CN116646046B - Electronic medical record processing method and system based on Internet diagnosis and treatment - Google Patents
Electronic medical record processing method and system based on Internet diagnosis and treatment Download PDFInfo
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Abstract
The invention discloses an electronic medical record processing method and system based on internet diagnosis and treatment, which belong to the technical field of data processing, wherein the method comprises the following steps: acquiring disease condition information of a patient; extracting a structured text and a non-structured text in the illness state information; calculating the matching degree of the structured text and the matching degree of the unstructured text with each historical medical record; according to the matching degree of the structured text and the matching degree of the unstructured text, calculating the comprehensive matching degree with each historical medical record; selecting a preset number of historical medical records with highest comprehensive matching degree, and generating an alternative diagnosis and treatment scheme; selecting a diagnosis and treatment scheme; generating an electronic medical record according to the illness state information and the corresponding diagnosis and treatment scheme; desensitizing the electronic medical record; generating a private key and a public key; encrypting the desensitized electronic medical record by using the public key and uploading the electronic medical record to the cloud; receiving a consulting request initiated by a user to a cloud; and verifying the private key of the user, and displaying the desensitized electronic medical record under the condition that the identity of the user passes the verification.
Description
Technical Field
The invention belongs to the technical field of data processing, and particularly relates to an electronic medical record processing method and system based on internet diagnosis and treatment.
Background
Along with the increase of medical data, internet medical treatment rises, more and more hospitals use electronic medical records to record information and treatment data of patients, and the electronic medical records have the advantages of small storage space, long storage time, easiness in management and the like, so that the electronic medical records become an indispensable tool in modern medical services, hospitals store the electronic medical records to a cloud server to manage the electronic medical record data more conveniently and at lower cost, and users and even doctors of other hospitals can check the electronic medical records anytime and anywhere by using the internet.
In the prior art, a doctor is often required to manually input the illness state and the diagnosis conclusion of a patient on a computer screen through a keyboard. First, some physicians who are unskilled in typing are not friendly, but rather waste time of the physicians, and diagnosis efficiency is lowered. In the second place, when diagnosis is performed according to the condition of a patient, subjective judgment by a doctor is completely relied on, but there is a difference in the cognitive level of each doctor, and there is a possibility that it is difficult to accurately make judgment of the problematic condition.
Disclosure of Invention
In order to solve the technical problems that the condition of a patient and a diagnosis conclusion are completely input through a keyboard in the prior art, a doctor is not friendly to some physicians with unskilled typing, the time of the doctor is wasted, the diagnosis and treatment efficiency is reduced, the subjective judgment of the doctor is completely relied on when the diagnosis is carried out according to the condition of the patient, but the cognition level of each doctor is different, and the difficult condition is difficult to accurately judge, the invention provides the electronic medical record processing method and the system based on the Internet diagnosis and treatment.
First aspect
The invention provides an electronic medical record processing method based on internet diagnosis and treatment, which comprises the following steps:
s101: acquiring disease condition information of a patient;
s102: extracting a structured text and a non-structured text in the illness state information;
s103: calculating the matching degree of the structured text with each history medical record;
s104: calculating the matching degree of unstructured text with each history;
s105: according to the matching degree of the structured text and the unstructured text, calculating the comprehensive matching degree with each historical medical record：
Wherein,ηrepresenting structured text matchingsim a Weight coefficient of 1-ηRepresenting unstructured text matching degreesim b Weight coefficient of (2);
s106: selecting a preset number of historical medical records with highest comprehensive matching degree, and generating an alternative diagnosis and treatment scheme;
s107: pushing alternative diagnosis and treatment schemes, and selecting the diagnosis and treatment schemes according to the alternative diagnosis and treatment schemes;
s108: generating an electronic medical record according to the illness state information and the corresponding diagnosis and treatment scheme;
s109: desensitizing the electronic medical record;
s110: generating a private key and a public key;
s111: encrypting the desensitized electronic medical record by using the public key and uploading the electronic medical record to the cloud;
s112: receiving a consulting request initiated by a user to a cloud;
s113: and verifying the private key of the user, and displaying the desensitized electronic medical record under the condition that the identity of the user passes the verification.
