CN114465828A - Case data processing method for medical system - Google Patents

Case data processing method for medical system Download PDF

Info

Publication number
CN114465828A
CN114465828A CN202210376560.1A CN202210376560A CN114465828A CN 114465828 A CN114465828 A CN 114465828A CN 202210376560 A CN202210376560 A CN 202210376560A CN 114465828 A CN114465828 A CN 114465828A
Authority
CN
China
Prior art keywords
case
child node
patient
decision tree
data
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202210376560.1A
Other languages
Chinese (zh)
Other versions
CN114465828B (en
Inventor
韩仕岳
周怡佳
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Xingchen Qilian Nanjing Digital Technology Co ltd
Original Assignee
Xingchen Qilian Nanjing Digital Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Xingchen Qilian Nanjing Digital Technology Co ltd filed Critical Xingchen Qilian Nanjing Digital Technology Co ltd
Priority to CN202210376560.1A priority Critical patent/CN114465828B/en
Publication of CN114465828A publication Critical patent/CN114465828A/en
Application granted granted Critical
Publication of CN114465828B publication Critical patent/CN114465828B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
    • H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H10/00—ICT specially adapted for the handling or processing of patient-related medical or healthcare data
    • G16H10/60—ICT specially adapted for the handling or processing of patient-related medical or healthcare data for patient-specific data, e.g. for electronic patient records
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
    • G16H80/00—ICT specially adapted for facilitating communication between medical practitioners or patients, e.g. for collaborative diagnosis, therapy or health monitoring
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
    • H04L63/0407—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the identity of one or more communicating identities is hidden
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L63/00—Network architectures or network communication protocols for network security
    • H04L63/06—Network architectures or network communication protocols for network security for supporting key management in a packet data network
    • H04L63/062—Network architectures or network communication protocols for network security for supporting key management in a packet data network for key distribution, e.g. centrally by trusted party
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network arrangements or protocols for supporting network services or applications
    • H04L67/01—Protocols
    • H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
    • H04L9/08—Key distribution or management, e.g. generation, sharing or updating, of cryptographic keys or passwords
    • H04L9/0816—Key establishment, i.e. cryptographic processes or cryptographic protocols whereby a shared secret becomes available to two or more parties, for subsequent use
    • H04L9/0819—Key transport or distribution, i.e. key establishment techniques where one party creates or otherwise obtains a secret value, and securely transfers it to the other(s)
    • H04L9/083—Key transport or distribution, i.e. key establishment techniques where one party creates or otherwise obtains a secret value, and securely transfers it to the other(s) involving central third party, e.g. key distribution center [KDC] or trusted third party [TTP]

Landscapes

  • Engineering & Computer Science (AREA)
  • Computer Security & Cryptography (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Health & Medical Sciences (AREA)
  • Computing Systems (AREA)
  • Medical Informatics (AREA)
  • General Health & Medical Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Computer Hardware Design (AREA)
  • Epidemiology (AREA)
  • Primary Health Care (AREA)
  • Public Health (AREA)
  • Biomedical Technology (AREA)
  • Pathology (AREA)
  • Medical Treatment And Welfare Office Work (AREA)

Abstract

The invention provides a case data processing method for a medical system, which comprises the steps of determining target case data of a target child node in a case decision tree through input data of a patient end based on a patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient; sending a case decision tree, a parent node key and a child node key which are preset at a patient end to a doctor end based on the sending request; obtaining target case data corresponding to child nodes in a case decision tree; the physician side updates the target case data based on the case adding data added by the physician to obtain updated case data, updates the case decision tree according to the updated case data to obtain an updated case decision tree, and sends the updated case decision tree to the patient side, so that the safety of the case data is improved, and data sharing is realized.

Description

Case data processing method for medical system
Technical Field
The invention relates to a data processing technology, in particular to a case data processing method for a medical system.
Background
With the continuous progress of science and technology, the living standard of people is continuously improved, and the existing medical system is continuously abundant, for example: provincial, urban, regional and rural hospitals, each hospital system storing abundant case data.
