CN111402976A

CN111402976A - Intelligent supercomputer management system based on big data

Info

Publication number: CN111402976A
Application number: CN202010174133.6A
Authority: CN
Inventors: 张继亮; 许玲; 顾锡冬; 魏慧军; 姚嘉麟; 焦丽静; 周迎春
Original assignee: Shanghai Luoshu Pharmaceutical Technology Co ltd
Current assignee: Shanghai Luoshu Pharmaceutical Technology Co ltd
Priority date: 2020-03-13
Filing date: 2020-03-13
Publication date: 2020-07-10

Abstract

The invention relates to an intelligent superconcephalon management system based on big data, which at least comprises: a first terminal (1) and a data manager (4), the data manager (4) being configured to: acquiring epidemic disease data in a set region range with the residence of a specific patient as a center of a circle based on the residence information of the patient; the data manager (4) can compare the medical record data of the specific patient with the epidemic disease data to obtain the similarity between the epidemic disease data and the medical record data, wherein the data manager (4) can push the diagnosis method data corresponding to the epidemic disease data to the first terminal (1) or push the treatment measure data corresponding to the epidemic disease data to the first terminal (1) under the condition that the similarity is greater than a set threshold.

Description

Intelligent supercomputer management system based on big data

Technical Field

The invention belongs to the technical field of management, and particularly relates to an intelligent supercomputer management system based on big data.

Background

IBM proposed the concept of "Intelligent medicine" in 2009, aimed at building a "patient-centric" medical service system. By achieving a good balance among cost of service, quality of service and accessibility of service, medical practice outcomes, innovative medical service models and business markets are optimized, and a high quality personal medical service experience is provided. The smart medical treatment refers to the construction of a medical information management and service system which is complete in medical information, across service departments and centered on patients on the basis of high-tech technologies such as internet of things, cloud computing and the like in various remissions such as diagnosis, treatment, rehabilitation, payment, health management and the like.

Intelligent medical treatment generally includes several stages of development as follows: the method comprises the steps of constructing a business management system (comprising hospital charging and drug management), constructing an electronic medical record system (comprising patient information and image information), constructing a clinical application system (comprising computer doctors, medical advice input (CPOE) and the like), constructing a regional medical information exchange system, constructing a clinical support decision-making system and constructing a public health safety system. At present, the application of intelligent medical treatment in China is mainly embodied in methods such as medical and health services, medical product management, medical instrument management, telemedicine, teleeducation and the like, namely in the stages of trial and starting. The construction of an intelligent management system based on an artificial intelligence technology is particularly important in the development process of intelligent medical treatment. The intelligent management system can analyze the mass data, further provides support such as assistant decision-making for medical personnel, and can improve the diagnosis accuracy of the medical personnel to patients and accelerate the diagnosis process. In the prior art, there are many intelligent management systems with different functions.

For example, patent document CN107633884A discloses an intelligent management system for medical big data, which applies the principles and practices of classical case diagnosis and teaching in medical field to system design and implementation to form a case-based reasoning system method, and guides the diagnosis, treatment and rehabilitation of the current case with the classical case. On the other hand, in order to solve the problem of knowledge of case data, a case template is defined by a frame system knowledge representation method in artificial intelligence, and medical case big data are directly converted into available knowledge; meanwhile, a comprehensive reasoning method based on a case framework system, a medical guide and a process standard rule is developed, so that the problems of knowledge representation and reasoning intelligence of medical big data are fundamentally solved, and a technology and means are provided for intellectualization, standardization and personalization of medical services.

For example, patent document CN108717875A discloses a chronic disease intelligent management system based on big data, which includes an intelligent wearable device: the system is used for acquiring vital sign data of a user and receiving an analysis result returned by the network transmission module; a network transmission module: the data analysis module is used for sending the vital sign data acquired by the intelligent wearable equipment to the cloud end and transmitting the analysis result of the data analysis module back to the intelligent wearable equipment worn by the user; a data analysis module: the system is used for establishing a pathological model and matching the pathological model with the vital sign data sent by the network transmission module to obtain an analysis result; big data storage module: used for storing pathological models, collected vital sign data and analysis results. The invention has the characteristics of relieving the psychological pressure of the disease on the patient, providing a good decision basis for the diagnosis of the patient by the doctor and having accurate and reliable diagnosis results.

In summary, in the prior art, the epidemic disease data of the staged outbreak cannot be collected and used for auxiliary diagnosis of the disease type of the patient. Therefore, the present invention is directed to an intelligent supercomputer management system that overcomes the above-mentioned drawbacks.

Furthermore, on the one hand, due to the differences in understanding to the person skilled in the art; on the other hand, since the inventor has studied a lot of documents and patents when making the present invention, but the space is not limited to the details and contents listed in the above, however, the present invention is by no means free of the features of the prior art, but the present invention has been provided with all the features of the prior art, and the applicant reserves the right to increase the related prior art in the background.

Disclosure of Invention

The word "module" as used herein describes any type of hardware, software, or combination of hardware and software that is capable of performing the functions associated with the "module".

