CN117238518A - Liver disease pathology consultation case data collection system based on small program - Google Patents
Liver disease pathology consultation case data collection system based on small program Download PDFInfo
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Abstract
The invention discloses a liver disease pathological consultation case collecting system based on small programs, which comprises: the data uploading module is used for uploading case information to an applet of the case data collecting system by a patient, wherein the case information comprises a patient examination report, and the patient uploads the items in the examination report in a photographing uploading and text uploading mode after logging in; the classification diagnosis module is used for classifying subtypes of liver diseases, acquiring liver tumor CT images of patients, and performing diagnosis analysis by retrieving case information of the patients in the examination report; the encryption sharing module is used for sharing case information among doctors and patients, desensitizing and encrypting the case information of the patients, and the doctor and the patient acquire the viewing authority of the case information through encryption keys respectively; the remote medical module is used for a patient to remotely consultate, acquiring subtype classification results of liver diseases, carrying out health early warning on the patient and recommending an on-line consultation doctor.
Description
Technical Field
The invention relates to a medical data collection technology, in particular to a liver disease case consultation case data collection system based on small programs.
Background
With the continued advancement of medical technology, clinicians are becoming more accurate in the treatment of non-neoplastic liver diseases, which also results in an increasing need for diagnosis of liver disease cases. The medical pathology diagnosis needs to be closely combined with clinic, especially needs to provide various examination results such as testing, endoscope of influence meter and the like, however, because non-tumor liver diseases are often chronic processes, the course of the disease can be as long as several years or even more than ten years, so that patients who need to carry out liver disease pathology consultation often need to carry or mail a large number of testing and examination data at different time points, and for pathologists, a great deal of time and effort are required to arrange the data, and the data storage and long-term follow-up on the patients are inconvenient.
The application number is: the invention of CN201810271362.2 discloses a liver pathological image classification method based on convolutional neural network, comprising collecting images and scaling the images to preset pixels to form an image set; preprocessing an image in the image set to amplify data in the image set; building a convolutional neural network model; training a convolutional neural network model and solving the weight of the convolutional neural network; and inputting a liver pathology image to be classified, and comparing the liver pathology image to be classified with an image in the convolutional neural network model to obtain a classification result.
According to the liver pathological image classification method based on the convolutional neural network in the prior art, automatic matching recognition and classification of liver cells to be classified are realized by establishing a convolutional neural network model. However, in the data collection and classification process, the analysis of the pathology of the patient by the doctor cannot be better realized, and the problem of leakage of private information of the patient is easily caused. In order to solve the problem, the invention provides a small procedure for collecting liver disease case data, which aims to help patients to quickly upload medical record data, and improve the working efficiency of pathologists while facilitating the doctor to visit and consultation. Through the small program, the patient can conveniently and rapidly upload the test report, the image data and the like of the patient into the system through a mobile phone or a computer, and the data are arranged and stored in a unified database, so that a doctor can access and analyze the data more conveniently.
Disclosure of Invention
The invention provides a liver disease pathological consultation case data collecting system based on small programs, which aims to solve the problems in the prior art.
A small procedure based liver disease pathology consultation case data collection system comprising:
The data uploading module is used for uploading case information to an applet of the case data collecting system by a patient, wherein the case information comprises a patient examination report, and the patient uploads the items in the examination report in a photographing uploading and text uploading mode after logging in;
the classification diagnosis module is used for classifying subtypes of liver diseases, acquiring liver tumor CT images of patients, and performing diagnosis analysis by retrieving case information of the patients in the examination report;
the encryption sharing module is used for sharing case information among doctors and patients, desensitizing and encrypting the case information of the patients, and the doctor and the patient acquire the viewing authority of the case information through encryption keys respectively;
the remote medical module is used for remotely visiting the patient, acquiring subtype classification results of the liver diseases, carrying out health early warning on the patient and recommending an on-line doctor to visit.
Preferably, the data uploading module includes:
the information collection unit is used for storing personal information of the patient, and the case data collection system is used for collecting basic information, personal history, past history and family history of the patient as an examination report, so that the patient can conveniently inquire; personal history includes options of medication used, history of infection, alcohol consumption, and smoking; the past history includes options of whether diabetes, hepatitis, and hypertension;
The information uploading unit is used for uploading the examination report to the patient, and the patient selects options corresponding to the patient symptoms and the personal conditions in the examination report according to the personal conditions and then uploads the options to the medical record data collecting system; meanwhile, the patient perfects the examination report by inputting options which do not appear in the examination report in the personal history and the past history.
Preferably, after the information uploading unit, the method further includes:
a family grading unit for grading the relatives in the family history, taking the direct relatives in the family history as first-class relatives, taking the non-direct relatives as second-class relatives, marking the relatives in the family history according to the grades, and perfecting the patient information by filling in the personal history and family history of the relatives in the examination report;
the photographing and uploading unit is used for photographing and uploading information in the report form by the patient, the case data collecting system provides report photo uploading according to blood convention and liver biochemistry, and the patient uploads the photo by selecting real-time photographing and a mobile phone album;
and the patient login unit is used for checking patient information by a doctor, and searching and checking the examination report uploaded by the patient by searching the name, the mobile phone number and the identity card number of the patient by the case data collection system in the patient login applet.