Second aspect
The invention provides an electronic medical record transmission system based on internet medical treatment, which is used for executing an electronic medical record processing method based on internet medical treatment in a first aspect.
Compared with the prior art, the invention has at least the following beneficial technical effects:
(1) According to the invention, according to the illness state information of the patient, the history medical record with higher matching degree is automatically matched from the history medical record library, the diagnosis and treatment scheme of the history medical record with higher matching degree is used as an alternative diagnosis and treatment scheme, and a doctor can select a final diagnosis and treatment scheme according to the alternative diagnosis and treatment scheme. On the one hand, when a proper diagnosis and treatment scheme exists in the alternative diagnosis and treatment schemes, a doctor can directly select the diagnosis and treatment scheme as a final diagnosis and treatment scheme without manual typing, so that potential problems caused by manual input errors are reduced, the time of the doctor is saved, and the diagnosis and treatment efficiency is improved; on the other hand, even if the patient faces the difficult illness, the history medical record can be used as reference information, so that subjective judgment is avoided, and diagnosis and treatment accuracy is improved.
(2) According to the invention, the electronic medical record of the patient can be subjected to desensitization treatment, so that the privacy and sensitive information of the patient are protected, and the safety of data is improved.
(3) According to the invention, the desensitized electronic medical record can be encrypted by using the public key, and when the identity verification of the user passes, the user has authority to review the electronic medical record, so that the information security of the electronic medical record of the patient in the sharing process is further ensured, the privacy and sensitive information of the patient are effectively protected, and the risk of data leakage is reduced.
Drawings
The above features, technical features, advantages and implementation of the present invention will be further described in the following description of preferred embodiments with reference to the accompanying drawings in a clear and easily understood manner.
Fig. 1 is a schematic flow chart of an electronic medical record processing method based on internet diagnosis and treatment.
Fig. 2 is a schematic structural diagram of an electronic medical record processing method based on internet diagnosis and treatment.
Detailed Description
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will explain the specific embodiments of the present invention with reference to the accompanying drawings. It is evident that the drawings in the following description are only examples of the invention, from which other drawings and other embodiments can be obtained by a person skilled in the art without inventive effort.
For simplicity of the drawing, only the parts relevant to the invention are schematically shown in each drawing, and they do not represent the actual structure thereof as a product. Additionally, in order to simplify the drawing for ease of understanding, components having the same structure or function in some of the drawings are shown schematically with only one of them, or only one of them is labeled. Herein, "a" means not only "only this one" but also "more than one" case.
It should be further understood that the term "and/or" as used in the present specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items, and includes such combinations.
In this context, it should be noted that the terms "mounted," "connected," and "connected" are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally connected, unless otherwise explicitly stated and defined. Either mechanically or electrically. Can be directly connected or indirectly connected through an intermediate medium, and can be communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
In addition, in the description of the present invention, the terms "first," "second," and the like are used merely to distinguish between descriptions and are not to be construed as indicating or implying relative importance.
Example 1
In one embodiment, referring to fig. 1 of the specification, a schematic flow chart of a method for processing an electronic medical record based on internet diagnosis and treatment provided by the invention is shown. Referring to fig. 2 of the specification, a schematic structural diagram of an electronic medical record processing method based on internet diagnosis and treatment is shown.
The invention provides an electronic medical record processing method based on internet diagnosis and treatment, which comprises the following steps:
s101: and acquiring the illness state information of the patient.
Alternatively, information about the patient's symptoms, pain sensations, medical history, etc. may be obtained directly by conducting face-to-face consultations and examinations with the patient. The historical illness state information of the patient can also be obtained from medical documents such as an electronic medical record, a diagnosis record, an assay report, a radiological image report and the like before the patient.
Wherein the condition information may be some description of the basic condition of the patient.
