CN118053598B - Medical information sharing method and system based on medical big data - Google Patents
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Abstract
The invention discloses a medical information sharing method and a system based on medical big data, which belong to the field of medical care informatics.
Description
Technical Field
The invention belongs to the field of medical health care informatics, and particularly relates to a medical information sharing method and system based on medical big data.
Background
Medical information sharing based on medical big data is a process for improving the quality and efficiency of medical care service by utilizing a big data set, and relates to data such as collecting, storing, analyzing and sharing patient information, medical records, diagnosis and treatment schemes, medicine information and the like, the medical information sharing has important significance for optimizing diagnosis and treatment processes, improving health management level and promoting medical research, the medical big data enables the patient information to be easier to acquire, doctors can quickly access medical records of the patient, accordingly more accurate diagnosis and treatment decisions are made, the medical information sharing allows information sharing among different medical institutions, and means that when the patient can be transferred between different hospitals and clinics, the doctors can know historical health conditions and treatment experiences of the patient.
In the prior art, in the process of sorting and screening the historical patient treatment data, as the historical patient treatment data of the patient are more, the historical patient treatment data related to the current patient treatment cannot be rapidly screened and extracted, so that the utilization efficiency of the historical patient treatment data is lower, and meanwhile, the burden of medical staff on screening the historical patient treatment data is increased, and the problems exist in the prior art;
For example, in chinese patent with publication number CN114038542B, a medical information sharing method and system based on medical big data are disclosed, wherein the method includes obtaining a terminal list, and the terminal list includes a plurality of medical terminals for broadcasting information to be transmitted; the method comprises the steps that broadcast information is sent to each medical terminal, the medical terminals are arranged in different local area networks, medical information based on users is stored in the medical terminals, and the broadcast information comprises identity information of the users and current medical department information; determining the matching degree of medical information in the medical terminal according to the current medical department information and the medical terminal; and receiving the return information of each medical terminal, screening the return information according to the preset standard matching degree from the return information, and fusing the information returned by the medical terminal where the medical information with the matching degree more than or equal to the standard matching degree is located. The information transmission of each medical terminal is realized, the situation of medical information island is effectively avoided, and the utilization efficiency of medical data is improved;
Meanwhile, the application publication number CN115312149A relates to a medical information sharing method and system based on medical big data; and (3) performing processing such as acquisition, processing, cleaning, processing analysis and the like on related medical data, constructing an SOA-based data sharing and information exchange platform, integrating all functional modules of the platform in a form of components, and providing access and adaptation for various services of the platform. According to the invention, the data sharing and information exchange platform based on the SOA is constructed, the information data of all medical institutions are effectively integrated to form a unified medical information sharing and management platform, the service capacity and efficiency of the medical service platform can be effectively improved, a more perfect medical service system is provided, information sharing is realized among medical institutions, doctors and patients, the workload of all parties is reduced, authorized sharing of diagnosis and treatment information of patients and electronic medical record sharing are realized, and data interoperability taking personal information as a center is realized;
The problems proposed in the background art exist in the above patents: in the prior art, in the process of sorting and screening the historical patient treatment data, as the historical patient treatment data of the patient are more, the historical patient treatment data related to the current patient treatment cannot be rapidly screened and extracted, so that the utilization efficiency of the historical patient treatment data is lower, and meanwhile, the burden of medical staff on screening the historical patient treatment data is increased.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a medical information sharing method and a medical information sharing system based on medical big data.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a medical information sharing method based on medical big data comprises the following specific steps:
Acquiring patient historical visit data and drug use data, and storing the patient historical visit data and the drug use data in a medical cloud platform, wherein medical staff invokes the patient historical visit data from the medical cloud platform;
Acquiring patient condition data in the current patient treatment process, and importing the patient condition data and the historical patient treatment data in the current patient treatment process into a first position correlation value calculation strategy to calculate a first position correlation value;
leading the patient disease data and the historical patient disease data in the current treatment process into a second position correlation value calculation strategy to calculate a second position correlation value;
Importing the calculated first position correlation value and the second position correlation value into a calculation formula of the influence probability of the historical diagnosis data to calculate the influence probability of the historical diagnosis data;
and outputting the historical visit data with the influence probability larger than or equal to the set influence probability threshold in a descending order as the reference data of the patient at the present visit, and taking the historical visit data with the influence probability smaller than the set influence probability threshold as the reference data of the patient at the present visit.
