CN112489743A - Medical data view implementation method and system - Google Patents
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Abstract
The invention discloses a medical data view realization method and a medical data view realization system, wherein a specific implementation mode of the method comprises the steps of establishing a patient health main index model, storing index data generated by the patient health main index model into a block chain according to a preset main index uploading contract, and further acquiring corresponding medical data based on the patient health model according to the index data; identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node; and generating a patient health data view based on a preset time axis data structure according to the time axis nodes and outputting the view. Therefore, the method and the system can solve the problems of high cost and high difficulty in sharing medical data among multiple mechanisms in the prior art.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a medical data view implementation method and system.
Background
At present, the view of the patient 360 is generally implemented by a medical institution, taking a hospital as an example, the patients visit the hospital in series through the view of the patient 360, the patient visits the hospital are associated through a main index of a time axis, and detailed information of each visit and a corresponding system, such as LIS, PAC and the like, provide panoramic and multidimensional patient information for a doctor, and provide reference for subsequent treatment. The view of a patient 360 of the general system is realized by adopting a centralized system and a database, the diagnosis history and diagnosis and treatment results of the patient are summarized by taking a time axis as a dimension, and the change trend of the physical condition of the patient can be reflected.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
currently, traditional medical data construction of 360 views of a patient is based on a single medical facility due to issues of databases, data structures, structural norms of different medical facilities. Therefore, there are many challenges for sharing medical information among multiple institutions, such as inconsistency of data structures of HIS systems among different hospitals, high early development cost, high policy risk, and high operation cost.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and a system for implementing a medical data view, which can solve the problems of high cost and difficulty in sharing medical data among multiple institutions in the prior art.
In order to achieve the above object, according to an aspect of the embodiments of the present invention, a medical data view implementation method is provided, including establishing a patient health main index model, storing index data generated by the patient health main index model into a block chain according to a preset main index upload contract, and further acquiring corresponding medical data based on the patient health model according to the index data; identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node; and generating a patient health data view based on a preset time axis data structure according to the time axis nodes and outputting the view.
Optionally, the index data generated by the patient health main index model is followed by:
acquiring preset patient identity information, and calling a dynamic update model to generate a unique identifier of the patient; and according to the unique identifier and the preset password of the patient, obtaining an encrypted first numerical value through a homomorphic encryption pailler algorithm, further processing the first numerical value by utilizing a sha256hash160 algorithm to obtain a second numerical value, and using the second numerical value as an address value for searching index data stored in the block chain.
Optionally, performing linear fitting on the medical data based on a time dimension to obtain a time axis node, including:
and performing linear fitting on the medical data through a cubic spline interpolation fitting algorithm to obtain time axis nodes, generating a patient health time linear model, and predicting health data through the patient health time linear model.
Optionally, the timeline data structure includes:
calculating to obtain a corresponding second numerical value as an index address through index data generated by the patient health main index model; each index address sequentially stores medical data according to time dimension, and the medical data of each time point is stored into one data instance; and respectively generating a unique address for the medical data of each attribute, and correspondingly storing the unique address into a data instance.
Optionally, the method further comprises:
receiving a view updating instruction, updating an intelligent contract according to a preset view, and determining an updating authority based on an identity in the view updating instruction so as to acquire corresponding medical data from a block chain and execute updating; and encrypting the updated medical data through the platform public key, and further storing the encrypted medical data into the corresponding block chain address.
Optionally, the method further comprises:
and starting a monitoring program, acquiring a circulation log and a circulation request of the medical data of the patient, and recording the circulation log and the circulation request into a preset distributed account book.
Optionally, the method further comprises:
displaying a time axis through a front-end page, calling a query interface, and further acquiring a corresponding patient health data view on a block chain; if a detailed page information instruction for presenting the health data of the patient is received, the ip address of the corresponding front-end processor is obtained, the external medical system is accessed, and the detailed page information is called.
In addition, the invention also provides a medical data view implementation system which comprises an acquisition module, a block chain and a display module, wherein the acquisition module is used for establishing a patient health main index model, storing index data generated by the patient health main index model into the block chain according to a preset main index upload contract, and further acquiring corresponding medical data based on the patient health model according to the index data; the processing module is used for identifying a target dimension in the medical data based on the patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node; and generating a patient health data view based on a preset time axis data structure according to the time axis nodes and outputting the view.