For example, the following is a description of patient condition information: patients develop urinary incontinence on XX, year XX, month XX, due to fever, up to 39 ℃. For diagnosis and treatment, anti-inflammatory treatment is performed, and symptoms are relieved. Line CT enhanced scan shows: the regional outward convex lump shadow is visible in the middle of the right kidney due to the space occupying lesion of the right kidney, the boundary is clear, the size is about 2.9cm multiplied by 3.4cm, and the possibility of considering kidney cancer is high. Abdomen-going color ultrasound showing in our hospital: left kidney mild hydrosis, right kidney parenchymal tumor. The clinic is supposed to receive I's department with "right kidney occupation". At present, the health care food has the advantages of good spirit, normal physical strength, normal appetite, normal sleep, 3kg of demographic information reduction, normal urination and normal stool, and is suitable for further examination and treatment. Past history of: plain health, history of diabetes, history of diseases such as hypertension, coronary heart disease, infectious diseases such as hepatitis, tuberculosis, malaria, etc., history of nasal polyp surgery, history of trauma, history of blood transfusion, and history of penicillin allergy. No history of food allergy, vaccination was performed locally.
S102: and extracting a structured text and a non-structured text in the illness state information.
Wherein structured text refers to text data organized according to certain rules and formats, with explicit fields and predefined data structures. The structured text in the condition information may include: symptoms, pain, blood routine test results, urine routine test results, imaging test results, pathology test results, past surgical history, past medical history, diagnostic results, demographic information, and the like.
Unstructured text refers to text data that does not have well-defined fields and data structures.
In one possible implementation, S102 is specifically:
and extracting the structured text and unstructured text in the illness state information through a text classification model based on a natural language processing technology.
S103: and calculating the matching degree of the structured text with each history medical record.
Specifically, a text similarity algorithm, such as cosine similarity, jaccard similarity, edit distance, etc., may be used to perform similarity comparison between the structured text and the target text. These algorithms can calculate the degree of similarity between texts based on the semantic and grammatical features of the texts.
In one possible implementation, S103 specifically includes:
s1031: calculating symptom matching degree with each historysim 1 Degree of pain matchingsim 2 Degree of matching of blood routine detection resultssim 3 Degree of matching of routine urine detection resultssim 4 Matching degree of imaging inspectionsim 5 Matching degree of pathological examinationsim 6 Matching degree of past surgical historysim 7 Matching degree of past medical historysim 8 Degree of matching of diagnosis resultssim 9 Matching with demographic informationsim 10 ：
Wherein,。
s1032: according to the degree of symptom matchingsim 1 Degree of pain matchingsim 2 Degree of matching of blood routine detection resultssim 3 Degree of matching of routine urine detection resultssim 4 Matching degree of imaging inspectionsim 5 Matching degree of pathological examinationsim 6 Matching degree of past surgical historysim 7 Matching degree of past medical historysim 8 Degree of matching of diagnosis resultssim 9 Matching with demographic informationsim 10 Weight, calculating the matching degree of the structured textsim a ：
Wherein,α i weights representing the degree of matching of different texts.
The weight refers to the importance of different text matching degrees in the process of calculating the structured text matching degree. For example, depending on the knowledge of the disease characteristics and the importance of treatment, a higher weight may be given to the degree of symptom matching, degree of matching of the detection results, etc., while a lower weight may be given to the degree of matching of the past medical history.
According to the invention, the disease information of the current patient can be compared and matched with the historical medical record by calculating the matching degree of the structured text. The method is favorable for finding out past cases similar to the illness state of the patient, provides more accurate reference and reference, and provides basis for making diagnosis and treatment schemes. By calculating the matching degree of the structured texts, the history medical records similar to the illness state of the patient can be quickly screened, the searching range of doctors is reduced, and time and energy are saved. This helps to increase the efficiency of medical work, speed up diagnostic and therapeutic procedures, and provide more timely and effective medical services to patients.
In one possible implementation, S103 further includes:
s1033: determining weights for different text matches byα i ：
Matching results through matching symptoms, pain and blood routine detectionMatching degree, urine routine detection result matching degree, imaging examination matching degree, pathology examination matching degree, past operation history matching degree, past medical history matching degree, diagnosis result matching degree and demographic information matching degree are compared in pairs, and a discrimination matrix is established by combining nine-level scale methodA：
Wherein,a ij represent the firstiThe degree of matching of the text is relative to the firstjThe degree of importance of the degree of matching of the individual texts,a ij the value of (2) is determined by a nine-pole scale method,n=10。
among them, nine-level Scale (Nine-Point Scale) is a Scale method for comparing and evaluating the relative importance, quality or degree of objects or concepts. Typically consists of a scale containing nine levels, each level being used to represent a different degree or degree. The person to be evaluated needs to select one of the nine grades to be the most consistent with the first grade according to own feeling or cognitioniThe degree of matching of the text is relative to the firstjAnd (5) evaluating the importance degree of the matching degree of the texts.