Preferably, the classification diagnosis module includes:
the pathological classification unit is used for classifying subtypes of liver diseases, the case data collection system divides the liver diseases into two collection pages of neoplastic lesions and non-neoplastic lesions, and a patient performs information filling by scanning corresponding two-dimensional codes in a login interface of the case data collection system in a small program and controls filling time within a preset time range;
the information retrieval unit is used for retrieving the case information of the patient, taking the identification card number of the patient as the unique identification for checking the case information of the patient, displaying the case information submitted by the patient in the interface of the patient login by the applet, providing a doctor-patient inquiry function, and inquiring the past information of the patient by the doctor through the automatic retrieval function after the patient uploads the checking report.
Preferably, the pathology classifying unit comprises:
the self-supervision training subunit is used for training a self-supervision learning model based on rotation prediction, acquiring a plurality of unlabeled liver tumor CT images, and performing feature marking on lesion areas in the liver tumor CT images to serve as unlabeled liver tumor data;
The contrast training subunit is used for training the unlabeled liver tumor data, constructing a feature extraction network and a feature discriminator, presetting training parameters in a self-supervision learning model to fix the training parameters as a feature extractor of a liver tumor CT image, acquiring the unlabeled liver tumor data and inputting the unlabeled liver tumor data into the self-supervision learning model;
and the sample discrimination subunit is used for extracting subtype of liver tumor from the self-supervision learning model and the feature extraction network, and discriminating the true sample and the false sample of the liver tumor CT image by the feature discriminator through applying constraint conditions.
Preferably, the sample discrimination subunit includes:
the data processing subunit is used for extracting and standardizing the region of interest, marking the region of interest in the liver tumor CT image, setting the region dimension of the region of interest, converting the CT value of pixels in the region dimension, and carrying out image enhancement processing on the liver tumor CT image according to the CT value;
the data collection subunit is used for training the data set, acquiring a plurality of liver tumor CT images subjected to image enhancement processing, and taking different types of tumor subtypes as the data set, wherein the data set is randomly divided into a test set and a training set, the training set is input into a self-supervision learning model, and the training set is trained by adopting a self-adaption learning rate optimization algorithm;
And the classification evaluation subunit is used for evaluating the accuracy of the liver tumor CT image classification, wherein the evaluation indexes comprise accuracy, precision and recall rate, setting a test threshold of the evaluation index, inputting the test threshold into the self-supervision learning model, and carrying out the identification classification of the liver disease subtype by the test set according to the test threshold.
Preferably, the encryption sharing module includes:
the data desensitization unit is used for carrying out desensitization treatment on the case information of the patient, uploading the case information to a blockchain network of the case data collection system, and carrying out desensitization operation on sensitive data in the case information by using shielding, generalization and interception modes;
the asymmetric encryption unit is used for encrypting the case information of the patient, the case information of the patient is divided into a patient examination report and a hospital diagnosis treatment scheme, and the hospital diagnosis treatment scheme is always in a ciphertext state by using an asymmetric encryption method so as to ensure that only the patient with the right and the hospital can view;
the information request unit is used for requesting to check case information by a doctor and constructing an asymmetric encryption model, and comprises a request party and an authorized party, wherein the request party is a doctor and a patient for checking the case information, and the authorized party is a case data collection system; the requester obtains the authorization of case information viewing by using a preset encryption algorithm to obtain the exchange key in the symmetric encryption model.
Preferably, the information request unit includes:
the initialization subunit is used for initializing the case data collection system, presetting the security parameters of the case data collection system, outputting and generating public keys, and ensuring that the security parameters in different blockchains are different;
a key generation subunit for generating a key for encrypting the case information, and verifying whether an equation associated with the encryption algorithm by the key including the security parameter is established; if so, randomly acquiring the security parameters in the blockchain node, the identity of the patient and a private key corresponding to the identity of the patient;
and the data sharing subunit is used for decrypting the ciphertext and then sharing the case information, the hospital is used as a holder of the case information to encrypt the ciphertext and issue the ciphertext to the blockchain network, and after the doctor and the patient pass through identity verification, the doctor uses the private key to decrypt and check the case information in the blockchain node.
Preferably, the telemedicine module includes:
the data processing unit is used for acquiring the update state of the case information of the patient in real time, and the case information of the patient is automatically updated by the case data collecting system after each patient visit is completed; the patient can use the ID card number to inquire the update state at the same time;
The disease analysis unit is used for automatically evaluating the liver disease of the patient, evaluating the body condition of the liver disease of the patient according to the case information of the patient, constructing a liver evaluation model, and inputting a preset health value into the liver evaluation model in a deep learning mode;
the intelligent early warning unit is used for reminding the health state of the patient by the case data collection system, acquiring various examination data in the case information of the patient, inputting the examination data into the liver assessment model, and carrying out early warning on the health state of the patient according to the data comparison function of the liver assessment model.