One embodiment of the above invention has the following advantages or benefits: the technical means of establishing the patient view time axis by using the block chain technology realizes the trusted and tamper-proof data processing and sharing; carrying out automatic processing and generation by using an intelligent contract, and controlling the access right of the view through the authority; block chain distributed ID generation based on cryptography, namely, a homomorphic encryption-based algorithm is adopted, and a user editable password is introduced into a generation process of a unique ID value, so that the privacy of user data and the system safety are ensured; real-time supervision and real-time intervention are carried out, and a reliable supervision function based on a block chain is provided for supervision departments; medical data sharing among medical institutions reduces repeated inspection and reduces data risks; the distributed storage avoids single point of failure, and the localization node improves the view efficiency of the patient; and (4) summarizing and analyzing medical data of the latitude of the time axis of the patient, predicting risks and protecting the health of the patient.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic diagram of a main flow of a medical data view implementation method according to a first embodiment of the present invention;
FIG. 2 is a schematic diagram of a timeline data structure according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of a timeline presentation according to an embodiment of the present invention;
fig. 4 is a schematic diagram of a main flow of a medical data view implementation method according to a second embodiment of the invention;
fig. 5 is a schematic diagram of a main flow of a medical data view implementation method according to a third embodiment of the invention;
FIG. 6 is a schematic diagram of the major modules of a medical data view implementation system according to an embodiment of the present invention;
FIG. 7 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 8 is a schematic structural diagram of a computer system suitable for implementing a terminal device or a server according to an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of a medical data view implementation method according to a first embodiment of the present invention, the medical data view implementation method including:
step S101, establishing a patient health main index model, storing index data generated by the patient health main index model into a block chain according to a preset main index uploading contract, and further acquiring corresponding medical data based on the patient health model according to the index data.
In some embodiments, after the index data generated by the patient health main index model, preset patient identity information may be acquired, and a dynamically updated model is invoked to generate a unique identifier of the patient. And according to the unique identifier and the preset password of the patient, obtaining an encrypted first numerical value through a homomorphic encryption pailler algorithm, further processing the first numerical value by utilizing a sha256hash160 algorithm to obtain a second numerical value, and using the second numerical value as an address value for searching index data stored in the block chain.
The specific embodiment is as follows: set a to the patient identification number, where "X" is replaced with "0", and b to the patient-supplied password, such as an 8 digit number. When a patient main index is established, a dynamic update model is called to generate a unique identifier a ' of the patient, and according to the unique identifier and the patient identity information, an encrypted first numerical value is obtained through a homomorphic encryption pailler algorithm, namely P (a ') + P (b) ═ P (a ' + b) is calculated. The patient address value is then calculated by the sha256hash160 algorithm: sha256hash160(P (a ') + P (b')).
For each time of a patient applying for historical data, a password b is input, the method calculates sha256hash160(P (b)) + P (sha1(sha1 (identification number + postfix code)))) to obtain id of a main index of the patient, and historical clinic data can be searched. If the patient password is incorrectly entered, the sha256hash160(P (b') + P (sha1(sha1 (id number + suffix code)))) will not obtain the corresponding medical data. If the patient password is modified from b to c, only the value of P (c) needs to be recalculated, the original main index key value is calculated by using sha256hash160(P (b)) + P (sha1(sha1 (identification number + postfix code))), the value of the original main index key value is assigned to the key value of sha256hash160(P (c)) + P (sha1(sha1 (identification number + postfix code))), and the value of sha256hash160(P (b)) + P (sha1(sha1 (identification number + postfix code))) is emptied.
Therefore, the health information of the patient cannot be directly acquired through the identity card number, so that a malicious user is prevented from crawling the information of the doctor of all the patients, the data privacy is ensured, the control right and the data are separated, and the method and the device are suitable for a plurality of scenes.
Preferably, the establishing of the patient health main index model comprises: the visit characteristics of a patient are marked from the following table dimensions, all of which are string types. Preferably, the generating method of the patentid is as follows: sha1(sha1 (identification number + postfix code)), the postfix code is systematic code, and the postfix code is different between different mechanisms so as to avoid duplication. Wherein, sha1 is a secure hash algorithm.