Calculating a discrimination matrixAFeature vectors and feature values of (a):
wherein,λrepresenting a discriminant matrixAIs used for the characteristic value of the (c),ωrepresenting a discriminant matrixATakes the maximum characteristic value as the characteristic vector of (1)λ max The feature vector corresponding to the largest feature value is noted asω max ，。
For the feature vector corresponding to the largest feature valueω max Normalization processing:
wherein the normalized vectorAre>Weights respectively representing the matching degree of each text are respectively marked asα 1 、α 2 、…、α n 。
In the invention, a systematic method is provided for determining the weight, based on the pairwise comparison among criteria and expert judgment, a plurality of factors such as symptom matching degree, pain matching degree, detection result matching degree and the like can be considered, and the importance of the factors is comprehensively considered, so that the contribution of different text matching degrees is more comprehensively evaluated and compared, and the decision process is more objective and scientific. Subjective bias and randomness can be reduced through weight determination, and a quantifiable basis is provided for decision making.
S104: and calculating the matching degree of unstructured text with each history.
In one possible implementation, S104 specifically includes sub-steps S1041 to S1047:
s1041: and constructing a text matching model based on the cyclic neural network.
Wherein the text matching model comprises: an input layer, a state layer, an attention layer, a full connection layer, and a matching layer.
S1042: grouping individual unstructured text into word vector sequences [x 1 , x 2 ,…, x m ]。
S1043: in the state layer, word vectors are computed attHidden state at time:
wherein,representing the current state of the forward loop, +.>Representing the current state of the backward loop, GRU () represents the nonlinear computation through the loop neural network,u t representation->Is used for the weight coefficient of the (c),v t representation->Is used for the weight coefficient of the (c),p t representation oftBias terms for time hidden states.
S1044: in the attention layer, each word vector is assigned a weightγAnd accumulating to obtain hidden state of current attention layers t ：
。
S1045: outputting the eigenvalue of the word vectorO：
。
S1046: in the full connection layer, feature values of word vectors are aggregated.
S1047: in the matching layer, unstructured text matching degree is calculated through cosine similaritysim b ：
Wherein,Bunstructured text in the patient's condition information,Crepresenting unstructured text in each of the historic medical records,O B a feature value representing unstructured text in patient condition information,O C representing unstructured in individual historic medical recordsCharacteristic values of the text.
It should be noted that, the unstructured text matching model based on the cyclic neural network can introduce an attention mechanism through learning the characteristics and the context information of the text sequence, establish text characteristic representation, and calculate matching degree through cosine similarity, so that matching performance and accuracy of unstructured text are improved. Such models may play an important role in matching unstructured text tasks and provide more accurate results and better matching results.
S105: according to the matching degree of the structured text and the unstructured text, calculating the comprehensive matching degree with each historical medical record：
Wherein,ηrepresenting structured text matchingsim a Weight coefficient of 1-ηRepresenting unstructured text matching degreesim b Weight coefficient of (c) in the above-mentioned formula (c).
Wherein, the person skilled in the art can set the matching degree of the structured text according to the actual situationsim a Is of the weight of (1)ηThe size is not limited by the invention. If the matching degree of the structured document is found to be more important in practice, the weighting coefficient of the structured document can be increased to reflect its influence on the comprehensive matching degree to a greater extent. The flexibility and the adjustability enable the method to be adapted to different application scenarios and disease characteristics.
In the present invention, both structured and unstructured text may provide important aspects of the condition information. By comprehensively considering the matching degree of the two, the similarity degree of the historical medical record and the current patient condition can be more comprehensively estimated. The structured text comprises specific indexes and numerical values, the unstructured text comprises more descriptive information, and the comprehensive matching degree can more accurately reflect the matching degree of the historical medical record and the patient condition by weighing the contributions of the two. The reliability and the accuracy of medical decision can be improved, better reference and support are provided for doctors, and the determination of personalized and accurate diagnosis and treatment schemes is promoted.