Aiming at the defects of the prior art, the invention provides an intelligent supercomputer management system based on big data, which at least comprises: the first terminal can be used for acquiring identity data and medical record data of a patient and storing the identity data and the medical record data in the data storage center; the data storage center can enable symptom data of a plurality of diseases stored in the data storage center and treatment measure data and diagnosis method data corresponding to the plurality of diseases to be transmitted to the first terminal based on an interactive request of the first terminal; a data manager capable of comparing the medical record data acquired by the first terminal with the symptom data stored in the data storage center to determine at least one suspected disease type, wherein the data manager is capable of screening the data storage center for therapeutic measure data and/or diagnostic method data that substantially matches the medical record data acquired by the first terminal based on the at least one suspected disease type, the data manager configured to screen the therapeutic measure data and/or the diagnostic method data according to the following steps: acquiring popular disease data within a set area range with the residence of a specific patient identified based on the identity data of the first terminal as a center of a circle based on the residence information of the patient acquired by the first terminal; the data manager can compare the medical record data of the specific patient stored in the data storage center with the popular disease data determined by the first terminal, and acquire the similarity between the popular disease data and the medical record data of the specific patient acquired by the first terminal, wherein when the similarity is greater than a set threshold and the diagnosis method data corresponding to the suspected disease type with the largest matching degree in the data storage center cannot confirm the specific patient, the data manager can push the diagnosis method data corresponding to the popular disease data to the first terminal or push the treatment measure data corresponding to the popular disease data to the first terminal. The common outbreak of the epidemic disease can cause different users in the same area to suffer from the epidemic disease in sequence, and the relevant treatment data generated during the treatment process of the patient suffering from the epidemic disease in advance can provide reliable reference for the subsequent patients. For example, a specific drug for the epidemic disease can be obtained by referring to treatment data of a previous patient, and the specific drug can be used for treatment of a subsequent patient, thereby achieving the purpose of shortening the treatment period for an individual patient. According to a preferred embodiment, the data manager is further configured to screen the treatment measure data and/or the diagnosis determination method data according to the following steps: acquiring at least one second patient whose residence is less than a set threshold from the residence of the first patient based on the residence information of the first patient acquired by the first terminal, and acquiring at least one third patient whose coincidence with the medical record data of the first patient is greater than the set threshold based on the medical record data of the second patient stored in the data storage center; when the disease type corresponding to the diagnosis data of the third patient is different from all the suspected disease types, and the diagnosis method data corresponding to the suspected disease type with the largest matching degree in the data storage center cannot complete diagnosis of the first patient, the data manager can push the diagnosis method data of the third patient stored in the data storage center to the first terminal, so that the inquiry of the first patient can be completed based on the diagnosis method data of the third patient. In the prior art, the intelligent superconcephalon management system does not count the residence information of the patient. In the residential area, the drinking conditions, eating habits and environmental factors are slightly different, so that a plurality of people can suffer from the same disease. For example, in a residential area, drinking water conditions are the same, which, when water quality is affected, can cause multiple people in the residential area to suffer from digestive tract diseases. Alternatively, many people in the area of residence may suffer from upper respiratory illness due to environmental factors. For diseases such as cancer, it is usually characterized by strong latency and unobvious symptoms, which makes its diagnosis and discovery difficult. For example, in actual clinical cases, symptoms of lung cancer may include leg pain. Since the site involved in the symptom is not related to the disease focus, when a general doctor or a doctor with little experience performs diagnosis and treatment, for example, the general doctor or the doctor cannot take medicines according to the symptoms, the disease condition is further worsened, and finally, the cancer is in an advanced stage once found. Or require a number of diagnostic aids such as radiographs, CT examinations, etc. to confirm the disease type, thereby increasing the cost of medical care and increasing the risk of missing the optimal treatment time. By analyzing the population around the residence of the patient, the invention can find the disease with unobvious symptoms in time based on the regional characteristics.

According to a preferred embodiment, the data manager is further configured to screen the treatment measure data and/or the diagnosis determination method data according to the following steps: in the case where at least one suspected disease type determined by the data manager by comparing medical record data acquired by the first terminal with symptom data in the data storage center is the same as a disease type corresponding to confirmed data of a third patient stored in the data storage center, pushing the diagnosis determining method data of the third patient stored in the data storage center to the first terminal to at least complete the inquiry diagnosis of the first patient, or in the case that the confirmed data stored in the data storage center of the third patient corresponds to the same disease type as the at least one suspected disease type, determining at least one drug class for the disease type based on the third patient's treatment data, and pushing the at least one drug category to the first terminal such that the corresponding drug involved in the therapeutic measure data can be replaced by the at least one drug category.

According to a preferred embodiment, the first terminal is further configured to collect recovery data of the patient, and the data manager is configured to evaluate the efficacy of the treatment on the patient based on the recovery data, wherein the data manager is configured to adjust the filtered therapeutic measure data as follows: establishing at least a first level at which the therapeutic effect meets the desired effect and a second level at which the therapeutic effect does not meet the desired effect; according to the time sequence, a plurality of recovery data formed by a plurality of treatment processes of a patient aiming at the same disease are obtained based on the first terminal, and the plurality of recovery data are evaluated to obtain a plurality of evaluation results; and dividing the grades of the plurality of evaluation results, wherein under the condition that the grades of the evaluation results are gradually increased, a prompt is sent out through the first terminal and/or the data manager, so that the medicine type and/or the medicine consumption can be changed.

According to a preferred embodiment, in the case where the data storage center is configured to be able to store patient-specific medication data from the second terminal, the intelligent superconcephalon management system is further configured to change the type of medication and/or the amount of medication as follows: counting medication data of a specific patient identified based on the identity data of the first terminal for the same disease type, so as to obtain the frequency and/or the quantity of the same or similar medicines continuously and repeatedly taken by the specific patient in a time period set by the second terminal according to the first terminal; and sending out a prompt through the second terminal and the first terminal to prompt the change of the medication type and/or the medication amount under the condition that the data manager obtains that the frequency and/or the number of the same or similar medicines continuously and repeatedly taken by the patient is larger than a set threshold value based on the analysis module of the data manager. In the prior art, the intelligent superconcephalon management system does not count the medication data of the patient aiming at the same disease, and in an actual situation, the patient does not select a large hospital to see a doctor firstly for all diseases, for example, aiming at cold, and the patient can be treated for multiple times in a small clinic. The sub-diagnosis system is not usually provided in the small clinic, so that the medication data used by the patient in the small clinic is lost. Meanwhile, in practical cases, since the physician has limited experience, the small clinic may repeatedly use the same prescription for the same disease. After a plurality of treatments, a patient can gradually generate resistance to the medicine in the prescription, so that the prescription completely loses the treatment efficacy, and finally, after the condition of an illness deteriorates, the patient can choose to go to a large hospital configured with an intelligent supercomputer management system for treatment. According to the invention, the first terminal is configured, so that the patient can input the medication data of the patient, and further all the medication data of the patient aiming at the same disease can be stored, so that the treatment measure data finally given by the intelligent superconcephalon management system is more effective. Meanwhile, through the analysis of the medication data of the patient, the same drug can be prevented from being reused in a short time, and the risk of the disease generating resistance to the drug can be reduced.

