable of carrying said computer readable instruction code, recording medium, U-disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM).
The block chain 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.
With reference to fig. 1, the memory 12 in the electronic device 1 stores computer-readable instructions to implement a visit policy recommendation method, and the processor 13 can execute the computer-readable instructions to implement:
extracting a patient identification code and diagnosis information from the patient information when the patient information is received;
detecting the presence of the patient identifier in a configuration repository;
when the patient identification code does not exist in the configuration library, detecting a predicted disease corresponding to the diagnosis information by using a pre-trained disease prediction model, and detecting whether the predicted disease is a chronic disease;
extracting abnormal medical data from the diagnostic information when the predicted disease is detected as the chronic disease;
inputting the abnormal medical data into a pre-trained clinic strategy recommendation model to obtain a target clinic strategy corresponding to the predicted disease;
and determining a recommendation mode according to the target visit strategy, and recommending the target visit strategy in the recommendation mode.
Specifically, the processor 13 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer readable instructions, which is not described herein again.
In the embodiments provided in the present invention, it should be understood that the disclosed system, 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 computer readable storage medium has computer readable instructions stored thereon, wherein the computer readable instructions when executed by the processor 13 are configured to implement the steps of:
extracting a patient identification code and diagnosis information from the patient information when the patient information is received;
detecting the presence of the patient identifier in a configuration repository;
when the patient identification code does not exist in the configuration library, detecting a predicted disease corresponding to the diagnosis information by using a pre-trained disease prediction model, and detecting whether the predicted disease is a chronic disease;
extracting abnormal medical data from the diagnostic information when the predicted disease is detected as the chronic disease;
inputting the abnormal medical data into a pre-trained clinic strategy recommendation model to obtain a target clinic strategy corresponding to the predicted disease;
and determining a recommendation mode according to the target visit strategy, and recommending the target visit strategy in the recommendation mode.
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 invention 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.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention 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 signs in the claims shall not be construed as limiting the claim concerned.
Furthermore, it is obvious that the word "comprising" does not exclude other elements or steps, and the singular does not exclude the plural. A plurality of units or means recited in the system claims may also be implemented by one unit or means in software or hardware. The terms second, etc. are used to denote names, but not any particular order.
Finally, it should be noted that the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting, and although the present invention 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 may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. A consultation strategy recommendation method is characterized by comprising the following steps:
extracting a patient identification code and diagnosis information from the patient information when the patient information is received;
detecting the presence of the patient identifier in a configuration repository;
when the patient identification code does not exist in the configuration library, detecting a predicted disease corresponding to the diagnosis information by using a pre-trained disease prediction model, and detecting whether the predicted disease is a chronic disease;
extracting abnormal medical data from the diagnostic information when the predicted disease is detected as the chronic disease;
inputting the abnormal medical data into a pre-trained clinic strategy recommendation model to obtain a target clinic strategy corresponding to the predicted disease;
and determining a recommendation mode according to the target visit strategy, and recommending the target visit strategy in the recommendation mode.
2. The visit strategy recommendation method of claim 1 wherein said extracting patient identification codes and diagnostic information from said patient information comprises:
acquiring a first preset label and a second preset label from a configuration label table, wherein the first preset label is used for indicating an identification code, and the second preset label is used for indicating diagnosis and treatment information;
acquiring information corresponding to the first preset label from the patient information as the patient identification code;
and acquiring information corresponding to the second preset label from the patient information as the diagnosis information.
3. The visit strategy recommendation method of claim 1 wherein said detecting the presence of said patient identification code in said configuration repository comprises:
traversing the identification codes in the configuration library;
comparing the traversed identifier with the patient identifier;
when detecting that the patient identification code is the same as any identification code in the traversed identification codes, determining that the patient identification code exists in the configuration library; or
And when the patient identification code is detected to be different from the traversed identification code, determining that the patient identification code does not exist in the configuration library.
4. The visit strategy recommendation method of claim 1 wherein the detecting a predicted disease corresponding to the diagnostic information using a pre-trained disease prediction model comprises:
performing word segmentation processing on the diagnosis information to obtain diagnosis word segmentation;
vectorizing the diagnosis word segmentation to obtain a word segmentation vector;
inputting the word segmentation vectors into the disease prediction model to obtain the output probability of each output label and the transition probability of each output label, wherein the output labels are used for indicating diseases;
carrying out weighting and operation on each output probability and each transition probability to obtain the target probability of each output label;
and determining the output label with the highest target probability as the predicted disease.
5. The visit strategy recommendation method of claim 4, wherein the tokenizing the diagnostic information to obtain diagnostic tokenization comprises:
segmenting the diagnostic information by using a preset dictionary to obtain a plurality of segmentation sequences, wherein each segmentation sequence comprises segmentation participles obtained after segmenting the diagnostic information;
acquiring the weight of all segmentation participles in each segmentation sequence from the preset dictionary, and determining the segmentation weight of each segmentation sequence according to the acquired weight;
and determining the segmentation sequence with the highest segmentation weight as a target sequence, and determining all segmentation participles in the target sequence as the diagnosis participles.
6. The visit strategy recommendation method of claim 1, wherein the method further comprises:
when the patient identification code exists in the configuration library, acquiring historical diagnosis information corresponding to the patient identification code from the configuration library, and acquiring the age and the sex of the patient corresponding to the patient identification code from the configuration library;
drawing an index trend graph according to the historical diagnosis information and the diagnosis information, and acquiring an index interval value corresponding to the age and the sex of the patient;
analyzing the index trend graph according to the index interval value to obtain an analysis result;
and when the analysis result is a target result, acquiring a diagnosis strategy of the patient identification code, and storing the diagnosis strategy, wherein the target result indicates that the index trend graph tends to the index interval value.
7. The visit strategy recommendation method of claim 1 wherein said determining a recommendation based on said target visit strategy comprises:
determining a visit frequency of the target visit strategy;
and determining the recommendation mode according to the visit frequency.
8. A visit policy recommendation device, characterized in that the visit policy recommendation device comprises:
an extraction unit for extracting a patient identification code and diagnosis information from patient information when the patient information is received;
the detection unit is used for detecting whether the patient identification code exists in a configuration library or not;
the detection unit is further configured to detect a predicted disease corresponding to the diagnosis information by using a pre-trained disease prediction model when the patient identifier does not exist in the configuration library, and detect whether the predicted disease is a chronic disease;
the extracting unit is further used for extracting abnormal medical data from the diagnosis information when the predicted disease is detected to be the chronic disease;
the input unit is used for inputting the abnormal medical data into a pre-trained clinic strategy recommendation model to obtain a target clinic strategy corresponding to the predicted disease;
and the recommending unit is used for determining a recommending mode according to the target seeing-eye strategy and recommending the target seeing-eye strategy in the recommending mode.
9. An electronic device, characterized in that the electronic device comprises:
a memory storing computer readable instructions; and
a processor executing computer readable instructions stored in the memory to implement the visit policy recommendation method of any one of claims 1 to 7.
10. A computer-readable storage medium characterized by: the computer readable storage medium has stored therein computer readable instructions for execution by a processor in an electronic device to implement the visit policy recommendation method as claimed in any one of claims 1 to 7.
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