S106: and selecting a preset number of historical medical records with highest comprehensive matching degree, and generating an alternative diagnosis and treatment scheme.
The preset number may be 10, and the specific numerical value of the preset number is not limited in the invention.
Specifically, diagnosis and treatment schemes in the history medical records with highest comprehensive matching degree can be selected and summarized together to generate alternative diagnosis and treatment schemes.
S107: pushing the alternative diagnosis and treatment scheme, and selecting the diagnosis and treatment scheme according to the alternative diagnosis and treatment scheme.
Specifically, doctors can select the diagnosis and treatment scheme which is most in line with the current illness state after reading the pushed alternative diagnosis and treatment scheme, automatic filling is performed on the diagnosis and treatment scheme input interface, manual typing is not needed, potential problems caused by manual input errors are reduced, time of the doctors is saved, and diagnosis and treatment efficiency is improved. On the other hand, even if the patient faces the difficult illness, the history medical record can be used as reference information, so that subjective judgment is avoided, and diagnosis and treatment accuracy is improved.
S108: and generating an electronic medical record according to the illness state information and the corresponding diagnosis and treatment scheme.
Specifically, the electronic medical record is generated from personal information, medical history records, physical examination records, blood routine examination records, urine routine examination records, imaging examination records, diagnostic records, medical treatment protocols, and follow-up records of the patient.
S109: and desensitizing the electronic medical record.
In particular, personal identification information such as patient's name, identification card number, address, telephone number, etc. can be deleted from the electronic medical record or replaced with a desensitizing identification, such as with an anonymously encoded or virtual identifier. The date and time information in the electronic medical record can also be desensitized, and the date can be obscured, such as by retaining only the year or replacing a specific date with a generic date.
In order to perform the desensitization process more comprehensively and accurately, in one possible embodiment, S109 specifically includes:
s1091: and forming the text in the electronic medical record into a word vector sequence.
S1092: and outputting the characteristic value of the word vector through the text matching model.
It should be noted that, in the present invention, the text matching model may be used to calculate the matching degree of the unstructured text on the one hand, and may also be used to desensitize the electronic medical record on the other hand.
S1093: calculating the matching degree of the text in the electronic medical record and the sensitive words of each sensitive word in the sensitive word database through cosine similaritysim c ：
Wherein,Drepresenting text in an electronic medical record,Erepresenting individual sensitive words in the sensitive word database,O D a characteristic value representing text in the electronic medical record,O E characteristic values representing the individual sensitive words in the sensitive word database.
S1094: when the matching degree of the target text in the electronic medical record and the sensitive word of the sensitive word in the sensitive word databasesim c And if the matching degree is larger than the preset matching degree, privacy protection processing is carried out on the target text.
It should be noted that cosine similarity calculation is a simple and efficient calculation method, the calculation of matching degree can be completed in a short time, accurate matching degree of sensitive words can be obtained by calculating matching degree of texts in electronic medical records and sensitive words of each sensitive word in a sensitive word database through cosine similarity, the efficiency of desensitization processing is improved, and the method is suitable for large-scale electronic medical record data processing, further, target texts with matching degree of the sensitive words being larger than the preset matching degree are subjected to desensitization processing, personal privacy and sensitive information of patients are effectively protected, and the information is ensured not to be accessed, used or leaked by unauthorized persons or institutions.
In one possible implementation, S1094 is specifically:
when the matching degree of the target text in the electronic medical record and the sensitive word of the sensitive word in the sensitive word databasesim c And under the condition that the matching degree is larger than the preset matching degree, hiding, deleting or replacing the target text with the number.
The sensitive words are hidden, deleted or replaced, so that sensitive information in the electronic medical record can be effectively protected, and sensitive data can be prevented from being leaked. This helps to comply with privacy regulations and medical privacy requirements, ensuring that the privacy rights of the patient are protected.
S110: a private key and a public key are generated.