According to a preferred embodiment, the intelligent supercomputer management system can be configured with at least one cloud server and a plurality of data managers loaded to different hospitals, and the cloud server and the at least one data manager can perform data transmission operation as follows: under the condition that the data manager performs a data transmission operation to transmit data to the cloud server, the data manager generates a first permission request and a second permission request based on the data transmission operation, wherein the first permission request is transmitted to the first terminal, and the second permission request is transmitted to the second terminal; the first terminal performs a first modification operation on the data needing to be transmitted by the data manager according to the first permission request, and the second terminal performs a second modification operation on the data needing to be transmitted by the data manager according to the second permission request, so that the private data in the data needing to be transmitted by the data manager can be partially deleted based on the first modification operation and the second modification operation.

According to a preferred embodiment, each of said data managers is capable of configuring at least one of said first terminals, at least one second terminal and at least one data storage center, wherein: the data manager of the first hospital can transmit the access requirement to the data manager of the second hospital through the cloud server, and the data manager of the first hospital can access the data storage center of the second hospital under the condition that the access permission of the data manager of the second hospital is obtained.

According to a preferred embodiment, the first terminal can establish a communication connection with a designated second terminal in a manner of initiating an access request to the designated second terminal, or in a case that the first terminal sends the access request to the data manager, the data manager can send the access request to the second terminal in an idle state, so that the first terminal can establish a communication connection with the second terminal.

The invention also provides an intelligent medical system, which at least comprises: a data manager capable of comparing medical record data with symptom data to determine at least one suspected disease type, the data manager configured to screen treatment data and/or method of determination data as follows: acquiring popular disease data within a set area range with the residence of a specific patient identified based on the identity data of the first terminal as a center of a circle based on the residence information of the patient acquired by the first terminal; the data manager determines similarity between medical record data and popular disease data based on comparison between the medical record data and the popular disease data of the specific patient, wherein when the similarity is larger than a set threshold and the diagnosis method data corresponding to the suspected disease type with the largest matching degree in the data storage center cannot confirm the specific patient, the data manager can push the diagnosis method data corresponding to the popular disease data to the first terminal to provide reference basis for inquiry of the specific patient, or the data manager can push the treatment measure data corresponding to the popular disease data to the first terminal to provide reference basis for treatment of the specific patient.

According to a preferred embodiment, the data manager is further configured to screen the treatment measure data and/or the diagnosis determination method data according to the following steps: acquiring at least one second patient of which the distance between the residence and the residence of the first patient is smaller than a set threshold value based on the residence information of the first patient, and acquiring at least one third patient of which the coincidence degree with the medical record data of the first patient is larger than the set threshold value based on the medical record data of the second patient; when the disease type corresponding to the diagnosis data of the third patient is different from all the suspected disease types and the confirmed diagnosis method data corresponding to the suspected disease type with the largest matching degree cannot complete the confirmed diagnosis of the first patient, the data manager can push the confirmed diagnosis method data of the third patient, which is stored in the data storage center, to the first terminal, so that the inquiry of the first patient can be completed at least based on the confirmed diagnosis method data of the third patient.

Drawings

FIG. 1 is a schematic diagram of a preferred intelligent superconcephalon management system of the present invention; and

fig. 2 is a schematic diagram of an operating principle of a preferred cloud server according to the present invention.

List of reference numerals

1: the first terminal 2: the second terminal 3: data storage center

4: the data manager 5: cloud server 6: data analysis unit

7: data storage unit 8: the interaction unit 9: communication unit

3 a: first storage unit 3 b: second storage unit 3 c: third memory cell

3 d: fourth storage unit 3 e: the fifth memory cell

4 a: the data acquisition unit 4 b: the data sorting unit 4 c: data matching unit

4 d: aid decision unit

Detailed Description

The following detailed description is made with reference to the accompanying drawings.

Example 1

As shown in fig. 1 and fig. 2, the present invention provides an intelligent supercomputer management system based on artificial intelligence, which at least includes a first terminal 1, a second terminal 2, a data storage center 3 and a data manager 4. The first terminal 1, the second terminal 2 and the data storage center 3 can each be communicatively coupled to a data manager 4. Alternatively, the first terminal 1, the second terminal 2, the data storage center 3 and the data manager 4 can be communicatively coupled to each other, so that the first terminal 1, the second terminal 2, the data storage center 3 and the data manager 4 can perform data transmission with each other. The first terminal 1 is configured for use by a patient. The patient can input the identity data and the medical record data through the first terminal 1. The identity data is used to confirm its identity to facilitate differentiation from other patients. The identity data may include name, gender, age, identification number, etc. Medical record data refers to data that can be used to characterize the status of its condition. For example, medical record data can include data on the duration of physical discomfort, specific symptoms of discomfort, and the like. The identity data and the medical record data can be transmitted to the data storage center 3 for classified storage. That is, the data storage center 3 can individually configure a storage space for each patient, and the storage space can store all relevant identity data and medical record data about a certain patient. The data manager 4 is capable of analyzing medical record data input by the first terminal 1 to make an aid in medical decision making. The aid decision may include the type of disease the patient may suffer from and its corresponding probability. For example, when the specific discomfort symptom data in the medical record data is headache, the data manager may search the data in the data storage center 3 to find out the disease categories corresponding to all patients with headache symptoms. The probability of each disease type can be calculated statistically according to the number of the diseases. The greater the number of occurrences, the greater the probability of occurrence of the disease. It is understood that the medical record data may also include auxiliary judgment data to facilitate the data manager 4 to adjust the occurrence probability of each disease. For example, the auxiliary judgment data may include image data of a tongue, image data of a face, B-ultrasonic data, CT data, and the like of the patient. The auxiliary judgment data can provide an additional judgment basis, so that the data manager 4 can improve the judgment accuracy. The second terminal 2 can establish a communication connection with the first terminal 1 according to the auxiliary diagnosis and treatment decision generated by the data manager 4, so that the medical staff can finally confirm the disease type of the patient in an inquiry form.