TABLE 1
| Serial number | Data item | Description of the |
| 1 | patientid | |
| 2 | hospitalCode | Hospital for |
| 3 | diseaseCode | Disease coding |
| 4 | cost | Amount of |
| 5 | diagnosis | Diagnosis of |
| 6 | doctorAdvise | Advice of doctor |
| 7 | enable | Marker bit |
In addition, the invention adopts the solid (the solid is the collection of codes and data at a specific address of the EtherFangblockchain) language to realize the acquisition of the corresponding medical data based on the patient health model:
struct MainIndexDetail{
address patientid；
string hospitalCode；
string diseaseCode；
string cost；
string diagnosis；
string doctorAdvise；
bytes32 media _ info; v/medical information index data Structure Address
boolean enable；
}
The data structure can calculate bytes32 value of the structure instance through sha256 algorithm, and the bytes32 value is used as a link address value and is used as a retrieval key subsequently. Meanwhile, defining a global storage map, and storing all uploaded data instances, wherein the key is the bytes32 value, and the following is a solid format map:
mapping (bytes32 ═ mainlndexdetail) main _ index _ detail _ mapping. The method is used for storing the mapping relation between the main index and the address, so that the numerical value of the specific field of the main index can be obtained as long as the address value is obtained, and the model information of the patient can be obtained.
It should be further noted that the present invention further defines a primary index upload contract, generating a parent _ time _ series core data structure:
function upload_index(MainIndexDetail mainindex,string date)returns(uint8 code){
if (upload right does not exist) front face
Returning no authority;
}
calculating bytes32 values using sha256 according to the primary index detail data structure;
main_index_detail_mapping[bytes32]＝mainindex；
// store the details of the present visit
if (bytes32 are not in the bytes32 array)
Patient_time_series[mainindex.patientid][date].push(bytes32)；
This step saves the link address into the time axis visit array
}
}
Step S102, identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node.
In some embodiments, the medical data is linearly fitted based on a time dimension to obtain time axis nodes, the medical data is linearly fitted through a cubic spline interpolation fitting algorithm to obtain time axis nodes, a patient health time linear model is generated, and health data prediction is performed through the patient health time linear model. That is, a certain dimension of the patient health model is used as a basic unit, cubic spline interpolation fitting is performed in a time dimension, time axis nodes are calculated by a time axis generation algorithm by utilizing the automatic execution characteristic of an intelligent contract, and the time axis nodes are connected in series through a time axis data structure. Preferably, the patient view primary index data structure is a mapping of an array of primary index health data structures keyed by date (yyyyMMdd format) with the patient core health model data as the mapping structure and the patient address as the key. mapping (patient address > mapping (date > primary index details array)). The specific code and data structure is:
mapping(address＝>mapping(string＝>bytes32[]))Patient_time_series；
the aforementioned mapping is used as a standard structure not only for storing and logic of the diagnosis timeline in the patient view, but also for the diagnosis timeline, medication timeline, examination timeline, and the like.
Further, based on the basic structure of medical data of the patient health model, the characteristic value of the present visit data of the patient can be obtained according to the examination of a specific item, and the following characteristic value of the patient health designed for the present invention is an array in the bytes32 format, and can be linked to the block and the details of the detailed data content. The linking mode is that the sha256 algorithm calculates bytes32 value of the structure instance and obtains the byte 32 value in the detailed information mapping map.
struct Medical_Info{
bytes32[ ] diagnosis; // diagnosis
bytes32[ ] exam; v/inspection of
bytes32 cis _ main; // first page of medical record
bytes32[ ] description; // prescription
bytes32[ ] docctororder; // order for doctor
bytes32[ ] temp; // body temperature
bytes32[ ] blood; v/recording with blood
}
A storage mapping (id ═ Medical _ Info data structure instance) defining this data structure is a solid format mapping as follows:
mapping(bytes32＝>Medical_Info)medical_detail_mapping；
the mapping relation between the content and the address used for saving the details of the medical data can be obtained by only obtaining the address value of the data structure, and then the numerical value of the specific field corresponding to the medical data can be obtained, and the specific type information of the patient for seeing a doctor can be obtained.