However, in existing medical systems, individual case data cannot be shared, for example: the case data of a plurality of hospitals still use case records, and every time a patient goes to the hospital to make an inquiry, a doctor updates the case data on the case records correspondingly, but if the patient transfers to the hospital, the corresponding case records are not applicable any more, and cannot be further updated on the basis of the past; some hospitals have developed electronic information systems to record personal data of patients, but the sharing of personal case data of patients cannot be realized, and part of the reasons are that the personal privacy of patients cannot be guaranteed after the patients access to a network, and in addition, because daily case data is huge, network paralysis can be easily caused, once a data server is paralyzed, the hospital cannot normally operate, and the problem that the past case data needs to be recovered is solved.
Therefore, how to realize secure sharing of personal case data becomes an urgent problem to be solved.
Disclosure of Invention
The embodiment of the invention provides a case data processing method for a medical system, which can realize sharing of case data under the condition of ensuring safety of the case data of a patient, can enable the patient to select and display the type of a relevant case which the patient wants to ask based on personal intention of the patient, and can hide the type of the case which is irrelevant to the inquiry.
In a first aspect of the embodiments of the present invention, a method for processing case data for a medical system is provided, where a patient transmits case data to a physician end of a physician based on a patient end, and the method specifically includes:
the patient end determines target case data of a target child node in a case decision tree based on input data of a patient, generates a child node key of the target child node according to the target case data, and generates a parent node key of the case decision tree according to patient attribute data of the patient;
sending a case decision tree, a parent node key and a child node key which are preset at a patient end to a doctor end based on the sending request;
the physician side decrypts the case decision tree for the first time based on the parent node key to obtain the patient attribute data corresponding to the parent node in the case decision tree, and decrypts the case decision tree for the second time based on the child node key to obtain the target case data corresponding to the child node in the case decision tree;
and the physician end updates the target case data based on the case adding data added by the physician to obtain updated case data, updates the case decision tree according to the updated case data to obtain an updated case decision tree, and sends the updated case decision tree to the patient end.
Optionally, in a possible implementation manner of the first aspect, initializing the case decision tree by the following steps, specifically including:
the patient end receives historical case data input by a user, and sequentially extracts patient attribute data, case type data and case data corresponding to each case type data in the historical case data;
constructing parent nodes of a case decision tree according to the patient attribute data;
constructing a corresponding number of decision tree child nodes according to the case category data, wherein each decision tree child node is connected with the decision tree parent node;
and constructing decision tree grandchild nodes in a corresponding number according to the case data corresponding to each case category data, wherein each decision tree grandchild node is connected with a corresponding decision tree child node.
Alternatively, in one possible implementation form of the first aspect,
the steps of determining target case data of a target child node in a case decision tree based on input data of a patient from a patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient specifically include:
generating a target child node by a patient based on input data of the patient, and acquiring target case data at a target grandchild node connected with the target child node in a case decision tree;
counting the number of the acquired target grandchild nodes, the occupied capacity of each target grandchild node, the current inquiry initial time and a first random number to generate a first character string, calculating the first character string based on a Hash algorithm to obtain a first Hash value, and taking the first Hash value as a first child node key, wherein the first child node key is the key of the target child node;
generating a second character string according to the patient age data, the patient height data, the patient communication data and a second random number, calculating the second character string based on a Hash algorithm to obtain a second Hash value, and taking the second Hash value as a parent node key;
a first child node key and a parent node key are generated by the following formulas,
Figure 723126DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 92796DEST_PATH_IMAGE002
is a first sub-node key that is,
Figure 341375DEST_PATH_IMAGE003
is a key of a parent node, and is,
Figure 205426DEST_PATH_IMAGE004
in order to be a function of the hash function,
Figure 236442DEST_PATH_IMAGE005
the number of nodes that are the target grandchild node,
Figure 527746DEST_PATH_IMAGE006
in order to examine the initial time of the diagnosis,
Figure 263621DEST_PATH_IMAGE007
is a first random number that is a random number,
Figure 931363DEST_PATH_IMAGE008
is as follows
Figure 318350DEST_PATH_IMAGE009
The capacity of the target grandchild node is occupied,
Figure 46135DEST_PATH_IMAGE010
is the data of the age of the patient,
Figure 3727DEST_PATH_IMAGE011
is the height data of the patient and is the height data of the patient,
Figure 209580DEST_PATH_IMAGE012
an upper limit value for the patient communication data,
Figure 952539DEST_PATH_IMAGE013
is as follows
Figure 851225DEST_PATH_IMAGE014
A value corresponding to the patient communication data,
Figure 561692DEST_PATH_IMAGE015
is a second random number.