The method for acquiring the patient history visit data and the medicine use data is characterized by comprising the following specific steps that:
S11, after each treatment of a medical unit is finished, the treatment data of the patient are uploaded to a medical cloud platform, and the treatment data of the same patient are stored in the same storage space according to time sequence;
S12, in the patient treatment process, medical staff acquires all historical treatment data of a corresponding patient to be treated through the information of the patient to be treated, wherein the treatment data comprise position data of the disease of the patient, disease occurrence time data and disease type data, and the position data of the disease of the patient are position data of wounded parts corresponding to the disease of the patient, such as the instep and the ankle; the disease type data is classified according to medical type, and generally includes information such as classification, name, symptom, cause, and treatment method of various diseases, for example:
Infectious diseases include: influenza, coronavirus (COVID-19), HIV/AIDS, tuberculosis, malaria, etc.;
genetic diseases include: cystic fibrosis, thalassemia, dunaliella muscular dystrophy, huntington's disease, etc.;
metabolic diseases include: obesity, hypercholesterolemia, hyperglycemia, hyperuricemia, etc.;
Digestive system diseases include: gastritis, gastric ulcer, liver cirrhosis, irritable bowel syndrome, etc.;
these classifications and examples are only a portion of the disease categories.
It should be specifically noted that the first location association value calculation policy includes the following specific contents:
S21, acquiring patient condition data described in the current patient treatment process, and simultaneously acquiring patient history treatment data from a medical cloud platform;
S22, the position data of the patient disease in the patient disease data described in the patient current visit process is obtained, meanwhile, the position data of the patient disease in the patient history visit data is obtained, the obtained position data of the patient disease in the patient disease data described in the patient current visit process and the obtained position data of the patient disease in the patient jth history visit data are imported into a first position correlation value calculation formula to calculate a first position correlation value, wherein the first position correlation value calculation formula is as follows: Wherein h is the height of the human body, m () is the number of elements in brackets, C is the position data set of the patient disease in the patient disease data described in the current patient treatment process,/> For a set of positional data of patient disease in a jth historical visit data of the patient, n is the number of positional data of patient disease in the jth historical visit data,/>Is the distance between the ith location of the patient disease in the jth historical visit data and the location of the patient disease in the patient condition data described in the closest patient present visit, wherein/>For the first duty cycle,/>Is a second duty cycle, wherein/(；
S23, acquiring all the calculated historical diagnosis data and a first position correlation value in the diagnosis process, and storing the obtained historical diagnosis data and the first position correlation value in a storage module;
It should be specifically noted that the second location correlation value calculation strategy includes the following specific steps:
S31, acquiring patient disease type data in the current patient treatment process and patient disease type data in the historical patient treatment data, and importing the patient disease type data in the j-th patient treatment data of the patient history and the patient disease type data in the current patient treatment process into a second position correlation value calculation formula to calculate a second position correlation value, wherein the second position correlation value calculation formula is as follows: K is the patient disease type data set in the present treatment process,/> For the patient disease category data set in the patient history jth visit data,/>For the intersection,/>Is a union;
S32, acquiring all the calculated historical diagnosis data and a second position correlation value in the diagnosis process, and storing the obtained historical diagnosis data and the second position correlation value in a storage module;
The method for calculating the influence probability of the historical diagnosis data according to the influence probability calculation formula for importing the calculated first position correlation value and the calculated second position correlation value into the historical diagnosis data comprises the following specific steps:
S41, acquiring all the calculated historical diagnosis data and a first position correlation value and a second position correlation value of the diagnosis process;
s42, substituting the obtained first position association value and second position association value into a first-stage association value calculation formula to calculate a first-stage association value, wherein the first-stage association value calculation formula is as follows: wherein/> For the first-level correlation value of the j-th patient visit data and the patient visit data in the current patient visit process, T is the time period from the j-th patient visit to the current patient visit, exp () is the power of e, and is/>For the set standard duration,/>For the first position-related value duty cycle factor,/>Is a second position-related value duty cycle, wherein/>；
S43, importing the calculated first-level correlation value of the historical treatment data of the patient and the treatment data of the patient in the current treatment process into a calculation formula of the influence probability of the historical treatment data to calculate the influence probability of the historical treatment data, wherein the calculation formula of the influence probability of the j-th historical treatment data is as follows: Wherein Z is the number of patient history visit data,/> A first order correlation value for the patient's historical z-th visit data and the visit data during the patient's present visit.