Preferably, after the intelligent early warning unit, the method further includes:
the intelligent recommending unit is used for intelligently recommending doctor to visit according to early warning, setting early warning prompt grades, recommending common doctors and expert doctors according to the grades respectively, and enabling patients to select corresponding doctors to conduct appointment registration according to the early warning prompt grades;
the remote medical unit is used for carrying out remote diagnosis and treatment on patients, the patients provide data in the liver assessment model and an examination report form for a registering doctor as consultation information, and the consultation mode is video communication;
the expert consultation unit is used for remotely consultation of the patient by the doctor, the registering doctor acquires consultation information of the patient to carry out consultation, the consultation content comprises image diagnosis analysis and pathological diagnosis analysis, and the registering doctor returns a prescription result to the case data collecting system to record the consultation content after the analysis is completed.
Compared with the prior art, the invention has the following advantages:
the invention provides a liver disease pathological consultation case data collection system based on a small program, which is convenient and quick for patients to upload data by only using a mobile phone or a computer without carrying a large amount of paper reports and image data. For pathologists, it is no longer necessary to spend a great deal of time in order to organize the patient's data, but rather it can be directly viewed and analyzed in the system. Greatly reduces the workload of pathologists and improves the working efficiency. In addition, the applet can also conveniently store and manage the patient's case data and be used for later follow-up and treatment condition understanding.
By remotely accessing and uploading the data, the pathologist better assists in telemedicine and makes accurate diagnosis and treatment advice for the case. This is particularly important in remote areas or where medical resources are scarce, as they can obtain guidance from specialists through remote pathology consultation, improving diagnosis level and therapeutic effect.
In conclusion, the hepatopathy case data collection applet improves the medical convenience of the patient and greatly improves the working efficiency of pathologists. The method can reduce the data arrangement time of pathologists, improve the diagnosis accuracy and facilitate follow-up patient visit and treatment condition understanding. With further development, the applet can be popularized to other diagnostic scenes, and brings more convenience and benefits to medical work.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a block diagram of a system for collecting liver disease pathology consultation case data based on an applet in an embodiment of the present invention;
FIG. 2 is a block diagram of a case data collection system for classifying subtypes of CT images of liver tumors according to an embodiment of the present invention;
fig. 3 is a block diagram of a unit for remote medical treatment of a patient in an embodiment of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
Referring to fig. 1, an embodiment of the present invention provides an applet-based liver disease pathology consultation case data collection system, comprising:
the data uploading module is used for uploading case information to an applet of the case data collecting system by a patient, wherein the case information comprises a patient examination report, and the patient uploads the items in the examination report in a photographing uploading and text uploading mode after logging in;
the classification diagnosis module is used for classifying subtypes of liver diseases, acquiring liver tumor CT images of patients, and performing diagnosis analysis by retrieving case information of the patients in the examination report;
the encryption sharing module is used for sharing case information among doctors and patients, desensitizing and encrypting the case information of the patients, and the doctor and the patient acquire the viewing authority of the case information through encryption keys respectively;
the remote medical module is used for remotely visiting the patient, acquiring subtype classification results of the liver diseases, carrying out health early warning on the patient and recommending an on-line doctor to visit.
In another embodiment, the data uploading module includes:
the information collection unit is used for storing personal information of the patient, and the case data collection system is used for collecting basic information, personal history, past history and family history of the patient as an examination report, so that the patient can conveniently inquire; personal history includes options of medication used, history of infection, alcohol consumption, and smoking; the past history includes options of whether diabetes, hepatitis, and hypertension;
The information uploading unit is used for uploading the examination report to the patient, and the patient selects options corresponding to the patient symptoms and the personal conditions in the examination report according to the personal conditions and then uploads the options to the medical record data collecting system; meanwhile, the patient perfects the examination report by inputting options which do not appear in the examination report in the personal history and the past history.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the personal information of the user is uploaded, so that the information in the case data collection system is more perfect, and the user humanized experience is improved.
In another embodiment, after the information uploading unit, the method further includes:
a family grading unit for grading the relatives in the family history, taking the direct relatives in the family history as first-class relatives, taking the non-direct relatives as second-class relatives, marking the relatives in the family history according to the grades, and perfecting the patient information by filling in the personal history and family history of the relatives in the examination report;
the photographing and uploading unit is used for photographing and uploading information in the report form by the patient, the case data collecting system provides report photo uploading according to blood convention and liver biochemistry, and the patient uploads the photo by selecting real-time photographing and a mobile phone album;
And the patient login unit is used for checking patient information by a doctor, and searching and checking the examination report uploaded by the patient by searching the name, the mobile phone number and the identity card number of the patient by the case data collection system in the patient login applet.