Preferably, it can be understood that the data manager 4 may have a built-in artificial intelligence algorithm such as machine learning or deep learning, and the data manager 4 may further perform continuous self-learning to achieve the purpose of improving the determination accuracy. The artificial intelligence algorithm can receive medical record data recorded by a patient in the diagnosis process and diagnosis confirmation data finally formed by the second terminal 2, and the artificial intelligence algorithm can adjust the weight values of the parameters through the diagnosis confirmation data, so that the auxiliary diagnosis decision of the data manager 4 is more and more accurate. For example, the artificial intelligence algorithm may be an artificial neural network algorithm. Artificial neural network algorithms include, but are not limited to, recurrent neural networks, bidirectional recurrent neural networks, deep neural networks, convolutional neural networks, and stochastic neural networks.

Preferably, referring again to fig. 1, the intelligent supercomputer management system can further comprise at least one cloud server 5. Each hospital may be provided with a number of first terminals 1, a number of second terminals 2, at least one data storage center 3 and at least one data manager 4. The data manager 4 is communicatively coupled to the cloud server 5. For example, the data manager 4 can be communicatively coupled to the cloud server 5 via a wireless network. The cloud server 5 is used for realizing data sharing in the data storage centers 3 of different hospitals. Specifically, medical personnel in the first hospital can make an access request to the cloud server 5 through the data manager 4. The cloud server 5 may transmit the access requirement to the data manager 4 of the second hospital, and under the condition of permission of the data manager 4 of the second hospital, the medical staff of the first hospital can access the data in the data storage center 3 of the second hospital. Or, medical personnel of different hospitals can upload the treatment data of the patient to the cloud server 5 through their respective data managers 4, and then medical personnel of different hospitals can visit the cloud server 5 through their respective data managers 4, thereby realizing data sharing.

Preferably, the data storage center 3 includes at least a first storage unit 3a, a second storage unit 3b, and a third storage unit 3 c. The first storage unit 3a is used for storing medical record data and identity data of different patients. The second storage unit 3b is used to store symptom data of various diseases. For example, symptoms of a cold may include coughing, dry throat, runny nose, and the like. The third storage unit 3b is used for storing therapeutic measure data and diagnosis confirming method data corresponding to various diseases. The therapeutic data may refer to data of a formula capable of treating the corresponding disease. Diagnostic method data may refer to diagnostic measure data necessary to diagnose the type of disease. For example, when the medical record data of the patient is too small to confirm the disease type of the patient, the diagnosis confirming method data may be diagnostic data such as CT data that can assist the confirmation of the disease type. For example, the specialist doctor can accurately determine the focus of the patient by means of inquiry by virtue of abundant treatment experience, and then the medicine is administered according to the symptoms. Therefore, the diagnostic method data may be inquiry data formed based on the clinical experience of the specialist doctor in each field.

Preferably, as shown in fig. 2, the data manager 4 at least includes a data acquisition unit 4a, a data classification unit 4b, a data matching unit 4c and an assistant decision unit 4 d. The data acquisition unit 4a is used for receiving the identity data and the medical record data input by the first terminal 1. The data classifying unit 4b can extract keywords in the medical record data, classify the patient according to the extracted keywords, and store the medical record data corresponding to the extracted keywords into the corresponding first storage unit 3 a. The medical record data can be associated with the corresponding patient, and all the medical record data of the patient can be obtained only by inputting the identity data of the patient during retrieval. For example, medical record data can include keywords such as "headache, cough, limp limbs," and the like. The data classification unit 4b can preliminarily determine the disease type according to the medical record data, and then store the medical record data of the patient according to the disease type. The data matching unit 4c is used for infusing the patientThe entered medical record data is compared with the symptom data stored in the second storage unit 3b, and then the matching degree of the medical record data and various diseases can be determined. The matching degree is the degree of coincidence between the symptom and the symptom data in the medical record data. For example, the disease a in the second storage unit 3B may include a specific symptoms, and the symptoms in the medical record data may include B symptoms. When the symptoms of the medical record data coincide with the symptoms in the symptom data by n, the matching degree can be determined by

And (4) performing representation.

A larger value of (a) indicates a larger degree of matching. The data matching unit 4c can select a plurality of suspected disease types according to the mode that the matching degree is from large to small. For example, a set threshold value of the matching degree can be set, and then the disease type with the matching degree larger than the set threshold value can be selected for the reference of the medical staff. The aid decision unit 4d can select the corresponding treatment data and/or diagnostic data from the third memory unit 3c depending on the type of disease selected by the data matching unit 4 c. The treatment data and/or the diagnosis determination data selected by the aid decision unit 4d can be transmitted to the second terminal 2 for reference by the medical staff.

Preferably, the first terminal 1 and the second terminal 2 may each include a data analysis unit 6, a data storage unit 7, an interaction unit 8, and a communication unit 9. The interaction unit 8 is used for data interaction with a patient or a medical staff. The interactive unit 8 can be a mouse, a keyboard, an image collector, a voice recorder and the like, and then through the interactive unit 8, the patient can input the identity data and the medical record data into the first terminal 1, and medical personnel can input the access requirements into the second terminal 2. The data storage unit 7 can be used for temporarily storing patient identity data and medical record data, and it can also be used for storing access requirement data of medical staff. The data storage unit 7 may be a computer readable storage medium including, but not limited to, an internal memory and an external memory. The data analysis unit 6 can be used for processing the data it receives and then transmitting it to the data manager 4 or the data storage center 3. The communication unit 9 can be used for communication between the data analysis unit 6, the data storage unit 7 and the interaction unit 8.

For ease of understanding, the working principle of the intelligent supercomputer management system of the present invention will be explained.