Here, the influence probability threshold, the first duty ratio coefficient, the second duty ratio coefficient, the first position association value duty ratio coefficient, the second position association value duty ratio coefficient and the set standard time length take the following values: acquiring 5000 groups of patient condition data and patient historical visit data, adopting 500 medical professionals to select visit data related to the patient condition data from the historical visit data, importing the patient condition data and the patient historical visit data into an influence probability calculation formula of the historical visit data to perform influence probability calculation, importing the calculated influence probability and related classification results into fitting software, and outputting influence probability threshold values, first duty ratio coefficients, first position association value duty ratio coefficients of second duty ratio coefficients, second position association value duty ratio coefficients and set standard duration values which accord with the highest judgment accuracy.
The medical information sharing system based on the medical big data is realized based on the medical information sharing method based on the medical big data, and specifically comprises a data acquisition module, a first position correlation value calculation module, a second position correlation value calculation module, an influence probability calculation module, a reference data output module and a control module, wherein the data acquisition module is used for acquiring historical patient treatment data and medicine use data, the data are stored in a medical cloud platform, medical staff call the historical patient treatment data from the medical cloud platform, the first position correlation value calculation module is used for acquiring patient condition data of a patient in the current treatment process, and the patient condition data and the historical patient treatment data in the current treatment process are imported into a first position correlation value calculation strategy to calculate a first position correlation value.
The method specifically needs to be described, the second position association value calculation module is used for guiding patient condition data and historical patient condition data in the current treatment process into a second position association value calculation strategy to calculate a second position association value, the influence probability calculation module is used for guiding the calculated first position association value and the calculated second position association value into an influence probability calculation formula of the historical treatment data to calculate the influence probability of the historical treatment data, the reference data output module is used for outputting the historical treatment data with the influence probability being greater than or equal to a set influence probability threshold in descending order as reference data of the current treatment of the patient, and the historical treatment data with the influence probability being smaller than the set influence probability threshold is not used as reference data of the current treatment of the patient.
The control module is used for controlling the operation of the data acquisition module, the first position correlation value calculation module, the second position correlation value calculation module, the influence probability calculation module and the reference data output module.
An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes the medical information sharing method based on the medical big data by calling the computer program stored in the memory.
A computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a medical information sharing method based on medical big data as described above.
Compared with the prior art, the invention has the beneficial effects that:
According to the medical treatment system, historical patient treatment data and drug use data are acquired and stored in a medical cloud platform, medical staff calls the historical patient treatment data of a patient from the medical cloud platform, the patient condition data and the historical patient treatment data in the current patient treatment process are acquired and are imported into a first position correlation value calculation strategy to calculate a first position correlation value, the patient condition data and the historical patient condition data in the current patient treatment process are imported into a second position correlation value calculation strategy to calculate a second position correlation value, the influence probability of the historical patient treatment data is calculated according to an influence probability calculation formula of the first position correlation value and the second position correlation value which are obtained through calculation, the historical patient treatment data with the influence probability being greater than or equal to a set influence probability threshold is output as reference data of the current patient treatment, the historical patient treatment data with the influence probability being smaller than the set influence probability threshold is not used as reference data of the current patient treatment, the historical patient treatment data is sorted, the screening accuracy of the historical patient treatment data is improved, meanwhile, the screening efficiency of the historical patient treatment data is improved, and the medical staff is improved.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the overall flow of a medical information sharing method based on medical big data;
FIG. 2 is a schematic diagram of a first position correlation value calculation strategy in a medical information sharing method based on medical big data according to the present invention;
Fig. 3 is a schematic diagram of an overall framework of a medical information sharing system based on medical big data according to the present invention.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application, but the present application may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present application is not limited to the specific embodiments disclosed below.