In addition, the invention defines a medical data uploading contract, and respectively processes the medical data according to different attributes, taking a prescription as an example:
the data structure on the block chain of the recipe is struct:
struct Prescription{
string PrescriptionId; // prescription number
string description _ content; v/prescription content, encrypted using public keys
string timing; // square root time
bolean enable; // whether Start flag
}
Uploading the prescription information to the prescription map in the block chain and returning the bytes32 address value;
storing the address value of the prescription information into a description array of the medical information index data, and judging repeatedly before adding;
function add(string patientid,string date,string jzlsh,Prescription prescriptions)returns(uint8){
calculating the address a of the patient by using the patientid;
calculating bytes32 b using jzlsh and the healthcare facility code;
storing the prescription into the prescription mapping of the block chain to obtain pres _ bytes 32;
saving pres _ bytes32 of the recipe to a media _ detail _ mapping [ b ] description array
if(Patient_time_series[a].enable＝＝false){
Patient_time_series[a][date]＝b；
}
return 200；
}
And S103, generating and outputting a patient health data view based on a preset time axis data structure according to the time axis nodes.
In some embodiments, based on a preset time axis data structure, as shown in fig. 2, the index data generated by the patient health main index model is calculated to obtain a corresponding second value, i.e. the index address. And each index address stores medical data in turn according to a time dimension, for example, the stored medical data comprises diagnosis results, medical orders, disease types, money amounts, hospital visits and the like, and the medical data at each time point can be stored into one data instance. And a unique address is generated for each attribute of the medical data respectively and is correspondingly stored in a data instance. Preferably, the bytes32 value of the structure instance is calculated by the sha256 algorithm as the link address value.
In still other embodiments, the present invention provides a query interface, which is called by exposing the timeline (as shown in fig. 3) on the front page, so as to obtain the corresponding view of the patient health data on the blockchain. If a detailed page information instruction for presenting the health data of the patient is received, the ip address of the corresponding front-end processor is obtained, the external medical system is accessed, and the detailed page information is called. Among other things, the present invention establishes query intelligence contracts to automatically execute timeline patient health data views. Specifically, the method comprises the following steps:
function query360(string patientid,string date)returns(bytes32[]){
calculating the address of patient Patientid;
return Patient_time_series[address(patientid)][date]；
}
function query360detail(bytes32 detail_id)returns(MainIndexDetail mainindexdetail){
return main_index_detail_mapping[detail_id]；
}
function query360medical_detail(bytes32 medical_detail_id)returns(Medical_Info medical_info){
return medical_detail_mapping[medical_detail_id]；
}
as another embodiment, the method may further receive a view update instruction, update the intelligent contract according to a preset view, and determine an update permission based on the identity in the view update instruction, so as to obtain corresponding medical data from the blockchain and perform update; and encrypting the updated medical data through the platform public key, and further storing the encrypted medical data into the corresponding block chain address. That is to say, the invention establishes the patient update intelligent contract, because the special condition needs to update the view, or the medical data needs to be perfect, the intelligent contract is updated to modify, the permission modification is ensured, and the behavior is recorded.
It is also worth explaining that the invention starts the monitoring program, obtains the circulation log and the circulation request of the medical data of the patient, and records the circulation log and the circulation request into the preset distributed account book. Therefore, real-time monitoring and recording of medical data processing are achieved.
In conclusion, the invention realizes the time axis of the patient view based on the intelligent contract of the block chain, improves the credibility of data, improves the time dimension of the main index of the patient, can be popularized to the view angles with different dimensions by the invention, integrates the hashed patient data into a continuous patient health tracking structure, and greatly improves the range data query function in the block chain medical information sharing system. And moreover, the system is designed in a layered mode based on the block chain, so that the data safety is ensured, the expandability of the system is improved, and the seamless connection of the data during upgrading is ensured. In addition, in medical information sharing, a patient view is established in a decentralized mode through an intelligent contract, the flexibility of the system is improved, and the adaptability of the system is improved by combining direct display of a front end. Furthermore, the invention can avoid data loss or errors based on sharing the patient view of the ledger storage.