The first terminal 1 and the second terminal 2 can be in communication connection with each other, and the integration of the data manager 4 enables the patient at the site A to communicate with the medical staff at the site B in real time through the first terminal 1. The patient can register on the first terminal 1 by means of the identity data, and thus obtain the usage right of the first terminal 1. The medical staff can also register on the second terminal 2 by the identity data thereof, and further obtain the use authority of the second terminal 2. The patient first uploads his medical record data to the first terminal 1. The medical record data can be realized in the form of images, characters or voice. The first terminal 1 can transmit the received medical record data to the data manager 4 for analysis and processing, and further obtain a plurality of suspected disease types and treatment measure data and/or diagnosis confirmation method data corresponding to the suspected disease types. The data manager 4 can transmit the corresponding patient identity data, medical record data, treatment measure data and diagnosis confirming method data to the second terminal 2 in an idle state in the corresponding department according to the suspected disease type, so that the second terminal 2 can establish communication connection with the first terminal 1, and finally medical staff can confirm the focus of the patient in an inquiry form. Preferably, the patient can also initiate an access request to a designated second terminal 2 via the first terminal 1. That is, the patient may designate a particular healthcare worker for whom to treat.

Example 2

This embodiment is a further improvement of embodiment 1, and repeated contents are not described again.

Preferably, the first terminal 1 is also able to acquire recovery data of the patient. Recovery data refers to information that can characterize a patient's treatment. The data storage center 3 may include a fifth storage unit 3e so that the restoration data can be stored in the fifth storage unit 3 e. For example, the recovery data may be response information after the patient takes the medicine, time node information of disease weakening, time node data of disease recovery, and the like. By retrieving the data, the data manager 4 can evaluate the patient's efficacy to produce an evaluation result for adjusting the therapeutic measure data. The evaluation result may be divided into a first grade and a second grade. The first grade indicates that the efficacy meets the expected requirements. The second level indicates that the efficacy does not meet the expected requirements. For example, when the time node of no adverse reaction or weakened disease condition of the patient after taking the medicine meets the expected requirement or the time node of disease rehabilitation meets the expected requirement, the evaluation result is classified into a first grade. And when the time node of the patient with adverse reaction and weakened disease condition does not meet the expected requirement or the time node of the patient with recovered disease condition does not meet the expected requirement after the patient takes the medicine, dividing the evaluation grade into a second grade. During the treatment of the next course of treatment of the patient, the medical staff can adjust the treatment measure data according to the evaluation result to obtain better treatment effect. It can be understood that, according to actual needs, those skilled in the art can subdivide the evaluation result into several levels, and make corresponding adjustment strategies for the therapeutic measure data for different levels.

Preferably, the data storage center 3 can further include a fourth storage unit 3 d. The fourth storage unit 3d can be used to store medication data for each patient for a set disease. The medication data at least includes information such as the type of medication, the amount of medication, the time of medication, etc. of the patient. For example, a patient may experience several colds over their lifetime, and each time the patient undergoes a treatment, the healthcare worker may administer a different type or amount of medication. The fourth storage unit 3d can record the medication type and the medication amount of the medication of the patient in the order of the medication time. The data manager 4 is configured to adjust the therapy data it screens as follows:

a1: medication data for a specific patient identified based on the identity data of the first terminal 1 for the same disease type is counted in relation to the residence information provided by the first terminal 1, thereby obtaining the frequency with which the same or similar medication is continuously repeatedly taken by the specific patient during the time period set by the second terminal 2 in accordance with the first terminal 1.

Specifically, the set time period may be one year. Patients may suffer from disease a m times during a set time period of the year. For example, m may be 5 times, and when the B drug is taken in 5 times, the B drug is continuously and repeatedly taken at a frequency of 5 times/year. When the drug B is not taken at least once in 5 times, the frequency can be determined by calculating the average value. Specifically, the time for the patient to first develop disease a is 1 month. The time for the patient to have disease a second time is 3 months. The time for the patient to develop disease a for the third time is 8 months. The fourth time the patient had disease a was 11 months. The patient had disease A for a fifth time of 12 months, wherein the patient did not take drug B at the time of disease A for the third time. The frequency of continuous repeated taking of the medicine B is that the medicine B is continuously taken for a set period of 1 to 3 months

And (4) times/month, wherein the frequency of continuously and repeatedly taking the medicine B by the patient in a set time period from 11 months to 12 months is 1 time/month. Finally, the frequency of continuous repeated taking of the B medicament is as follows in a set time period of one year

Second/month.

A2: in the case where the data manager 4 confirms that the frequency and/or number of continuous repeated administrations of the same or similar drug by a specific patient associated with specific residence information is greater than a set threshold value based on the analysis performed by its analysis module in a time-dependent manner, the medication type and/or dosage is changed.

Specifically, the gradual increase of the grade of the evaluation result, or the frequency of the same or similar medicine being continuously and repeatedly taken by the patient is greater than the set threshold value, indicates that the patient has already developed a certain resistance to the medicine, and if the medicine is continuously taken again, the curative effect is further reduced. Therefore, the kind of administration can be changed or the amount of administration can be reduced. For example, a change in drug class may select a drug that has negative cross-resistance with the current drug to replace the current drug. The analysis module may be an operator having a data operation function. It is possible to obtain the frequency with which the medicine is continuously taken by the patient according to the number of times the patient takes a particular medicine within a set time.

A3: in the case where the data manager 4, based on its analysis module, finds that the frequency and/or number of consecutive repeated administrations of the same or similar drug in a specific residential area is greater than a set threshold, a reminder is issued to the medical administrative authority via the data manager 4. The medical administrative department can find the infection source and the group infection condition in time through the reminding sent by the data manager. Further, the risk of delaying treatment when the discovery is not timely can be reduced.