Example 1
Referring to fig. 1-2, an embodiment of the present invention is provided: the technical problems solved by the embodiment are as follows: in the prior art, in the process of sorting and screening the historical patient treatment data, as the historical patient treatment data of the patient are more, the historical patient treatment data related to the current patient treatment cannot be rapidly screened and extracted, so that the utilization efficiency of the historical patient treatment data is lower, and meanwhile, the burden of medical staff for screening the historical patient treatment data is increased;
a medical information sharing method based on medical big data comprises the following specific steps:
Acquiring patient historical visit data and drug use data, and storing the patient historical visit data and the drug use data in a medical cloud platform, wherein medical staff invokes the patient historical visit data from the medical cloud platform;
Acquiring patient condition data in the current patient treatment process, and importing the patient condition data and the historical patient treatment data in the current patient treatment process into a first position correlation value calculation strategy to calculate a first position correlation value;
leading the patient disease data and the historical patient disease data in the current treatment process into a second position correlation value calculation strategy to calculate a second position correlation value;
Importing the calculated first position correlation value and the second position correlation value into a calculation formula of the influence probability of the historical diagnosis data to calculate the influence probability of the historical diagnosis data;
and outputting the historical visit data with the influence probability larger than or equal to the set influence probability threshold in a descending order as the reference data of the patient at the present visit, and taking the historical visit data with the influence probability smaller than the set influence probability threshold as the reference data of the patient at the present visit.
In this embodiment, it should be noted that, acquiring patient history visit data and drug usage data to store in the medical cloud platform, and the medical staff invoking the patient history visit data from the medical cloud platform includes the following specific steps:
S11, after each treatment of a medical unit is finished, the treatment data of the patient are uploaded to a medical cloud platform, and the treatment data of the same patient are stored in the same storage space according to time sequence;
this step is implemented here by example code:
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
struct patient {
char name[50];
int age;
char diagnosis[100];
char treatment[100];
char date[20];
};
void uploadData(struct patient *p, int numPatients) {
FILE *file = fopen("patient_data.txt", "a");
if (file == NULL) {
printf("Error opening file.\n");
return;
}
for (int i = 0; i < numPatients; i++) {
fprintf(file, "Name: %s\nAge: %d\nDiagnosis: %s\nTreatment: %s\nDate: %s\n\n", p[i].name, p[i].age, p[i].diagnosis, p[i].treatment, p[i].date);
}
fclose(file);
}int main() {
int numPatients;
printf("Enter the number of patients: ");
scanf("%d", &numPatients);
struct patient *patients = (struct patient *)malloc(numPatients * sizeof(struct patient));
for (int i = 0; i < numPatients; i++) {
printf("\nPatient %d\n", i+1);
printf("Enter name: ");
scanf("%s", patients[i].name);
printf("Enter age: ");
scanf("%d", &patients[i].age);
printf("Enter diagnosis: ");
scanf("%s", patients[i].diagnosis);
printf("Enter treatment: ");
scanf("%s", patients[i].treatment);
printf("Enter date of visit: ");
scanf("%s", patients[i].date);
}
uploadData(patients, numPatients);
free(patients);
return 0;
}
S12, in the patient treatment process, medical staff acquires all historical treatment data of a corresponding patient to be treated through the information of the patient to be treated, wherein the treatment data comprise position data of the disease of the patient, disease occurrence time data and disease type data, and the position data of the disease of the patient are position data of wounded parts corresponding to the disease of the patient, such as the instep and the ankle; the disease type data is classified according to medical type, and generally includes information such as classification, name, symptom, cause, and treatment method of various diseases, for example:
Infectious diseases include: influenza, coronavirus (COVID-19), HIV/AIDS, tuberculosis, malaria, etc.;
genetic diseases include: cystic fibrosis, thalassemia, dunaliella muscular dystrophy, huntington's disease, etc.;
metabolic diseases include: obesity, hypercholesterolemia, hyperglycemia, hyperuricemia, etc.;
Digestive system diseases include: gastritis, gastric ulcer, liver cirrhosis, irritable bowel syndrome, etc.;
These classifications and examples are only a portion of the disease categories;
in this embodiment, it should be noted that the first location association value calculation policy includes the following specific contents:
S21, acquiring patient condition data described in the current patient treatment process, and simultaneously acquiring patient history treatment data from a medical cloud platform;
S22, the position data of the patient disease in the patient disease data described in the patient current visit process is obtained, meanwhile, the position data of the patient disease in the patient history visit data is obtained, the obtained position data of the patient disease in the patient disease data described in the patient current visit process and the obtained position data of the patient disease in the patient jth history visit data are imported into a first position correlation value calculation formula to calculate a first position correlation value, wherein the first position correlation value calculation formula is as follows: Wherein h is the height of the human body, m () is the number of elements in brackets, C is the position data set of the patient disease in the patient disease data described in the current patient treatment process,/> For a set of positional data of patient disease in a jth historical visit data of the patient, n is the number of positional data of patient disease in the jth historical visit data,/>Is the distance between the ith location of the patient disease in the jth historical visit data and the location of the patient disease in the patient condition data described in the closest patient present visit, wherein/>For the first duty cycle,/>Is a second duty cycle, wherein/(；