Fig. 4 is a schematic diagram of a main flow of a medical data view implementation method according to a second embodiment of the present invention, where the present invention incorporates a medical institution into an alliance chain by means of a block chain characteristic, performs circulation of medical data through a P2P network protocol of the block chain, integrates data dispersed in each information island through a unified model through characteristics such as a distributed ledger, a consensus mechanism, privacy protection, and security traceability, and finally generates a trusted electronic health record of residents. Preferably deployed in the FISCO BCOS (financial version block chain underlying platform) alliance chain using smart contracts developed based on gender, providing a mapping structure type based on the time dimension of the patient, storing patient view data, and visually operating and presenting in Dapp (decentralized application) fashion. Specifically, the method comprises the following steps:
and storing index data generated by the patient health main index model into a block chain according to a preset main index uploading contract, and further acquiring corresponding medical data based on the patient health model according to the index data. Identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node. And finally, generating a patient health data view based on a preset time axis data structure according to the time axis nodes and outputting the view. And calculating to obtain a corresponding second numerical value, namely an index address, through index data generated by the patient health main index model. And each index address stores medical data in turn according to a time dimension, for example, the stored medical data comprises diagnosis results, medical orders, disease types, money amounts, hospital visits and the like, and the medical data at each time point can be stored into one data instance. And a unique address is generated for each attribute of the medical data respectively and is correspondingly stored in a data instance. Preferably, the bytes32 value of the structure instance is calculated by the sha256 algorithm as the link address value. Preferably, preset patient identity information is acquired, and a dynamic update model is invoked to generate a unique identifier of the patient. And according to the unique identifier and the preset password of the patient, obtaining an encrypted first numerical value through a homomorphic encryption pailler algorithm, and further processing the first numerical value by utilizing a sha256hash160 algorithm to obtain a second numerical value.
Therefore, under the scene of government medical insurance business, the regional medical data sharing system obtains the data of seeing a doctor from each hospital and intelligently generates a patient view through an intelligent contract, the regional medical data sharing system can directly display the patient view stored on the block chain through a front-end page and can also inquire the information of the patient through the intelligent contract of other dimensions, such as time period, diseases, hospitals, expenses and the like, the detailed data in the patient view can also provide access paths and parameters of a front-end processor, and the detailed data content is forwarded after the platform carries out authority verification and requests log record. In the prescription circulation business scene, a patient view-prescription time axis is established, and when a patient goes to a hospital for a doctor, a doctor is authorized to check the historical data of the patient as reference by inputting a password. After the prescription flows out, the prescription is taken in a designated pharmacy, and when the prescription is checked by a pharmacist, the password is input, so that the pharmacist can be authorized to check the historical prescription information of the patient himself and the historical prescription information can be used as the reference of the checking. The platform side can analyze, group and predict according to the time axis of the patient view, and provide health management and insurance services for the platform side.
Fig. 5 is a schematic diagram of a main flow of a medical data view implementation method according to a third embodiment of the present invention, the medical data view implementation method including:
step S501, a patient health main index model is established, and index data is generated through the patient health main index model according to an uploading contract of a preset main index.
Step S502, acquiring preset patient identity information, and calling a dynamic update model to generate a unique identifier of the patient.
Step S503, according to the unique identifier and the preset password of the patient, obtaining an encrypted first numerical value through a homomorphic encryption pailler algorithm, further processing the first numerical value through a sha256hash160 algorithm to obtain a second numerical value, and storing the second numerical value and the index data into a block chain.
And step S504, acquiring corresponding medical data based on the patient health model according to the index data.
And S505, identifying a target dimension in the medical data based on the patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on the time dimension by taking the target dimension as a basic unit to obtain a time axis node.
And step S506, generating a patient health data view based on a preset time axis data structure according to the time axis nodes.
And step S507, displaying a time axis through a front-end page, and calling a query interface to further obtain a corresponding patient health data view on the block chain.
And step S508, if a detailed page information instruction for presenting the health data of the patient is received, acquiring the ip address of the corresponding front-end processor, accessing the external medical system, and calling the detailed page information.
The patient view time axis is shared by multiple medical institutions, the data circulation and the availability are effectively improved, and data analysis can be performed on big data. In addition, according to the patient health model, part of medical information is acquired and screened through the block chain, the number of indexes is calculated, the health model of the patient in a certain time period is formed, the model result of the patient is continuously updated through intelligent contract and the like, and the accuracy of the patient health view model is improved.