Through the mode, the following technical effects can be at least achieved: in the prior art, the intelligent superconcephalon management system does not count the medication data of the patient aiming at the same disease, and in an actual situation, the patient does not select a large hospital to see a doctor firstly for all diseases, for example, aiming at cold, and the patient can be treated for multiple times in a small clinic. The sub-diagnosis system is not usually provided in the small clinic, so that the medication data used by the patient in the small clinic is lost. Meanwhile, in practical cases, since the physician has limited experience, the small clinic may repeatedly use the same prescription for the same disease. After a plurality of treatments, a patient can gradually generate resistance to the medicine in the prescription, so that the prescription completely loses the treatment efficacy, and finally, after the condition of an illness deteriorates, the patient can choose to go to a large hospital configured with an intelligent supercomputer management system for treatment. According to the invention, the first terminal 1 is configured, so that the patient can input the medication data of the patient, and further all the medication data of the patient aiming at the same disease can be stored, so that the treatment measure data finally given by the intelligent superconcephalon management system is more effective. Meanwhile, through the analysis of the medication data of the patient, the same drug can be prevented from being reused in a short time, and the risk of the disease generating resistance to the drug can be reduced.

Example 3

This embodiment is a further improvement on

embodiments

1 and 2, and repeated details are not repeated.

Preferably, the identity data comprises at least residence information of the patient. In case the first terminal 1 transmits the identity data and medical record data of the patient it has acquired to the data manager 4, the data manager 4 also obtains the therapeutic measure data and/or the method of determination data as follows:

b1: at least one second patient whose residence is less than a set threshold from the residence of the first patient is acquired based on the residence information of the first patient.

Specifically, the data sorting unit 4b of the data manager 4 can extract the information according to the residence of the first patient and place it into its built-in map. The residence information for each patient can be placed in the map in the form of points. The data classification unit 4b will establish a circular coverage area with a radius R around the first patient's residence, and a falling within the circular coverage area indicates that the distance between the residence and the first patient's residence is less than the set threshold. The set threshold may be adjusted based on the R value. That is, one skilled in the art may adjust the R value to enable the at least one second patient to fall within the circular coverage area as the case may be.

B2: and acquiring at least one third patient with the coincidence degree of the medical record data of the first patient larger than a set threshold value based on the medical record data of the second patient.

In particular, the data matching unit 4c can compare medical record data of the second patient with medical record data of the first patient. The degree of coincidence can be determined by the amount of specific discomfort symptom data that the first patient and the second patient coincide with each other. The person skilled in the art can set the threshold value for the degree of overlap according to the actual requirements.

B3: in the case where the treatment data and/or method of determination data is obtained based on a match of the medical record data and the symptom data of the first patient, the method of determination data of the first patient is adjusted based on the method of determination data of the third patient, or the treatment data of the first patient is adjusted based on the treatment data of the third patient.

Specifically, based on the comparison between the medical record data of the first patient and the symptom data stored in the second storage unit 3b, the matching degree between the medical record data and various diseases can be determined, and then a plurality of suspected disease types can be screened out. The assistant decision unit 4d can preliminarily determine the corresponding therapeutic measure data and/or the diagnosis confirming method data from the third storage unit 3c according to the screened plurality of suspected disease types. According to the matching degree, the suspected disease types can be prioritized. That is, the greater the degree of matching, the greater the priority. The smaller the degree of matching, the smaller the priority. Adjusting the method of determining data for the first patient based on the method of determining data for the third patient comprises at least the steps of:

c1: in the case that the disease type corresponding to the diagnosis data of the third patient is the same as the at least one suspected disease type, the data manager 4 pushes the diagnosis method data of the third patient stored in the data storage center 3 to the first terminal 1, so that the diagnosis of at least the first patient is completed based on the diagnosis method data of the third patient.

C2: in the case that the disease type corresponding to the diagnosis data of the third patient is different from all the suspected disease types, and the confirmed diagnosis cannot be completed for the first patient based on the diagnosis method data corresponding to the suspected disease type with the largest matching degree, the data manager 4 can push the diagnosis method data of the third patient stored in the data storage center 3 to the first terminal 1, so that the inquiry for at least the first patient is completed based on the diagnosis method data of the third patient.

Preferably, in the case that the disease type corresponding to the diagnosis data of the third patient is the same as the at least one suspected disease type, at least one drug category for the disease type is determined based on the treatment data of the third patient, and the at least one drug category is pushed to the first terminal 1 and/or the second terminal through the data manager 4, so that the treatment data is replaced based on the at least one drug category. For example, at least one drug in the therapeutic measure data may be replaced with a drug corresponding to the at least one drug category.

Through the mode, the following technical effects can be at least achieved: in the prior art, the intelligent superconcephalon management system does not count the residence information of the patient. In the residential area, the drinking conditions, eating habits and environmental factors are slightly different, so that a plurality of people can suffer from the same disease. For example, in a residential area, drinking water conditions are the same, which, when water quality is affected, can cause multiple people in the residential area to suffer from digestive tract diseases. Alternatively, many people in the area of residence may suffer from upper respiratory illness due to environmental factors. For diseases such as cancer, it is usually characterized by strong latency and unobvious symptoms, which makes its diagnosis and discovery difficult. For example, in actual clinical cases, symptoms of lung cancer may include leg pain. Since the site involved in the symptom is not related to the disease focus, when a general doctor or a doctor with little experience performs diagnosis and treatment, for example, the general doctor or the doctor cannot take medicines according to the symptoms, the disease condition is further worsened, and finally, the cancer is in an advanced stage once found. Or require a number of diagnostic aids such as radiographs, CT examinations, etc. to confirm the disease type, thereby increasing the cost of medical care and increasing the risk of missing the optimal treatment time. By analyzing the population around the residence of the patient, the invention can find the disease with unobvious symptoms in time based on the regional characteristics.

Example 4

This embodiment is a further improvement of the foregoing embodiment, and repeated contents are not described again.

The invention also provides an intelligent medical system which at least comprises a data manager 4. The data manager 4 can compare the medical record data with the symptom data to determine at least one suspected disease type. The data manager 4 is configured to screen the therapeutic measure data and/or the diagnostic method data as follows: based on the residence information of the patient collected by the first terminal 1, the epidemic disease data within the set area range centered on the residence of the specific patient identified based on the identity data of the first terminal 1 is acquired. The data manager 4 determines similarity between the medical record data and the popular disease data based on comparison between the medical record data and the popular disease data of the specific patient, which are stored in the data storage center 3, wherein when the similarity is greater than a set threshold and the diagnosis method data corresponding to the suspected disease type with the largest matching degree in the data storage center 3 cannot complete a definite diagnosis for the specific patient, the data manager completes an inquiry of the specific patient based on the diagnosis method data corresponding to the popular disease data or completes treatment of the specific patient based on the treatment measure data corresponding to the popular disease data.