S23, acquiring all the calculated historical diagnosis data and a first position correlation value in the diagnosis process, and storing the obtained historical diagnosis data and the first position correlation value in a storage module;
in this embodiment, it should be noted that the second location association value calculation policy includes the following specific steps:
S31, acquiring patient disease type data in the current patient treatment process and patient disease type data in the historical patient treatment data, and importing the patient disease type data in the j-th patient treatment data of the patient history and the patient disease type data in the current patient treatment process into a second position correlation value calculation formula to calculate a second position correlation value, wherein the second position correlation value calculation formula is as follows: K is the patient disease type data set in the present treatment process,/> For the patient disease category data set in the patient history jth visit data,/>For the intersection,/>Is a union;
S32, acquiring all the calculated historical diagnosis data and a second position correlation value in the diagnosis process, and storing the obtained historical diagnosis data and the second position correlation value in a storage module;
In this embodiment, it should be noted that, the method for calculating the influence probability of the historical diagnosis data by importing the calculated first position correlation value and the calculated second position correlation value into the influence probability calculation formula of the historical diagnosis data includes the following specific steps:
S41, acquiring all the calculated historical diagnosis data and a first position correlation value and a second position correlation value of the diagnosis process;
s42, substituting the obtained first position association value and second position association value into a first-stage association value calculation formula to calculate a first-stage association value, wherein the first-stage association value calculation formula is as follows: wherein/> For the first-level correlation value of the j-th patient visit data and the patient visit data in the current patient visit process, T is the time period from the j-th patient visit to the current patient visit, exp () is the power of e, and is/>For the set standard duration,/>For the first position-related value duty cycle factor,/>Is a second position-related value duty cycle, wherein/>；
S43, importing the calculated first-level correlation value of the historical treatment data of the patient and the treatment data of the patient in the current treatment process into a calculation formula of the influence probability of the historical treatment data to calculate the influence probability of the historical treatment data, wherein the calculation formula of the influence probability of the j-th historical treatment data is as follows: Wherein Z is the number of patient history visit data,/> A first-level correlation value of the patient history z-th visit data and the visit data in the patient present visit process;
In this embodiment, it should be noted that, the values of the influence probability threshold, the first duty ratio coefficient, the second duty ratio coefficient, the first position association value duty ratio coefficient, the second position association value duty ratio coefficient and the set standard duration are as follows: acquiring 5000 groups of patient condition data and patient historical visit data, adopting 500 medical professionals to select visit data related to the patient condition data from the historical visit data, importing the patient condition data and the patient historical visit data into an influence probability calculation formula of the historical visit data to perform influence probability calculation, importing the calculated influence probability and related classification results into fitting software, and outputting influence probability threshold values, first duty ratio coefficients, first position association value duty ratio coefficients of second duty ratio coefficients, second position association value duty ratio coefficients and set standard time values which accord with the highest judgment accuracy;
It should be noted that the advantages of this embodiment compared with the prior art are: the medical staff calls the patient history treatment data from the medical cloud platform, acquires the patient condition data in the patient treatment process, guides the patient condition data and the history treatment data in the patient treatment process into a first position correlation value calculation strategy to calculate a first position correlation value, guides the patient condition data and the history patient condition data in the patient treatment process into a second position correlation value calculation strategy to calculate a second position correlation value, guides the calculated first position correlation value and second position correlation value into an influence probability calculation formula of the history treatment data to calculate the influence probability of the history treatment data, outputs the history treatment data descending order with the influence probability larger than or equal to a set influence probability threshold as reference data of the patient treatment, and uses the history treatment data with the influence probability smaller than the set influence probability threshold not as reference data of the patient treatment, so that the screening accuracy of the history treatment data is improved, and the utilization efficiency of the history treatment data is improved.