The working principle of the technical scheme is as follows: the embodiment adopts the scheme that the case data collection system comprises patient basic information, personal history, past history, family history and examination report, can pick up options such as medicines, infections, drinking, smoking and the like, and can provide comprehensive supplementary information by inputting detailed contents, wherein the past history comprises diabetes, hepatitis, hypertension and the like, other past diseases can also be input, the family history can be accurate to first-level relatives and second-level relatives, and the lower content items are the same as the past history. In order to facilitate the patient to take a picture in real time and select a picture in a mobile phone album, a pathologist can search and view information uploaded by the patient through names, mobile phone numbers, identity card numbers and the like at a client side during consultation. The information uploaded by the patient can be searched and checked through names, mobile phone numbers, identity card numbers and the like.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the patient can directly upload the data to the case data collection system, so that an intermediate link is omitted, the whole process is more efficient, a pathologist can quickly acquire the required data, diagnose and analyze the data, and better medical service is provided for the patient.
In another embodiment, the classification diagnostic module comprises:
the pathological classification unit is used for classifying subtypes of liver diseases, the case data collection system divides the liver diseases into two collection pages of neoplastic lesions and non-neoplastic lesions, and a patient performs information filling by scanning corresponding two-dimensional codes in a login interface of the case data collection system in a small program and controls filling time within a preset time range;
the information retrieval unit is used for retrieving the case information of the patient, taking the identification card number of the patient as the unique identification for checking the case information of the patient, displaying the case information submitted by the patient in the interface of the patient login by the applet, providing a doctor-patient inquiry function, and inquiring the past information of the patient by the doctor through the automatic retrieval function after the patient uploads the checking report.
The working principle of the technical scheme is as follows: the scheme adopted by the embodiment is that the case data collection system is divided into two collection pages of neoplastic lesions and non-neoplastic lesions, a patient enters the case data collection system by using mobile equipment to scan corresponding two-dimensional codes, and the patient needs to control the filling time within five minutes. And the case data collection system interface is simple and easy to use. The patient identification card number is used as a unique identifier, so that the client can not only display the case information submitted this time, but also automatically search and display the information submitted in the past, thereby facilitating the comparison of history data by a case doctor.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the efficiency of consultation of pathologists is obviously improved with the assistance of case data, and the satisfaction degree of patients is also improved continuously. The past submitted information is automatically retrieved and presented, so that a pathologist can conveniently compare historical data.
Referring to fig. 2, in another embodiment, the pathology classification unit comprises:
the self-supervision training subunit is used for training a self-supervision learning model based on rotation prediction, acquiring a plurality of unlabeled liver tumor CT images, and performing feature marking on lesion areas in the liver tumor CT images to serve as unlabeled liver tumor data;
the contrast training subunit is used for training the unlabeled liver tumor data, constructing a feature extraction network and a feature discriminator, presetting training parameters in a self-supervision learning model to fix the training parameters as a feature extractor of a liver tumor CT image, acquiring the unlabeled liver tumor data and inputting the unlabeled liver tumor data into the self-supervision learning model;
and the sample discrimination subunit is used for extracting subtype of liver tumor from the self-supervision learning model and the feature extraction network, and discriminating the true sample and the false sample of the liver tumor CT image by the feature discriminator through applying constraint conditions.
The working principle of the technical scheme is as follows: the embodiment adopts a scheme that the examination report contains data obtained by each examination of a patient, and the subtype of the tumor is classified by using a tumor classification method based on self-supervision and antagonism learning. When classifying liver tumor CT images, due to the fact that the privacy of medical images and the cost of manual labeling of the medical images are too high, the CT image data of labeled high-quality liver tumors are less, and therefore serious fitting problems often occur when model training is carried out. Firstly, universal expression of a lesion area is obtained from a large number of unlabeled liver tumor CT images in a self-supervision learning mode, the extracted additional universal features are introduced into model training, and accuracy of classification of the liver tumor CT images is improved by setting constraint conditions.
Firstly, a self-supervision learning model based on rotation prediction is trained, general expression of lesion region features is obtained from a large number of unlabeled liver tumor CT images, namely feature extraction is carried out, and a feature extraction part is used as unlabeled liver tumor data. The method comprises the steps of constructing a feature extraction network and a feature discriminator, inputting preset parameters into a trained self-supervision learning model for identification by the feature discriminator, simultaneously acquiring general expressions of feature areas by a plurality of unlabeled liver tumor data, introducing additional general features into the self-supervision learning model, applying constraint conditions to enable the self-supervision learning model to identify more accurately, and discriminating a true sample and a false sample by the self-supervision learning model and the feature extraction network by using the discriminator.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the classification of liver tumor subtypes is facilitated by constructing the self-supervision learning model, so that doctors can collect the data of liver diseases of patients more efficiently.
In another embodiment, the sample discrimination unit includes:
the data processing subunit is used for extracting and standardizing the region of interest, marking the region of interest in the liver tumor CT image, setting the region dimension of the region of interest, converting the CT value of pixels in the region dimension, and carrying out image enhancement processing on the liver tumor CT image according to the CT value;
the data collection subunit is used for training the data set, acquiring a plurality of liver tumor CT images subjected to image enhancement processing, and taking different types of tumor subtypes as the data set, wherein the data set is randomly divided into a test set and a training set, the training set is input into a self-supervision learning model, and the training set is trained by adopting a self-adaption learning rate optimization algorithm;
and the classification evaluation subunit is used for evaluating the accuracy of the liver tumor CT image classification, wherein the evaluation indexes comprise accuracy, precision and recall rate, setting a test threshold of the evaluation index, inputting the test threshold into the self-supervision learning model, and carrying out the identification classification of the liver disease subtype by the test set according to the test threshold.