Preferably, the data manager 4 is further configured to screen the therapeutic measure data and/or the diagnostic method data according to the following steps: and acquiring at least one second patient of which the distance between the residence and the residence of the first patient is less than a set threshold value on the basis of the residence information of the first patient, and acquiring at least one third patient of which the coincidence degree with the medical record data of the first patient is greater than the set threshold value on the basis of the medical record data of the second patient. And under the condition that the disease type corresponding to the diagnosis data of the third patient is different from all the suspected disease types, and the confirmed diagnosis cannot be completed for the first patient based on the diagnosis method data corresponding to the suspected disease type with the maximum matching degree, at least the inquiry of the first patient is completed based on the diagnosis method data of the third patient.

Example 5

Preferably, the cloud server 5 can obtain the epidemic disease data of the current period. The epidemic disease data includes at least epidemic disease type information, its corresponding symptom information, and treatment information. The outbreak of different diseases often occurs under the influence of regions and seasons. For example, viral influenza is commonly found in spring and winter. Meanwhile, in a dense population region, influenza is more likely to outbreak. The cloud server 5 can be networked with the disease monitoring center, and then popular disease data of each region can be obtained from the disease monitoring center. Or, the cloud server 5 can perform access analysis on the data storage centers 3 of the hospitals, so as to acquire popular disease data. For example, the data storage center 3 stores therein diagnosis data of all patients in a hospital visit. If a large number of patients have the same disease within a set period of time, the disease can be defined as a current stage of epidemic disease. Meanwhile, the cloud server 5 may perform access analysis on the data storage centers 3 of all hospitals in the set area. For example, the city or the district may be set as a set area, and the diagnosis confirmation data of all patients in all hospitals in the set area may be analyzed and counted to determine whether or not there is a large number of outbreaks of epidemic diseases at the present time.

Preferably, the data manager 4 also obtains the treatment data and/or the confirmation method data as follows:

d1: acquiring popular disease data in a set area range with the residence of the patient as the center of a circle based on the residence information of the patient, wherein the popular disease data at least comprises popular disease type information, corresponding symptom information, diagnosis confirming information and treatment measure information.

D2: similarity of the medical record data and the epidemic disease data is determined based on comparison of the medical record data of the patient and the symptom information of the epidemic disease data.

D3: when the similarity is greater than the set threshold and the patient cannot be diagnosed based on the diagnosis method data corresponding to the suspected disease type with the largest matching degree, the data manager 4 can push the diagnosis method data corresponding to the popular disease data to the first terminal 1 and/or the second terminal, so that the patient can be asked based on the diagnosis information corresponding to the popular disease data, or the data manager 4 can push the treatment measure data corresponding to the popular disease data to the first terminal 1 and/or the second terminal, so that the patient can be treated based on the treatment measure data corresponding to the popular disease data. It will be appreciated that the pushed treatment data and diagnostic data is an intermediate data that is referenced by the healthcare worker or patient, which provides only a viable reference for the healthcare worker.

Example 6

Preferably, the data manager 4 performs data transmission with the cloud server 5 as follows:

f1: in a case where the data manager 4 performs a data transfer operation to transfer data to the cloud server 5, the data manager 4 generates a first permission request and a second permission request based on the data transfer operation, wherein the first permission request is transmitted to the first terminal 1, and the second permission request is transmitted to the second terminal 2.

Specifically, the data transmission operation refers to that the data manager 4 transmits data that can be shared to the cloud server 5 for storage and backup, so that the data can be shared among different data managers 4. The first permission request is for acquiring permission of the first terminal 1. The second permission request is for acquiring permission of the second terminal 2. That is, only data permitted by the first terminal 1 and the second terminal 2 can be transmitted to the cloud server 5.

F2: the first terminal 1 performs a first modification operation on the data which needs to be transmitted by the data manager 4 according to the first permission request, and the second terminal 2 performs a second modification operation on the data which needs to be transmitted by the data manager 4 according to the second permission request, so that private data in the data which needs to be transmitted by the data manager 4 can be partially deleted based on the first modification operation and the second modification operation.

Specifically, the first modification operation and the second modification operation both refer to partial deletion of private data in the data. The private data may be identity data of the patient, picture data related to a patient specific symptom, etc.

F3: the data manager 4 uploads the data to the cloud server 5, and the cloud server 5 only performs analysis processing on the data to determine whether the frequency and/or the number of the same or similar medicines continuously and repeatedly taken by the patient are greater than a set threshold value.

F4: the data manager 4 is configured to perform encryption processing on data in a manner of data encryption.

Specifically, the data encryption may employ a symmetric encryption algorithm such as DES, AES, or an asymmetric encryption algorithm such as RSA, DSA, ECC. By the method, the private data of the patient can be protected, and further the private data of the patient can be prevented from being leaked.

Example 7

Preferably, the data manager 4, based on its analysis module, is able to determine whether the frequency and/or number of consecutive repeated administrations of the same or similar medication by the patient is greater than a set threshold. In the case that the frequency and/or number of the same or similar medicines continuously and repeatedly taken by the patient is larger than the set threshold, the data manager 4 can feed back the medication reference data to at least one of the first terminal 1, the second terminal 2 and the cloud server 5. Medication reference data is recommended medication according to complex national standards or industry guidelines for a particular disease. For example, for epilepsy, there are often a range of recommended medications in the industry guidelines. Therefore, the data manager 4 can feed back the medicines recommended by the industry guide to at least one of the first terminal 1, the second terminal 2 and the cloud server 5. Preferably, the data manager 4 can further analyze which guideline medication the medication of the patient specifically conforms to according to the medication category of the patient at the current stage, and finally, the data manager 4 can push all guidelines the medication of the patient at the current stage conforms to at least one of the first terminal 1, the second terminal 2 and the cloud server 5.