In one possible implementation, S110 specifically includes sub-steps S1101 to S1104:
s1101: selecting two large primesaAndbcalculation ofAnd +.>：
S1102: randomly selecting an integereSo that the random numbereThe method meets the following conditions:
wherein,representing random numberseAnd->Mutually good quality.
S1103: calculating random numberseIs the inverse of:
s1104: will be%G,e) As a private key, will @G,d) As a public key.
It should be noted that the generation of the private and public keys using the above-described method may provide a secure encryption and authentication mechanism, protecting the confidentiality and integrity of sensitive data, and ensuring that only authorized persons can access and manipulate electronic medical records.
In a possible implementation, S110 further includes a substep S1105:
s1105: construction of random numberseAnd inverse elementdIs a chaotic mapping relation of:
wherein,kthe number of times of verification is indicated,λ 1 、λ 2 、λ 3 、λ 4 andλ 5 representing the control parameters and being constant.
It should be noted that the function of the chaotic mapping relation is to protect random numberseAnd inverse elementdIn each subsequent verification, the random numbereAnd inverse elementdAll will change. In the prior art, the chaotic mapping relation is that the range of chaotic parameters is discontinuous, a plurality of periodic windows exist in a parameter space, the chaotic behavior is fragile, and when the parameters are interfered, the chaotic behavior is easy to disappear, so that the problem of chaotic degradation occurs. The method comprises the steps of initializing two parameter polynomials, folding any value into a fixed range through modular operation, generating chaotic mapping from a nonlinear polynomial, generating two-dimensional chaotic mapping with robust chaos, and overcoming the defects in the conventional chaotic mapping relation.
S111: and encrypting the desensitized electronic medical record by using the public key and uploading the electronic medical record to the cloud.
S112: and receiving a consulting request initiated by a user to the cloud.
The user can initiate a query request from various clients such as a computer, a mobile phone, a tablet personal computer and the like to the cloud.
Wherein the user may be a patient, a physician, etc., even a physician of other internet medical institutions.
S113: and verifying the private key of the user, and displaying the desensitized electronic medical record under the condition that the identity of the user passes the verification.
It should be noted that, the process of verifying the private key is the inverse of the encryption process, and the present invention is not limited to the above-mentioned process for avoiding repetition.
Compared with the prior art, the invention has at least the following beneficial technical effects:
(1) According to the invention, according to the illness state information of the patient, the history medical record with higher matching degree is automatically matched from the history medical record library, the diagnosis and treatment scheme of the history medical record with higher matching degree is used as an alternative diagnosis and treatment scheme, and a doctor can select a final diagnosis and treatment scheme according to the alternative diagnosis and treatment scheme. On the one hand, when a proper diagnosis and treatment scheme exists in the alternative diagnosis and treatment schemes, doctors can directly select the diagnosis and treatment scheme as a final diagnosis and treatment scheme without manually typing, so that potential problems caused by manual input errors are reduced, the time of the doctors is saved, and the diagnosis and treatment efficiency is improved. On the other hand, even if the patient faces the difficult illness, the history medical record can be used as reference information, so that subjective judgment is avoided, and diagnosis and treatment accuracy is improved.
(2) According to the invention, the electronic medical record of the patient can be subjected to desensitization treatment, so that the privacy and sensitive information of the patient are protected, and the safety of data is improved.
(3) According to the invention, the desensitized electronic medical record can be encrypted by using the public key, and when the identity verification of the user passes, the user has authority to review the electronic medical record, so that the information security of the electronic medical record of the patient in the sharing process is further ensured, the privacy and sensitive information of the patient are effectively protected, and the risk of data leakage is reduced.
Example 2
In one embodiment, the invention provides an electronic medical record transmission system based on internet medical treatment, which is used for executing the electronic medical record processing method based on internet medical treatment in embodiment 1.
The electronic medical record transmission system based on internet medical treatment provided by the invention can realize the steps and effects of the electronic medical record processing method based on internet medical treatment in the embodiment 1, and the invention is not repeated for avoiding repetition.