The working principle of the technical scheme is as follows: the scheme adopted by the embodiment is that subtypes of CT images of liver tumors of different types are respectively collected, and liver tumor data are randomly divided into a test set and a training set. Compared with the whole liver image, the liver tumor part only occupies a small part, the influence of other irrelevant tissues and lesion areas is not reduced, doctors are required to outline the liver tumor lesion areas, and the dimension of the region of interest is unified to be 49 multiplied by 49 during training. CT image of liver tumor is then converted into CT value, which is a measure unit for representing the density of human organ or tissue. The CT values are used for distinguishing the densities of different tissues of the human body, the CT images are enhanced, and the expansion of the data set is realized through cutting and filling.
To verify the accuracy of image recognition, it is necessary to determine the accuracy of feature recognition using evaluation indexes including accuracy, precision, and recall. The accuracy is the ratio of the correct test result to the total sample number; the accuracy rate is the probability of the number of samples with the correct test results in practice, namely, the accuracy rate can be predicted by knowing how much in the sample results with the positive test results; the recall rate represents the probability of being correctly predicted as a positive sample in the original sample, and the recognition accuracy of the image is detected through the range of a preset evaluation index.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the method and the device can effectively relieve the problems in liver tumor classification after analysis by constructing a data set, carrying out index evaluation and data preprocessing and assisting doctors in early detection and treatment of liver tumor diseases, and greatly improve the cure rate of patients.
In another embodiment, the encryption sharing module includes:
the data desensitization unit is used for carrying out desensitization treatment on the case information of the patient, uploading the case information to a blockchain network of the case data collection system, and carrying out desensitization operation on sensitive data in the case information by using shielding, generalization and interception modes;
the asymmetric encryption unit is used for encrypting the case information of the patient, the case information of the patient is divided into a patient examination report and a hospital diagnosis treatment scheme, and the hospital diagnosis treatment scheme is always in a ciphertext state by using an asymmetric encryption method so as to ensure that only the patient with the right and the hospital can view;
the information request unit is used for requesting to check case information by a doctor and constructing an asymmetric encryption model, and comprises a request party and an authorized party, wherein the request party is a doctor and a patient for checking the case information, and the authorized party is a case data collection system; the requester obtains the authorization of case information viewing by using a preset encryption algorithm to obtain the exchange key in the symmetric encryption model.
The working principle of the technical scheme is as follows: the scheme adopted by the embodiment is very important for hospitals in terms of privacy protection of case information, the method of combining the blockchain and the case information is used for ensuring that sensitive information stored in the blockchain is not revealed, desensitization operation is needed before the case information is stored in the blockchain, and then the case information is uploaded to the blockchain of a case data collection system, so that personal cases of patients cannot be tampered and can be permanently stored, and meanwhile, effective traceability of the patients to past medical history of the patients is facilitated. Because the blockchain is a ledger maintained by different nodes together, both patients and doctors for one or more treatments have access to medical records. In order to protect privacy of a patient, in a hospital treatment scene, the patient-authorized hospital uploads private data in case information, and desensitization operation of sensitive data is performed by a desensitization method, so that personal privacy information of the patient is protected from being identified by other people, and the usability of the data to a certain extent can be ensured by the desensitization method, wherein the desensitization method comprises shielding, generalization and interception. Wherein the shielding comprises uniformly replacing partial areas in the sensitive information content in the case by a certain masking symbol, and then exposing the sensitive information data holding part so as to ensure the authenticity of the data and further facilitate verification. For example, the name field can be accurately positioned to the identity of the user, so that an occlusion method for reserving the name by occluding the first name is adopted for the name, and the occlusion of the name realization part is realized by selecting an identifier. For example "Qiu". Generalization refers to replacing a value of data with a specific number by an abstract word, for numbers, a section containing values may be replaced, for classified feature attributes, a more abstract class. For example, age 35, the generalization age range is 30-40. Intercepting means that an identifier is selected for an original value, then a subsequent intercepting operation is carried out according to the identifier, a finally displayed value is a partial numerical value intercepted by a cutting preset method, namely partial information in the original overall information is displayed, the input treatment privacy information contains sensitive information such as the main treatment of a patient or the time of treatment, and the desensitization operation can be realized by an intercepting mode. For example, the data "ssstthjhj" may be intercepted by intercepting the portion of the reserved "ssstt".