Example 2
As shown in fig. 3, a medical information sharing system based on big medical data is realized based on the medical information sharing method based on big medical data, which specifically includes a data acquisition module, a first position correlation value calculation module, a second position correlation value calculation module, an influence probability calculation module, a reference data output module and a control module, wherein the data acquisition module is used for acquiring historical patient treatment data and medicine use data and storing the historical patient treatment data in a medical cloud platform, a medical staff invokes the historical patient treatment data from the medical cloud platform, the first position correlation value calculation module is used for acquiring patient condition data of a patient in the current treatment process of the patient, and the patient condition data and the historical patient treatment data in the current treatment process are imported into a first position correlation value calculation strategy to calculate a first position correlation value; the second position association value calculation module is used for leading the patient condition data and the historical patient condition data in the current treatment process into a second position association value calculation strategy to calculate a second position association value, the influence probability calculation module is used for leading the calculated first position association value and the calculated second position association value into an influence probability calculation formula of the historical treatment data to calculate the influence probability of the historical treatment data, the reference data output module is used for outputting the historical treatment data with the influence probability being greater than or equal to a set influence probability threshold in a descending order as the reference data of the current treatment of the patient, and the historical treatment data with the influence probability being smaller than the set influence probability threshold is not used as the reference data of the current treatment of the patient; the control module is used for controlling the operation of the data acquisition module, the first position correlation value calculation module, the second position correlation value calculation module, the influence probability calculation module and the reference data output module.
Example 3
The present embodiment provides an electronic device including: a processor and a memory, wherein the memory stores a computer program for the processor to call;
The processor executes the medical information sharing method based on the medical big data by calling the computer program stored in the memory.
The electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, where at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to implement a medical information sharing method based on medical big data provided by the above method embodiments. The electronic device can also include other components for implementing the functions of the device, for example, the electronic device can also have wired or wireless network interfaces, input-output interfaces, and the like, for inputting and outputting data. The present embodiment is not described herein.
Example 4
The present embodiment proposes a computer-readable storage medium having stored thereon an erasable computer program;
the computer program, when executed on the computer device, causes the computer device to execute a medical information sharing method based on medical big data as described above.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, and the like.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of readable storage medium known in the art.
The principles and embodiments of the present application have been described in detail with reference to specific examples, which are provided to facilitate understanding of the method and core ideas of the present application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.
Claims (7)
1. The medical information sharing method based on the medical big data is characterized by comprising the following specific steps of:
Acquiring patient historical visit data and drug use data, and storing the patient historical visit data and the drug use data in a medical cloud platform, wherein medical staff invokes the patient historical visit data from the medical cloud platform;
Acquiring patient condition data in the current patient treatment process, and importing the patient condition data and the historical patient treatment data in the current patient treatment process into a first position correlation value calculation strategy to calculate a first position correlation value;
The first position association value calculation strategy comprises the following specific contents:
S21, acquiring patient condition data described in the current patient treatment process, and simultaneously acquiring patient history treatment data from a medical cloud platform;
S22, the position data of the patient disease in the patient disease data described in the patient current visit process is obtained, meanwhile, the position data of the patient disease in the patient history visit data is obtained, the obtained position data of the patient disease in the patient disease data described in the patient current visit process and the obtained position data of the patient disease in the patient jth history visit data are imported into a first position correlation value calculation formula to calculate a first position correlation value, wherein the first position correlation value calculation formula is as follows: Wherein h is the height of the human body, m () is the number of elements in brackets, C is the position data set of the patient disease in the patient disease data described in the current patient treatment process,/> For a set of positional data of patient disease in a jth historical visit data of the patient, n is the number of positional data of patient disease in the jth historical visit data,/>Is the distance between the ith location of the patient disease in the jth historical visit data and the location of the patient disease in the patient condition data described in the closest patient present visit, wherein/>For the first duty cycle,/>Is a second duty cycle, wherein/(；
S23, acquiring all the calculated historical diagnosis data and a first position correlation value in the diagnosis process, and storing the obtained historical diagnosis data and the first position correlation value in a storage module;
leading the patient disease data and the historical patient disease data in the current treatment process into a second position correlation value calculation strategy to calculate a second position correlation value;
the second position correlation value calculation strategy comprises the following specific steps:
S31, acquiring patient disease type data in the current patient treatment process and patient disease type data in the historical patient treatment data, and importing the patient disease type data in the j-th patient treatment data of the patient history and the patient disease type data in the current patient treatment process into a second position correlation value calculation formula to calculate a second position correlation value, wherein the second position correlation value calculation formula is as follows: K is the patient disease type data set in the present treatment process,/> For the patient disease category data set in the patient history jth visit data,/>For the intersection,/>Is a union;
S32, acquiring all the calculated historical diagnosis data and a second position correlation value in the diagnosis process, and storing the obtained historical diagnosis data and the second position correlation value in a storage module;
Importing the calculated first position correlation value and the second position correlation value into a calculation formula of the influence probability of the historical diagnosis data to calculate the influence probability of the historical diagnosis data;
The method for calculating the influence probability of the historical visit data by using the calculated influence probability calculation formula for importing the first position correlation value and the second position correlation value into the historical visit data comprises the following specific steps:
S41, acquiring all the calculated historical diagnosis data and a first position correlation value and a second position correlation value of the diagnosis process;
s42, substituting the obtained first position association value and second position association value into a first-stage association value calculation formula to calculate a first-stage association value, wherein the first-stage association value calculation formula is as follows: wherein/> For the first-level correlation value of the j-th patient visit data and the patient visit data in the current patient visit process, T is the time period from the j-th patient visit to the current patient visit, exp () is the power of e, and is/>For the set standard duration,/>For the first position-related value duty cycle factor,/>Is a second position-related value duty cycle, wherein/>；
S43, importing the calculated first-level correlation value of the historical treatment data of the patient and the treatment data of the patient in the current treatment process into a calculation formula of the influence probability of the historical treatment data to calculate the influence probability of the historical treatment data, wherein the calculation formula of the influence probability of the j-th historical treatment data is as follows: Wherein Z is the number of historical patient visit data, A first-level correlation value of the patient history z-th visit data and the visit data in the patient present visit process;
and outputting the historical visit data with the influence probability larger than or equal to the set influence probability threshold in a descending order as the reference data of the patient at the present visit, and taking the historical visit data with the influence probability smaller than the set influence probability threshold as the reference data of the patient at the present visit.
2. The medical information sharing method based on medical big data according to claim 1, wherein the acquired patient history visit data and the drug usage data are stored in a medical cloud platform, and the medical staff recalls the patient history visit data from the medical cloud platform, comprising the following specific steps:
S11, after each treatment of a medical unit is finished, the treatment data of the patient are uploaded to a medical cloud platform, and the treatment data of the same patient are stored in the same storage space according to time sequence;
And S12, in the patient treatment process, medical staff acquire all historical treatment data of the corresponding treatment patient through the treatment patient information.
3. The medical information sharing system based on the medical big data is realized based on the medical information sharing method based on the medical big data according to any one of claims 1-2, and is characterized by comprising a data acquisition module, a first position association value calculation module, a second position association value calculation module, an influence probability calculation module, a reference data output module and a control module, wherein the data acquisition module is used for acquiring historical patient treatment data and medicine use data and storing the historical patient treatment data in a medical cloud platform, a medical staff invokes the historical patient treatment data from the medical cloud platform, the first position association value calculation module is used for acquiring patient condition data in the patient treatment process, and the patient condition data and the historical patient treatment data in the patient treatment process are led into a first position association value calculation strategy to calculate a first position association value.
4. The medical information sharing system based on big medical data according to claim 3, wherein the second position related value calculating module is configured to import patient condition data and historical patient condition data in the current treatment process into a second position related value calculating strategy to calculate a second position related value, the influence probability calculating module is configured to import the calculated first position related value and second position related value into an influence probability calculating formula of the historical treatment data to calculate an influence probability of the historical treatment data, and the reference data output module is configured to output the historical treatment data with the influence probability greater than or equal to a set influence probability threshold in descending order as reference data of the current treatment of the patient, and the historical treatment data with the influence probability smaller than the set influence probability threshold is not used as reference data of the current treatment of the patient.
5. The medical information sharing system based on medical big data according to claim 4, wherein the control module is configured to control operations of the data acquisition module, the first position correlation value calculation module, the second position correlation value calculation module, the influence probability calculation module, and the reference data output module.
6. An electronic device, comprising: a processor and a memory, wherein the memory stores a computer program for the processor to call;
the processor executes a medical information sharing method based on medical big data according to any one of claims 1-2 by calling a computer program stored in the memory.
7. A computer readable storage medium storing instructions which, when run on a computer, cause the computer to perform a medical information sharing method based on medical big data as claimed in any of claims 1-2.
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