Fig. 6 is a schematic diagram of main modules of a medical data view implementation system according to an embodiment of the present invention, and as shown in fig. 6, the medical data view implementation system 600 includes an acquisition module 601 and a processing module 602. The obtaining module 601 builds a patient health main index model, so as to store index data generated by the patient health main index model into a block chain according to a preset main index upload contract, and further obtain corresponding medical data based on the patient health model according to the index data. The processing module 602 identifies a target dimension in medical data based on a patient health model, calls a preset view intelligent contract, and performs linear fitting on the medical data based on a time dimension with the target dimension as a basic unit to obtain a time axis node; and generating a patient health data view based on a preset time axis data structure according to the time axis nodes and outputting the view.
In some embodiments, the obtaining module 601, after obtaining the index data generated by the patient health main index model, includes:
and acquiring preset patient identity information, and calling a dynamic update model to generate a unique identifier of the patient. And according to the unique identifier and the preset password of the patient, obtaining an encrypted first numerical value through a homomorphic encryption pailler algorithm, further processing the first numerical value by utilizing a sha256hash160 algorithm to obtain a second numerical value, and using the second numerical value as an address value for searching index data stored in the block chain.
In some embodiments, the processing module 602 performs a linear fit to the medical data based on the time dimension to obtain a timeline node, including:
and performing linear fitting on the medical data through a cubic spline interpolation fitting algorithm to obtain time axis nodes, generating a patient health time linear model, and predicting health data through the patient health time linear model.
In some embodiments, the processing module 602 processes the timeline data structure, including:
calculating to obtain a corresponding second numerical value as an index address through index data generated by the patient health main index model; each index address sequentially stores medical data according to time dimension, and the medical data of each time point is stored into one data instance; and respectively generating a unique address for the medical data of each attribute, and correspondingly storing the unique address into a data instance.
In some embodiments, the processing module 602 is further configured to:
receiving a view updating instruction, updating an intelligent contract according to a preset view, and determining an updating authority based on an identity in the view updating instruction so as to acquire corresponding medical data from a block chain and execute updating; and encrypting the updated medical data through the platform public key, and further storing the encrypted medical data into the corresponding block chain address.
In some embodiments, the processing module 602 is further configured to: and starting a monitoring program, acquiring a circulation log and a circulation request of the medical data of the patient, and recording the circulation log and the circulation request into a preset distributed account book.
In some embodiments, the processing module 602 is further configured to: displaying a time axis through a front-end page, calling a query interface, and further acquiring a corresponding patient health data view on a block chain; if a detailed page information instruction for presenting the health data of the patient is received, the ip address of the corresponding front-end processor is obtained, the external medical system is accessed, and the detailed page information is called.
It should be noted that, the medical data view implementation method and the medical data view implementation system of the present invention have corresponding relationships in the specific implementation contents, and therefore, the repeated contents are not described again.
Fig. 7 illustrates an exemplary system architecture 700 of a medical data view implementation method or medical data view implementation system to which embodiments of the invention may be applied.
As shown in fig. 7, the system architecture 700 may include terminal devices 701, 702, 703, a network 704, and a server 705. The network 704 serves to provide a medium for communication links between the terminal devices 701, 702, 703 and the server 705. Network 704 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
A user may use the terminal devices 701, 702, 703 to interact with a server 705 over a network 704, to receive or send messages or the like. The terminal devices 701, 702, 703 may have installed thereon various communication client applications, such as a shopping-like application, a web browser application, a search-like application, an instant messaging tool, a mailbox client, social platform software, etc. (by way of example only).
The terminal devices 701, 702, 703 may be various electronic devices having medical data view-enabling screens and supporting web browsing, including but not limited to smart phones, tablets, laptop portable computers, desktop computers, and the like.
The server 705 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 701, 702, 703. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the medical data view implementation method provided by the embodiment of the present invention is generally executed by the server 705, and accordingly, the computing system is generally disposed in the server 705.