Optionally, in a possible implementation manner of the first aspect, in the step of determining, by the patient, target case data of a target child node in the case decision tree based on input data of the patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient, the method specifically includes:
determining all child nodes by the patient end based on the input data of the patient, and acquiring all case data at all grandchild nodes connected with all child nodes in the case decision tree;
counting the number of nodes of all grandchild nodes corresponding to all acquired child nodes, wherein all grandchild nodes occupy the capacity, the initial time of next inquiry and the first random number to generate a third character string, calculating the third character string based on a hash algorithm to obtain a third hash value, and taking the third hash value as a second child node key, wherein the second child node key is the key of all child nodes;
the second child node key is generated by the following formula,
Figure 305657DEST_PATH_IMAGE016
wherein the content of the first and second substances,
Figure 667237DEST_PATH_IMAGE017
is the key of the second child node and,
Figure 471245DEST_PATH_IMAGE018
as the number of nodes of all the grandchild nodes,
Figure 669009DEST_PATH_IMAGE006
in order to address the initial moment of the next inquiry,
Figure 216665DEST_PATH_IMAGE004
in order to be a function of the hash function,
Figure 948864DEST_PATH_IMAGE007
is a first random number that is a random number,
Figure 189353DEST_PATH_IMAGE019
is as follows
Figure 608833DEST_PATH_IMAGE020
The grandchild node of each patient occupies capacity.
Optionally, in a possible implementation manner of the first aspect, the method further includes:
the patient end determines a viewing sub-node that needs to be viewed by a physician based on the input data of the patient;
traversing all child nodes in the case decision tree, and if the target child node corresponding to the viewing child node does not exist, generating a target child node corresponding to the viewing child node;
connecting the newly generated target child node as a new added child node with the parent node of the decision tree, and updating the case decision tree;
and configuring an initial key for the newly added child node.
Optionally, in a possible implementation manner of the first aspect, the method further includes:
after receiving a case decision tree sent by a patient end, a doctor end generates a new child node of a corresponding case type based on case type data input by the doctor;
generating an additional grandchild node corresponding to the additional child node according to case data input by a doctor end to obtain an updated case decision tree of the doctor end;
after receiving the case decision tree updated by the doctor, the patient extracts case data corresponding to the new child node to generate a new child node key if judging that the new child node exists in the case decision tree.
Optionally, in a possible implementation manner of the first aspect, the method further includes:
initializing the pre-allocation capacity of each child node in the decision tree according to the initial number of the child nodes in the decision tree;
after judging the newly added child nodes, acquiring the pre-distribution capacity of the child nodes of each child node in the case decision tree and the current occupied capacity of each child node to obtain the residual capacity of each child node, and obtaining the residual total capacity according to the residual capacities of all the child nodes;
acquiring case category data corresponding to each child node, and determining a first distribution weight corresponding to each child node according to the case category data;
acquiring case data corresponding to all grandchild nodes of each child node, determining the last inquiry time closest to the current time in the case data, and determining the second distribution weight corresponding to each child node according to the last inquiry time;
and determining the newly-added capacity of the newly-added child node based on the first distribution weight, the second distribution weight, the node number of the grandchild node and the remaining total capacity.