It should be noted that the above-mentioned embodiments are exemplary, and that those skilled in the art, having benefit of the present disclosure, may devise various arrangements that are within the scope of the present disclosure and that fall within the scope of the invention. It should be understood by those skilled in the art that the present specification and figures are illustrative only and are not limiting upon the claims. The scope of the invention is defined by the claims and their equivalents.

Claims

1. Intelligent super-brain management system based on big data at least comprises: a first terminal (1) and a data manager (4) characterized in that,

the data manager (4) is configured to:

acquiring epidemic disease data in a set region range with the residence of a specific patient as a center of a circle based on the residence information of the patient;

the data manager (4) can compare the medical record data of the specific patient with the epidemic disease data to obtain the similarity between the epidemic disease data and the medical record data, wherein the data manager (4) can push the diagnosis method data corresponding to the epidemic disease data to the first terminal (1) or push the treatment measure data corresponding to the epidemic disease data to the first terminal (1) under the condition that the similarity is greater than a set threshold.

2. The intelligent superconcephalon management system according to claim 1, characterized in that the data manager (4) is further configured to:

acquiring at least one second patient of which the distance between the residence and the residence of the first patient is smaller than a set threshold value on the basis of the residence information of the first patient, and acquiring at least one third patient of which the coincidence degree with the medical record data of the first patient is larger than the set threshold value on the basis of the medical record data of the second patient;

under the condition that the disease type corresponding to the diagnosis data of the third patient is different from all suspected disease types, the data manager (4) can push the diagnosis method data of the third patient to the first terminal (1).

3. The intelligent superconcephalon management system according to claim 2, characterized in that the data manager (4) is further configured to:

under the condition that at least one suspected disease type is the same as the disease type corresponding to the diagnosis data of the third patient, the diagnosis method data of the third patient is pushed to the first terminal (1) so as to at least finish the inquiry of the first patient, or

And in the case that the disease type corresponding to the diagnosis data of the third patient is the same as the at least one suspected disease type, determining at least one drug type aiming at the disease type based on the treatment measure data of the third patient.

4. The intelligent superconcephalon management system according to claim 3, characterized in that the first terminal (1) is also operable to collect recovery data of the patient, the data manager (4) being able to evaluate the effectiveness of the treatment of the patient on the basis of the recovery data, wherein the data manager (4) is configured to:

establishing at least a first grade and a second grade;

acquiring a plurality of recovery data formed by a plurality of treatment processes of a patient aiming at the same disease based on the first terminal (1), and evaluating the plurality of recovery data to obtain a plurality of evaluation results;

and dividing the grades of a plurality of evaluation results, wherein under the condition that the grades of the evaluation results are gradually increased, a prompt is sent out through the first terminal (1) and/or the data manager (4).

5. The intelligent supercomputer management system according to claim 4, characterized in that it further comprises a data storage center (3), in case the data storage center (3) is configured to be able to store patient-specific medication data from a second terminal (2), the intelligent supercomputer management system being further configured to:

counting the medication data of a specific patient aiming at the same disease type, so as to obtain the frequency and/or the quantity of the same or similar medicines which are continuously and repeatedly taken by the specific patient in a set time period;

and sending out a reminder through the second terminal (2) and the first terminal (1) under the condition that the data manager (4) obtains that the frequency and/or the number of the same or similar medicines continuously and repeatedly taken by the patient are larger than a set threshold value based on the analysis module.

6. The intelligent supercomputer management system according to claim 5, characterized in that, the intelligent supercomputer management system is capable of configuring at least one cloud server (5) and a plurality of data managers (4) loaded to different hospitals, the cloud server (5) and the at least one data manager (4) are capable of performing data transmission operation according to the following manner:

in the case where the data manager (4) performs a data transfer operation to transfer data to the cloud server (5), the data manager (4) generates a first permission request and a second permission request based on the data transfer operation, wherein the first permission request is transmitted to the first terminal (1) and the second permission request is transmitted to the second terminal (2).

7. The intelligent superconcephalon management system according to claim 6, characterized in that each of said data managers (4) is capable of configuring at least one of said first terminals (1), at least one second terminal (2) and at least one data storage center (3), wherein:

the data manager (4) of the first hospital can transmit the access requirement to the data manager (4) of the second hospital through the cloud server (5), and the data manager (4) of the first hospital can access the data storage center (3) of the second hospital under the condition that the access permission of the data manager (4) of the second hospital is obtained.

8. The intelligent superconcephalon management system according to claim 7, characterized in that the first terminal (1) is able to establish a communication connection with a designated second terminal (2) in such a way that an access request is initiated to the designated second terminal (2), or in the case where the first terminal (1) sends an access request to the data manager (4), the data manager (4) is able to send the access request to the second terminal (2) in an idle state, so that the first terminal (1) is able to establish a communication connection with the second terminal (2).

9. An intelligent medical system, comprising:

a data manager (4) capable of comparing medical record data with symptom data to determine at least one suspected disease type, the data manager (4) configured to:

the data manager (4) determines the similarity between the medical record data and the popular disease data based on the comparison between the medical record data and the popular disease data of the specific patient, which are stored in the data storage center (3), wherein when the similarity is greater than a set threshold, the data manager (4) can push the diagnosis method data corresponding to the popular disease data to the first terminal (1), or the data manager (4) can push the treatment measure data corresponding to the popular disease data to the first terminal (1).

10. The intelligent medical system according to claim 9, wherein the data manager (4) is further configured to:

acquiring at least one second patient of which the distance between the residence and the residence of the first patient is smaller than a set threshold value based on the residence information of the first patient, and acquiring at least one third patient of which the coincidence degree with the medical record data of the first patient is larger than the set threshold value based on the medical record data of the second patient;

under the condition that the disease type corresponding to the diagnosis data of the third patient is different from all suspected disease types, the data manager (4) can push the diagnosis method data of the third patient, which are stored in the data storage center (3), to the first terminal (1).