It should be understood that the number of terminal devices, networks, and servers in fig. 7 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 8, shown is a block diagram of a computer system 800 suitable for use with a terminal device implementing an embodiment of the present invention. The terminal device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 8, the computer system 800 includes a Central Processing Unit (CPU)801 that can perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage section 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data necessary for the operation of the computer system 800 are also stored. The CPU801, ROM802, and RAM803 are connected to each other via a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
The following components are connected to the I/O interface 805: an input portion 806 including a keyboard, a mouse, and the like; an output section 807 including a display such as a Cathode Ray Tube (CRT), a liquid crystal medical data view implementer (LCD), and a speaker; a storage portion 808 including a hard disk and the like; and a communication section 809 including a network interface card such as a LAN card, a modem, or the like. The communication section 809 performs communication processing via a network such as the internet. A drive 810 is also connected to the I/O interface 805 as necessary. A removable medium 811 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 810 as necessary, so that a computer program read out therefrom is mounted on the storage section 808 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program can be downloaded and installed from a network through the communication section 809 and/or installed from the removable medium 811. The computer program executes the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 801.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams or flowchart illustration, and combinations of blocks in the block diagrams or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module and a processing module. Wherein the names of the modules do not in some cases constitute a limitation of the module itself.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs, and when the one or more programs are executed by one of the devices, the one or more programs enable the device to include a patient health main index model, so that index data generated by the patient health main index model is stored in a block chain according to a preset main index upload contract, and corresponding medical data based on the patient health model is obtained according to the index data; identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node; and generating a patient health data view based on a preset time axis data structure according to the time axis nodes and outputting the view.
According to the technical scheme of the embodiment of the invention, the embodiment of the invention can solve the problems of high cost and great difficulty in the existing medical data sharing among multiple mechanisms.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A medical data view implementation method is characterized by comprising the following steps:
establishing a patient health main index model, storing index data generated by the patient health main index model into a block chain according to a preset main index uploading contract, and further acquiring corresponding medical data based on the patient health model according to the index data;
identifying a target dimension in medical data based on a patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node;
and generating a patient health data view based on a preset time axis data structure according to the time axis nodes and outputting the view.
2. The method of claim 1, wherein the index data generated by the patient health master index model is followed by:
acquiring preset patient identity information, and calling a dynamic update model to generate a unique identifier of the patient;
and according to the unique identifier and the preset password of the patient, obtaining an encrypted first numerical value through a homomorphic encryption pailler algorithm, further processing the first numerical value by utilizing a sha256hash160 algorithm to obtain a second numerical value, and using the second numerical value as an address value for searching index data stored in the block chain.
3. The method of claim 1, wherein linearly fitting the medical data based on a time dimension to obtain timeline nodes comprises:
and performing linear fitting on the medical data through a cubic spline interpolation fitting algorithm to obtain time axis nodes, generating a patient health time linear model, and predicting health data through the patient health time linear model.
4. The method of claim 1, wherein the timeline data structure comprises:
calculating to obtain a corresponding second numerical value as an index address through index data generated by the patient health main index model;
each index address sequentially stores medical data according to time dimension, and the medical data of each time point is stored into one data instance; and respectively generating a unique address for the medical data of each attribute, and correspondingly storing the unique address into a data instance.
5. The method of claim 1, further comprising:
receiving a view updating instruction, updating an intelligent contract according to a preset view, and determining an updating authority based on an identity in the view updating instruction so as to acquire corresponding medical data from a block chain and execute updating;
and encrypting the updated medical data through the platform public key, and further storing the encrypted medical data into the corresponding block chain address.
6. The method of claim 1, further comprising:
and starting a monitoring program, acquiring a circulation log and a circulation request of the medical data of the patient, and recording the circulation log and the circulation request into a preset distributed account book.
7. The method of any of claims 1-6, further comprising:
displaying a time axis through a front-end page, calling a query interface, and further acquiring a corresponding patient health data view on a block chain;
if a detailed page information instruction for presenting the health data of the patient is received, the ip address of the corresponding front-end processor is obtained, the external medical system is accessed, and the detailed page information is called.
8. A medical data view implementation system, comprising:
the acquisition module is used for establishing a patient health main index model, storing index data generated by the patient health main index model into a block chain according to a preset main index uploading contract, and further acquiring corresponding medical data based on the patient health model according to the index data;
the processing module is used for identifying a target dimension in the medical data based on the patient health model, calling a preset view intelligent contract, and performing linear fitting on the medical data based on a time dimension by taking the target dimension as a basic unit to obtain a time axis node; and generating a patient health data view based on a preset time axis data structure according to the time axis nodes and outputting the view.
9. An electronic device, comprising:
one or more processors;
a storage system for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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