Optionally, in a possible implementation manner of the first aspect, in the step of determining a newly added capacity of a newly added child node based on the first allocation weight, the second allocation weight, the number of nodes of the grandchild node, and the remaining total capacity, the method specifically includes:
the newly added capacity is calculated by the following formula,
Figure 225759DEST_PATH_IMAGE021
wherein the content of the first and second substances,
Figure 47085DEST_PATH_IMAGE022
in order to newly increase the capacity of the child node,
Figure 707742DEST_PATH_IMAGE023
the first assigned weight of the child node is newly added,
Figure 614518DEST_PATH_IMAGE024
weights are assigned to the second of the newly added child nodes,
Figure 238397DEST_PATH_IMAGE025
for an upper bound on the number of nodes of the case decision tree child node,
Figure 930541DEST_PATH_IMAGE026
is the number of nodes of the child node,
Figure 512832DEST_PATH_IMAGE027
is as follows
Figure 172484DEST_PATH_IMAGE028
The number of nodes of the grandchild node to which the child node is connected,
Figure 865633DEST_PATH_IMAGE023
is as follows
Figure 395971DEST_PATH_IMAGE028
A first assigned weight for a child node,
Figure 398431DEST_PATH_IMAGE024
is as follows
Figure 279800DEST_PATH_IMAGE028
The second assignment of weights to the child nodes,
Figure 776640DEST_PATH_IMAGE029
the number of nodes that are standard grandchild nodes,
Figure 427064DEST_PATH_IMAGE030
is as follows
Figure 833382DEST_PATH_IMAGE028
The child nodes of the child nodes pre-allocate capacity,
Figure 202046DEST_PATH_IMAGE031
is a first
Figure 236998DEST_PATH_IMAGE028
The current occupied capacity of the child node(s),
Figure 7508DEST_PATH_IMAGE032
in order to have a remaining total capacity,
Figure 86191DEST_PATH_IMAGE033
the value is adjusted for the new increased capacity.
Alternatively, in one possible implementation form of the first aspect,
generating an inquiry frequency of each child node according to the number of nodes of grandchild nodes connected with each child node of the case decision tree, the inquiry initiation time corresponding to the initial grandchild node connected with each child node and the inquiry initiation corresponding to the final grandchild node connected with each child node;
generating a child node portrait according to the inquiry frequency of each child node and the number of child nodes of the case decision tree;
generating a decision tree portrait according to the child node portrait and the frequency weight of the child node;
obtaining a first integrated inspection time period according to the decision tree image;
the decision tree portrayal and the first volume examination time period are calculated by the following formula,
Figure 942152DEST_PATH_IMAGE034
wherein the content of the first and second substances,
Figure 515216DEST_PATH_IMAGE035
in order to make a decision tree representation,
Figure 140232DEST_PATH_IMAGE036
is the initial moment of the next inquiry corresponding to the initial grandchild node connected with the first child node,
Figure 156861DEST_PATH_IMAGE037
is as follows
Figure 234538DEST_PATH_IMAGE038
When the initial inquiry moment of the current inquiry is corresponding to the terminal grandchild node connected with the child node,
Figure 611293DEST_PATH_IMAGE039
is as follows
Figure 356395DEST_PATH_IMAGE038
The number of nodes of the grandchild node to which the child node is connected,
Figure 42460DEST_PATH_IMAGE040
is as follows
Figure 607434DEST_PATH_IMAGE038
The frequency weight of the child node(s),
Figure 787879DEST_PATH_IMAGE041
for the decision tree image adjustment value,
Figure 121909DEST_PATH_IMAGE042
for the reference value of the decision tree image,
Figure 483270DEST_PATH_IMAGE043
is a first examination time period of the body examination,
Figure 535539DEST_PATH_IMAGE044
is a bodyThe adjustment value of the time period is detected,
Figure 254097DEST_PATH_IMAGE045
for an upper bound on the number of nodes of the case decision tree child node,
Figure 708212DEST_PATH_IMAGE046
the number of nodes that are child nodes.
Alternatively, in one possible implementation form of the first aspect,
acquiring a second physical examination time period when the patient actually performs physical examination;
obtaining a time adjustment trend according to the first volume inspection time period and the second volume inspection time period;
correcting the adjustment value of the physical examination time period according to the time adjustment trend to obtain a corrected adjustment value of the physical examination time period;
the corrected adjustment value of the physical examination time period is obtained by the following formula,
Figure 736079DEST_PATH_IMAGE047
wherein the content of the first and second substances,
Figure 275645DEST_PATH_IMAGE048
for the second examination period of time,
Figure 797893DEST_PATH_IMAGE049
the adjusted value is the adjusted value of the physical examination time period after the correction,
Figure 372094DEST_PATH_IMAGE050
the trend correction value is increased for the physical examination period,
Figure 72328DEST_PATH_IMAGE051
the trend correction value is reduced for the physical examination period.