The case information of the patient is divided into a patient examination report and a hospital diagnosis treatment scheme, the personal privacy part of the examination report of the patient is preprocessed by using a desensitization part, and the hospital diagnosis treatment scheme is used for sharing the hospital and the patient by using an asymmetric encryption scheme, so that the hospital diagnosis treatment information is always ensured in a ciphertext state that only the patient or doctor who obtains the authority can view the related information. The asymmetric algorithm is realized by a pair of keys, one key is used for completing encryption work, the other key is needed for completing decryption work, the keys are divided into private keys and public keys, the private keys are secret and known by only a private key holder, and the public keys are public and can be known by all people. An asymmetric encryption model is built, an authorized party hospital or a patient obtains a request for checking medical information sent by a hospital or a patient of a requesting party, the patient and the hospital of the authorized party encrypt the desensitized case information through an encryption algorithm in the asymmetric encryption operation model and a public key of the requesting party, the encrypted case information is sent to the patient or the hospital of the requesting party, the patient or the hospital of the requesting party serves as a receiving party of the medical information, and a decryption algorithm and a private key are used for decrypting the ciphertext, so that the original ciphertext, namely the case information of the patient, is obtained.
The patient case data sharing is realized through the case data collecting system, and the sharing process stores the case information into the blockchain through an intelligent contract between all patients and hospitals aiming at medical information and between a patient and a hospital of a requester of the case information. The case checking requester needs to enter a data request layer of the data collection system, at the data request layer, a data requester hospital sends case information of a patient to be checked to an intelligent contract of the hospital to which the requester belongs through screening, a contract relation is established on the intelligent contract, and related information of the data requester is added in the contract at the same time, and the data requester hospital signature, hash codes of medical information and serial number information of the patient are contained. All data requests are then added to the data information informing the hospital of the status of the data information.
The hospital of the data owner enters the authorization function module, then the identity of the patient is verified, after the verification is successful, the patient needs to check whether the patient is himself or herself through the request sent by the hospital, and the patient or the use book is searched through the hospital data screening through the permission of the patient
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the case information is uploaded to the blockchain of the case data collection system, so that the personal case of the patient can be ensured not to be tampered and can be permanently stored, and meanwhile, the patient can conveniently and effectively trace back the past medical history of the patient.
In another embodiment, the information request unit includes:
the initialization subunit is used for initializing the case data collection system, presetting the security parameters of the case data collection system, outputting and generating public keys, and ensuring that the security parameters in different blockchains are different;
a key generation subunit for generating a key for encrypting the case information, and verifying whether an equation of the key containing the security parameter and the encryption algorithm is established; if so, randomly acquiring the security parameters in the blockchain node, the identity of the patient and the private key corresponding to the identity of the patient;
and the data sharing subunit is used for decrypting the ciphertext and then sharing the case information, the hospital is used as a holder of the case information to encrypt the ciphertext and issue the ciphertext to the blockchain network, and after the doctor and the patient pass through identity verification, the doctor uses the private key to decrypt and check the case information in the blockchain node.
The working principle of the technical scheme is as follows: when the case information of a patient is encrypted by using an asymmetric encryption algorithm, the case data collection system is required to be initialized, firstly, security parameters in the system are set, a secret key of the system is generated, then a secret key sharing stage is entered, whether an equation containing the secret key of the security parameters and the encryption algorithm is established or not is verified, if so, public parameters in a certain blockchain node and an identity mark of the patient are obtained, a private key corresponding to the identity of the patient is generated, and then data storage is carried out. After the patient is treated in a hospital, a corresponding electronic medical record is generated in a small program at a mobile terminal, after desensitization, the hospital is used as a data holder, case information is stored, a hash value of the case is calculated, the case information is encrypted by using a public key to obtain a ciphertext, the ciphertext is stored in a database of a medical record data collection system by the hospital and is issued to a blockchain network, a doctor and the patient subjected to identity verification browse data in the blockchain, a request is sent to the holder, and the holder performs identity confirmation. After the verification is successful, the data requesting party uses the private key to decrypt and acquire the case information.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the key decryption mode is adopted, so that the case information of the patient can be more ensured and the reliability of the hospital is improved when the patient information is collected. The hospital can manage the patient information conveniently.
In another embodiment, the telemedicine module includes:
the data processing unit is used for acquiring the update state of the case information of the patient in real time, and the case information of the patient is automatically updated by the case data collecting system after each patient visit is completed; the patient can use the ID card number to inquire the update state at the same time;
the disease analysis unit is used for automatically evaluating the liver disease of the patient, evaluating the body condition of the liver disease of the patient according to the case information of the patient, constructing a liver evaluation model, and inputting a preset health value into the liver evaluation model in a deep learning mode;
the intelligent early warning unit is used for reminding the health state of the patient by the case data collection system, acquiring various examination data in the case information of the patient, inputting the examination data into the liver assessment model, and carrying out early warning on the health state of the patient according to the data comparison function of the liver assessment model.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the health state of the patient is intelligently estimated through the case data collection system, corresponding early warning is made, the workload of doctors is reduced, and the probability that the patient can treat the disease in time is greatly improved.
Referring to fig. 3, in another embodiment, after the intelligent pre-warning unit, the method further includes:
the intelligent recommending unit is used for intelligently recommending doctor to visit according to early warning, setting early warning prompt grades, recommending common doctors and expert doctors according to the grades respectively, and enabling patients to select corresponding doctors to conduct appointment registration according to the early warning prompt grades;
the remote medical unit is used for carrying out remote diagnosis and treatment on patients, the patients provide data in the liver assessment model and an examination report form for a registering doctor as consultation information, and the consultation mode is video communication;
the expert consultation unit is used for remotely consultation of the patient by the doctor, the registering doctor acquires consultation information of the patient to carry out consultation, the consultation content comprises image diagnosis analysis and pathological diagnosis analysis, and the registering doctor returns a prescription result to the case data collecting system to record the consultation content after the analysis is completed.