Compared with the prior art, the invention has at least the following beneficial technical effects:
(1) According to the invention, according to the illness state information of the patient, the history medical record with higher matching degree is automatically matched from the history medical record library, the diagnosis and treatment scheme of the history medical record with higher matching degree is used as an alternative diagnosis and treatment scheme, and a doctor can select a final diagnosis and treatment scheme according to the alternative diagnosis and treatment scheme. On the one hand, when a proper diagnosis and treatment scheme exists in the alternative diagnosis and treatment schemes, doctors can directly select the diagnosis and treatment scheme as a final diagnosis and treatment scheme without manually typing, so that potential problems caused by manual input errors are reduced, the time of the doctors is saved, and the diagnosis and treatment efficiency is improved. On the other hand, even if the patient faces the difficult illness, the history medical record can be used as reference information, so that subjective judgment is avoided, and diagnosis and treatment accuracy is improved.
(2) According to the invention, the electronic medical record of the patient can be subjected to desensitization treatment, so that the privacy and sensitive information of the patient are protected, and the safety of data is improved.
(3) According to the invention, the desensitized electronic medical record can be encrypted by using the public key, and when the identity verification of the user passes, the user has authority to review the electronic medical record, so that the information security of the electronic medical record of the patient in the sharing process is further ensured, the privacy and sensitive information of the patient are effectively protected, and the risk of data leakage is reduced.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The foregoing examples illustrate only a few embodiments of the invention, which are described in detail and are not to be construed as limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (6)
1. The electronic medical record processing method based on internet diagnosis and treatment is characterized by comprising the following steps of:
s101: acquiring disease condition information of a patient;
s102: extracting a structured text and an unstructured text in the illness state information;
s103: calculating the matching degree of the structured text with each history medical record;
s104: calculating the matching degree of unstructured text with each history;
s105: according to the matching degree of the structured text and the matching degree of the unstructured text, calculating the comprehensive matching degree with each historical medical record：
；
Wherein,ηrepresenting the structured text matchsim a Weight coefficient of 1-ηRepresenting the unstructured text matching degreesim b Weight coefficient of (2);
s106: selecting the history medical records with the highest comprehensive matching degree and the preset number, and generating an alternative diagnosis and treatment scheme;
s107: pushing the alternative diagnosis and treatment scheme, and selecting the diagnosis and treatment scheme according to the alternative diagnosis and treatment scheme;
s108: generating an electronic medical record according to the illness state information and the corresponding diagnosis and treatment scheme;
s109: desensitizing the electronic medical record;
s110: generating a private key and a public key;
s111: encrypting the desensitized electronic medical record by using the public key and uploading the electronic medical record to the cloud;
s112: receiving a consulting request initiated by a user to a cloud;
s113: verifying the private key of the user, and displaying the desensitized electronic medical record under the condition that the identity of the user passes the verification;
wherein, the step S103 specifically includes:
s1031: calculating symptom matching degree with each historysim 1 Degree of pain matchingsim 2 Degree of matching of blood routine detection resultssim 3 Degree of matching of routine urine detection resultssim 4 Matching degree of imaging inspectionsim 5 Matching degree of pathological examinationsim 6 Matching degree of past surgical historysim 7 Matching degree of past medical historysim 8 Degree of matching of diagnosis resultssim 9 Matching with demographic informationsim 10 ：
；
Wherein,；
s1032: according to the symptom matching degreesim 1 Said pain matching degreesim 2 Matching degree of blood routine detection resultsim 3 Degree of matching of the urine routine test resultssim 4 Matching degree of the imaging examinationsim 5 Matching degree of the pathological examinationsim 6 Matching degree of the past operation historysim 7 Matching degree of the prior medical historysim 8 Degree of matching of the diagnosis resultssim 9 Matching the demographic informationsim 10 And weight, calculating the matching degree of the structured textsim a ：
；
Wherein,α i weights representing the degree of matching of different texts;
wherein, the step S104 specifically includes:
s1041: constructing a text matching model based on a cyclic neural network, wherein the text matching model comprises the following steps: an input layer, a state layer, an attention layer, a full connection layer and a matching layer;