In a second aspect of the embodiments of the present invention, a storage medium is provided, in which a computer program is stored, which, when being executed by a processor, is adapted to carry out the method according to the first aspect of the present invention and various possible designs of the first aspect of the present invention.
In a third aspect of the embodiments of the present invention, a readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, is adapted to implement the first aspect of the present invention and the methods according to the first aspect of the present invention.
According to the medical system case data processing method provided by the invention, the sharing of case data is realized through the interaction of the patient side and the doctor side, so that the doctor can refer to the past case data, and the acquired reference information is more comprehensive; according to the invention, the parent node key is used for protecting the personal attribute data, the basic information of the patient is protected, the information stealing is prevented, the protection of the privacy data of the patient is enhanced through the dynamically set child node key, the patient can select the corresponding case data under the case type to be displayed in the inquiry, the safety of the personal case data is improved, and the privacy function is increased.
The technical scheme provided by the invention can generate a corresponding parent node key and a corresponding child node key by utilizing a hash function based on a case decision tree generated before, wherein the parent node key can be generated based on the attribute data of each patient, such as: a random mother key is generated according to the height, the age, the mobile phone number and the like, the mother key of each person is different, and the mother keys generated each time are different due to the addition of random numbers, so that the safety of basic data of a patient is greatly improved; the child keys are generated according to the number of the grandchild nodes, the memory occupied by each child node, the inquiry time and the like, at the moment, the child node keys added with random numbers have randomness, and meanwhile, the memory capacity of information such as medical advice and the like added by a doctor each time is also random, so that the child node keys of patients after each visit can be refreshed, and the safety of case data is greatly improved.
According to the technical scheme provided by the invention, a plurality of scenes shared with a doctor end are provided, a patient can only display case data of a case type corresponding to the inquiry through the patient end, the keys of corresponding child nodes are different, and other part of case data are hidden to protect the privacy of the patient; many times, some diseases are complications caused by the simultaneous existence of several types of case data, and patients can also share the whole case decision tree, and all corresponding child nodes can generate the same child node key, so that doctors can conveniently check the case data, 2 different case data sharing modes are provided, the method is more practical, and the case data interaction efficiency is improved.
The technical scheme provided by the invention can obtain the residual capacity of each sub-node through the quantity of the sub-nodes of the case types, the initial memory capacity pre-allocated before and the current occupied capacity, so as to obtain the total residual capacity, and according to the total residual capacity and each case type, for example: the method comprises the steps of carrying out dynamic allocation on the residual capacity, selecting some common diseases to allocate some memories, selecting some recently seen diseases to allocate some memories for storing the follow-up examinations and the like, carrying out intelligent allocation on the capacity according to the types of the cases, carrying out zoning according to the types of the cases, and reducing the data retrieval time when the case data is extracted and used.
According to the technical scheme provided by the invention, the decision tree representation is generated according to the patient's seeing frequency and the number of the types of the cases, a time period for automatically reminding the patient of carrying out physical examination for a long time is generated according to the decision tree representation, and the adjustment is carried out according to the time period of the patient according to the actual need of the physical examination list, so that the result is more accurate.
Drawings
Fig. 1 is a schematic view of an application scenario of the technical solution provided by the present invention;
FIG. 2 is a flow chart of a first embodiment of a method for medical system case data processing;
FIG. 3 is a flow chart of a second embodiment of a method for medical system case data processing;
FIG. 4 is a schematic diagram of a medical data processing system for a medical system;
fig. 5 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The terms "first," "second," "third," "fourth," and the like in the description and in the claims, as well as in the drawings, if any, are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the invention described herein are capable of operation in sequences other than those illustrated or described herein.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the internal logic of the processes, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that in the present application, "comprising" and "having" and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
It should be understood that, in the present invention, "a plurality" means two or more. "and/or" is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship. "comprises A, B and C" and "comprises A, B, C" means that A, B, C all comprise, "comprises A, B or C" means comprise one of A, B, C, "comprises A, B and/or C" means comprise any 1 or any 2 or 3 of A, B, C.
It should be understood that in the present invention, "B corresponding to a", "a corresponds to B", or "B corresponds to a" means that B is associated with a, and B can be determined from a. Determining B from a does not mean determining B from a alone, but may be determined from a and/or other information. And the matching of A and B means that the similarity of A and B is greater than or equal to a preset threshold value.