The beneficial effects of the technical scheme are as follows: by adopting the scheme provided by the embodiment, the patient can be conveniently treated in time through remote consultation, so that the patient can enjoy the doctor service without going out of home, the humanized function of the data case collection system is improved, and the doctor watching efficiency of the patient is improved. Traditionally, pathologists have relied on paper or email to collect case data, which can involve extensive communication and manual grooming. Through the small program, the patient can directly upload the data to the system, so that an intermediate link is omitted, and the whole process is more efficient. The pathologist can quickly acquire the required data, and perform more accurate diagnosis and analysis, thereby providing better medical services for patients.
In addition, the hepatopathy case data collection applet can be integrated with other medical systems to realize more comprehensive and convenient medical information management. For example, the system can be connected with an electronic medical record system of a clinician, so that real-time data sharing and communication are realized, and the cooperation efficiency is improved. Meanwhile, the system can be integrated with an image system of a hospital, so that a pathologist can conveniently check and analyze related image data.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The utility model provides a liver disease pathology consultation case data collection system based on applet which characterized in that includes:
the data uploading module is used for uploading case information to an applet of the case data collecting system by a patient, wherein the case information comprises a patient examination report, and the patient uploads the items in the examination report in a photographing uploading and text uploading mode after logging in;
the classification diagnosis module is used for classifying subtypes of liver diseases, acquiring liver tumor CT images of patients, and performing diagnosis analysis by retrieving case information of the patients in the examination report;
the encryption sharing module is used for sharing case information among doctors and patients, desensitizing and encrypting the case information of the patients, and the doctor and the patient acquire the viewing authority of the case information through encryption keys respectively;
the remote medical module is used for remotely visiting the patient, acquiring subtype classification results of the liver diseases, carrying out health early warning on the patient and recommending an on-line doctor to visit.
2. The small-program-based liver disease pathology consultation case data collection system of claim 1, wherein the data uploading module comprises:
the information collection unit is used for storing personal information of the patient, and the case data collection system is used for collecting basic information, personal history, past history and family history of the patient as an examination report, so that the patient can conveniently inquire; personal history includes options of medication used, history of infection, alcohol consumption, and smoking; the past history includes options of whether diabetes, hepatitis, and hypertension;
the information uploading unit is used for uploading the examination report to the patient, and the patient selects options corresponding to the patient symptoms and the personal conditions in the examination report according to the personal conditions and then uploads the options to the medical record data collecting system; meanwhile, the patient perfects the examination report by inputting options which do not appear in the examination report in the personal history and the past history.
3. The applet-based liver disease pathology consultation case data collection system of claim 2, further comprising, after the information uploading unit:
a family grading unit for grading the relatives in the family history, taking the direct relatives in the family history as first-class relatives, taking the non-direct relatives as second-class relatives, marking the relatives in the family history according to the grades, and perfecting the patient information by filling in the personal history and family history of the relatives in the examination report;
The photographing and uploading unit is used for photographing and uploading information in the report form by the patient, the case data collecting system provides report photo uploading according to blood convention and liver biochemistry, and the patient uploads the photo by selecting real-time photographing and a mobile phone album;
and the patient login unit is used for checking patient information by a doctor, and searching and checking the examination report uploaded by the patient by searching the name, the mobile phone number and the identity card number of the patient by the case data collection system in the patient login applet.
4. The applet-based liver disease pathology consultation case data collection system of claim 1, wherein the classification diagnosis module comprises:
the pathological classification unit is used for classifying subtypes of liver diseases, the case data collection system divides the liver diseases into two collection pages of neoplastic lesions and non-neoplastic lesions, and a patient performs information filling by scanning corresponding two-dimensional codes in a login interface of the case data collection system in a small program and controls filling time within a preset time range;
the information retrieval unit is used for retrieving the case information of the patient, taking the identification card number of the patient as the unique identification for checking the case information of the patient, displaying the case information submitted by the patient in the interface of the patient login by the applet, providing a doctor-patient inquiry function, and inquiring the past information of the patient by the doctor through the automatic retrieval function after the patient uploads the checking report.
5. The small-program-based liver disease pathology consultation case data collection system of claim 4, wherein the pathology classification unit comprises:
the self-supervision training subunit is used for training a self-supervision learning model based on rotation prediction, acquiring a plurality of unlabeled liver tumor CT images, and performing feature marking on lesion areas in the liver tumor CT images to serve as unlabeled liver tumor data;
the contrast training subunit is used for training the unlabeled liver tumor data, constructing a feature extraction network and a feature discriminator, presetting training parameters in a self-supervision learning model to fix the training parameters as a feature extractor of a liver tumor CT image, acquiring the unlabeled liver tumor data and inputting the unlabeled liver tumor data into the self-supervision learning model;
and the sample discrimination subunit is used for extracting subtype of liver tumor from the self-supervision learning model and the feature extraction network, and discriminating the true sample and the false sample of the liver tumor CT image by the feature discriminator through applying constraint conditions.