s1042: grouping individual unstructured text into word vector sequences [x 1 , x 2 ,…, x m ]；
S1043: in the state layer, word vectors are calculated intHidden state at time:
；
wherein,representing the current state of the forward loop, +.>Representing the current state of the backward loop, GRU () represents the nonlinear computation through the loop neural network,u t representation->Is used for the weight coefficient of the (c),v t representation->Is used for the weight coefficient of the (c),p t representation oftBias of time hidden stateSetting items;
s1044: in the attention layer, a weight is assigned to each of the word vectorsγAnd accumulating to obtain the hidden state of the current attention layers t ：
；
S1045: outputting the eigenvalue of the word vectorO：
；
S1046: converging the characteristic values of the word vectors in the full connection layer;
s1047: in the matching layer, calculating the unstructured text matching degree through cosine similaritysim b ：
；
Wherein,Bunstructured text in the patient's condition information,Crepresenting unstructured text in each of the historic medical records,O B a feature value representing unstructured text in patient condition information,O C a feature value representing unstructured text in each of the historic medical records;
wherein, the step S110 specifically includes:
s1101: selecting two large primesaAndbcalculation ofAnd +.>：
；
S1102: randomly selecting an integereSo that the random numbereThe method meets the following conditions:
；
wherein,representing random numberseIs in contact with the->Mutual quality;
s1103: calculating random numberseIs the inverse of:
；
s1104: will be%G,e) As the private key, will @G,d) As the public key.
2. The internet diagnosis and treatment-based electronic medical record processing method according to claim 1, wherein the step S102 is specifically:
and extracting the structured text and unstructured text in the illness state information through a text classification model based on a natural language processing technology.
3. The electronic medical record processing method based on internet diagnosis and treatment according to claim 1, wherein S103 further comprises:
s1033: determining weights for different text matches byα i ：
By matching the symptom, pain, blood routine, urine routine, imaging, pathology, past surgical history, past medical history, and the likeThe diagnosis result matching degree and the demographic information matching degree are compared in pairs, and a discrimination matrix is established by combining a nine-level scale methodA：
；
Wherein,a ij represent the firstiThe degree of matching of the text is relative to the firstjThe degree of importance of the degree of matching of the individual texts,a ij the value of (2) is determined by a nine-pole scale method,n=10；
calculating the discrimination matrixAFeature vectors and feature values of (a):
；
wherein,λrepresenting a discriminant matrixAIs used for the characteristic value of the (c),ωrepresenting a discriminant matrixATakes the maximum characteristic value as the characteristic vector of (1)λ max The feature vector corresponding to the largest feature value is noted asω max ，；
For the feature vector corresponding to the maximum feature valueω max Normalization processing:
；
wherein the normalized vectorAre>Weights respectively representing the matching degree of each text are respectively marked asα 1 、α 2 、…、α n 。
4. The electronic medical record processing method based on internet diagnosis and treatment according to claim 1, wherein S109 specifically comprises:
s1091: forming a word vector sequence from the text in the electronic medical record;
s1092: outputting the characteristic value of the word vector through the text matching model;
s1093: calculating the matching degree of the text in the electronic medical record and the sensitive words of each sensitive word in the sensitive word database through cosine similaritysim c ：
；
Wherein,Drepresenting text in an electronic medical record,Erepresenting individual sensitive words in the sensitive word database,O D a characteristic value representing text in the electronic medical record,O E characteristic values representing each sensitive word in the sensitive word database;
s1094: when the matching degree of the target text in the electronic medical record and the sensitive word of the sensitive word in the sensitive word database is highsim c And if the matching degree is larger than the preset matching degree, privacy protection processing is carried out on the target text.
5. The internet diagnosis and treatment-based electronic medical record processing method according to claim 4, wherein S1094 specifically comprises:
when the matching degree of the target text in the electronic medical record and the sensitive word of the sensitive word in the sensitive word database is highsim c Under the condition that the matching degree is larger than the preset matching degree, hiding, deleting or replacing the target textNumber (x).
6. The electronic medical record processing method based on internet diagnosis and treatment according to claim 1, wherein S110 further comprises:
s1105: construction of random numberseAnd inverse elementdIs a chaotic mapping relation of:
；
wherein,kthe number of times of verification is indicated,λ 1 、λ 2 、λ 3 、λ 4 andλ 5 representing the control parameters and being constant.
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