As used herein, "if" may be interpreted as "at … …" or "when … …" or "in response to a determination" or "in response to a detection", depending on the context.
The technical solution of the present invention will be described in detail below with specific examples. The following several specific embodiments may be combined with each other, and details of the same or similar concepts or processes may not be repeated in some embodiments.
As shown in fig. 1, for a scene schematic diagram of the technical solution provided by the present invention, a patient selects case category data under a case data decision tree to be displayed by using a patient end, and sends the case decision tree, a parent node key and a child node key to a physician end, the patient end is connected with the physician end in a wired or wireless connection, which is not limited herein, the physician decrypts the case data decision tree twice based on the parent node key and the child node key, respectively obtains patient attribute data at the parent node and case data at a grandchild node under target case category data of the child node, the physician adds present case data content to generate a new grandchild node, correspondingly generates a new case decision tree, and sends the new case decision tree to the patient end through the physician end for updating, which can be understood, the patient end may be a mobile device such as a mobile phone and a tablet, but is not limited thereto, and the doctor end may be a processor device such as a laptop and a desktop.
According to the technical scheme provided by the invention, the sharing of individual case data is realized through the patient end carried by the patient, the safety of the patient case data is ensured in a dynamic encryption mode, and meanwhile, the patient can select the data to be displayed in each inquiry so as to ensure the individual privacy.
The invention provides a case data processing method for a medical system, as shown in fig. 2, a patient transmits case data to a doctor end of a doctor based on a patient end, and the method specifically comprises the following steps:
step S110, the patient end determines target case data of a target child node in the case decision tree based on input data of the patient, generates a child node key of the target child node according to the target case data, and generates a parent node key of the case decision tree according to patient attribute data of the patient.
According to the technical scheme provided by the invention, a patient end actively inputs data to be displayed on the basis of the patient to determine target case data of a target child node in a case data case decision tree, generates a child node key corresponding to the target child node according to the target case data to be displayed of the patient, and generates a parent node key according to the age, the height and the communication data of the patient; for example: after the patient arrives at the consulting room, the patient operates the patient end to search and select the data to be displayed, and then target disease case data under the child nodes are used, for example: there are four cases of data under the fracture case category, that is, the patient is fractured four times, and the child node key is generated according to the time, the number of the four times and the occupied capacity of the four times, and the data is obtained by the patient attribute data, for example: the age data of the patient, the height data of the patient and the communication data of the patient are not limited, and a master key is generated, so that the privacy of the personal case data is improved.
In a possible implementation manner of the technical solution provided by the present invention, step S110 specifically includes:
generating a target child node by a patient based on input data of the patient, and acquiring target case data at a target grandchild node connected with the target child node in a case decision tree;
counting the number of the acquired target grandchild nodes, the occupied capacity of each target grandchild node, the current inquiry initial time and a first random number to generate a first character string, calculating the first character string based on a Hash algorithm to obtain a first Hash value, and taking the first Hash value as a first child node key, wherein the first child node key is the key of the target child node; according to the technical scheme provided by the invention, the attribute data of the target grandchild node is encrypted by using the hash function, the encryption process of the hash function is irreversible, namely, what the original plaintext is cannot be deduced back through the output scattered data, and meanwhile, the data security is greatly improved by using random numbers, node numbers, the occupied capacity of each target grandchild node and the like to perform random encryption.
Generating a second character string according to the patient age data, the patient height data, the patient communication data and a second random number, calculating the second character string based on a Hash algorithm to obtain a second Hash value, and taking the second Hash value as a parent node key; according to the technical scheme provided by the invention, the patient age data and the patient height data are multiplied, so that the patient communication data are processed as follows: the mobile phone, the telephone and the like add the communication data of the patient, multiply the random number and the height correspondingly and respectively to obtain a random number according to the age, then carry out encryption processing by utilizing the hash function, and improve the safety of the data by utilizing the irreversible encryption of the hash function.