6. The applet based liver disease pathology consultation case data collection system of claim 5, wherein the sample discrimination subunit comprises:
The data processing subunit is used for extracting and standardizing the region of interest, marking the region of interest in the liver tumor CT image, setting the region dimension of the region of interest, converting the CT value of pixels in the region dimension, and carrying out image enhancement processing on the liver tumor CT image according to the CT value;
the data collection subunit is used for training the data set, acquiring a plurality of liver tumor CT images subjected to image enhancement processing, and taking different types of tumor subtypes as the data set, wherein the data set is randomly divided into a test set and a training set, the training set is input into a self-supervision learning model, and the training set is trained by adopting a self-adaption learning rate optimization algorithm;
and the classification evaluation subunit is used for evaluating the accuracy of the liver tumor CT image classification, wherein the evaluation indexes comprise accuracy, precision and recall rate, setting a test threshold of the evaluation index, inputting the test threshold into the self-supervision learning model, and carrying out the identification classification of the liver disease subtype by the test set according to the test threshold.
7. The applet based liver disease pathology consultation case data collection system of claim 1, wherein the encryption sharing module comprises:
The data desensitization unit is used for carrying out desensitization treatment on the case information of the patient, uploading the case information to a blockchain network of the case data collection system, and carrying out desensitization operation on sensitive data in the case information by using shielding, generalization and interception modes;
the asymmetric encryption unit is used for encrypting the case information of the patient, the case information of the patient is divided into a patient examination report and a hospital diagnosis treatment scheme, and the hospital diagnosis treatment scheme is always in a ciphertext state by using an asymmetric encryption method so as to ensure that only the patient with the right and the hospital can view;
the information request unit is used for requesting to check case information by a doctor and constructing an asymmetric encryption model, and comprises a request party and an authorized party, wherein the request party is a doctor and a patient for checking the case information, and the authorized party is a case data collection system; the requester obtains the authorization of case information viewing by using a preset encryption algorithm to obtain the exchange key in the symmetric encryption model.
8. The small-program-based liver disease pathology consultation case data collection system according to claim 1, wherein the information requesting unit includes:
The initialization subunit is used for initializing the case data collection system, presetting the security parameters of the case data collection system, outputting and generating public keys, and ensuring that the security parameters in different blockchains are different;
a key generation subunit for generating a key for encrypting the case information, and verifying whether an equation associated with the encryption algorithm by the key including the security parameter is established; if so, randomly acquiring the security parameters in the blockchain node, the identity of the patient and a private key corresponding to the identity of the patient;
and the data sharing subunit is used for decrypting the ciphertext and then sharing the case information, the hospital is used as a holder of the case information to encrypt the ciphertext and issue the ciphertext to the blockchain network, and after the doctor and the patient pass through identity verification, the doctor uses the private key to decrypt and check the case information in the blockchain node.
9. The applet-based liver disease pathology consultation case data collection system of claim 1, wherein the telemedicine module comprises:
the data processing unit is used for acquiring the update state of the case information of the patient in real time, and the case information of the patient is automatically updated by the case data collecting system after each patient visit is completed; the patient can use the ID card number to inquire the update state at the same time;
The disease analysis unit is used for automatically evaluating the liver disease of the patient, evaluating the body condition of the liver disease of the patient according to the case information of the patient, constructing a liver evaluation model, and inputting a preset health value into the liver evaluation model in a deep learning mode;
the intelligent early warning unit is used for reminding the health state of the patient by the case data collection system, acquiring various examination data in the case information of the patient, inputting the examination data into the liver assessment model, and carrying out early warning on the health state of the patient according to the data comparison function of the liver assessment model.
10. The small-program-based liver disease pathology consultation case data collection system of claim 9, further comprising, after the intelligent pre-warning unit:
the intelligent recommending unit is used for intelligently recommending doctor to visit according to early warning, setting early warning prompt grades, recommending common doctors and expert doctors according to the grades respectively, and enabling patients to select corresponding doctors to conduct appointment registration according to the early warning prompt grades;
the remote medical unit is used for carrying out remote diagnosis and treatment on patients, the patients provide data in the liver assessment model and an examination report form for a registering doctor as consultation information, and the consultation mode is video communication;
The expert consultation unit is used for remotely consultation of the patient by the doctor, the registering doctor acquires consultation information of the patient to carry out consultation, the consultation content comprises image diagnosis analysis and pathological diagnosis analysis, and the registering doctor returns a prescription result to the case data collecting system to record the consultation content after the analysis is completed.
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| CN202311250727.0A CN117238518A (en) | 2023-09-26 | 2023-09-26 | Liver disease pathology consultation case data collection system based on small program |
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| CN202311250727.0A CN117238518A (en) | 2023-09-26 | 2023-09-26 | Liver disease pathology consultation case data collection system based on small program |
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