A first child node key and a parent node key are generated by the following formulas,
Figure 833611DEST_PATH_IMAGE052
wherein the content of the first and second substances,
Figure 159550DEST_PATH_IMAGE002
is a first sub-node key that is,
Figure 322678DEST_PATH_IMAGE003
is a key of a parent node, and is,
Figure 692348DEST_PATH_IMAGE004
in order to be a function of the hash function,
Figure 206506DEST_PATH_IMAGE005
the number of nodes that are the target grandchild node,
Figure 70557DEST_PATH_IMAGE006
in order to address the initial moment of the next inquiry,
Figure 88191DEST_PATH_IMAGE007
is a first random number that is a random number,
Figure 392877DEST_PATH_IMAGE008
is as follows
Figure 128752DEST_PATH_IMAGE009
The capacity of the target grandchild node is occupied,
Figure 796494DEST_PATH_IMAGE010
is the data of the age of the patient,
Figure 668635DEST_PATH_IMAGE011
is the height data of the patient and is the height data of the patient,
Figure 645687DEST_PATH_IMAGE012
an upper limit value for the patient communication data,
Figure 868858DEST_PATH_IMAGE013
is as follows
Figure 74712DEST_PATH_IMAGE014
A value corresponding to the communication data of the patient, wherein the communication data can be a mobile phone number or a telephone number without limitation,
Figure 66938DEST_PATH_IMAGE053
is a second random number that is a function of,
Figure 716357DEST_PATH_IMAGE054
the capacity is occupied for all the target grandchild nodes,
Figure 161244DEST_PATH_IMAGE055
a numerical sum of the patient communication data, such as: 12345678912 if the patient communication data is the patient mobile phone number, corresponding to
Figure 170789DEST_PATH_IMAGE056
Is 48.
According to the technical scheme provided by the invention, the parent node and the child node are encrypted by utilizing the hash function, and other nodes do not provide password locking, so that a doctor can only look up the content of the case type relevant to the inquiry, and the privacy data of a patient is protected, for example: the patient has had the disease history of haemorrhoids, feels extremely private and just do not demonstrate to falling down the fracture state of an illness and do not help, has guaranteed that patient's privacy case data can not be looked into, has also promoted the security of case data simultaneously.
In another possible implementation manner of the technical solution provided by the present invention, step S110 specifically includes:
determining all child nodes by the patient based on the input data of the patient, and acquiring all case data at all grandchild nodes connected with all child nodes in the case decision tree; according to the technical scheme provided by the invention, all child nodes are determined by a patient end based on input data of the patient, and all case data at grandchild nodes connected with all child nodes in a case decision tree are obtained, such as: the patient has hyperglycemia, hypertension and hyperlipidemia, and correspondingly obtains the case data of three child nodes of hyperglycemia, hypertension and hyperlipidemia and all grandchild nodes connected under hyperglycemia, hypertension and hyperlipidemia, for example, a consultation experience is provided at the grandchild node under hyperglycemia: 3/2022, 25/15: 00, carrying out detection once, wherein the blood sugar detection value is 7.1 millimole/liter, and a consultation experience exists at a grandchild node below a hypertension node: 3/2022, 25/25 13:00, performing one-time detection, wherein the blood pressure detection value is systolic pressure of 120, and the blood pressure detection value is diastolic pressure of 80; the grandchild node under the hyperlipemia node has an inquiry experience: 26/2022 15: 00, carrying out one-time detection, wherein the blood fat detection value is 6.2 mmol/L.
Counting the number of nodes of all grandchild nodes corresponding to all acquired child nodes, wherein all grandchild nodes occupy the capacity, the initial time of next inquiry and the first random number to generate a third character string, calculating the third character string based on a hash algorithm to obtain a third hash value, and taking the third hash value as a second child node key, wherein the second child node key is the key of all child nodes; according to the technical scheme provided by the invention, the node numbers of the grandchild nodes under all child nodes are obtained, and it can be understood that the assumption that a patient only sees one disease type every time the patient goes to a hospital is made, and the total times of seeing a disease for the patient corresponding to the number of the grandchild nodes are three inquiry experiences under hyperglycemia, hypertension and hyperlipidemia; obtaining the inquiry experience at the grandchild node: year 2022, 3 month 25 day 15: 00, carrying out detection once, wherein the blood sugar detection value is 7.1mmol/L, and the grandchild node under the hypertension node has an inquiry experience: 3/2022, 25/25 13:00, carrying out primary detection, wherein the blood pressure detection value is systolic pressure of 120, and the blood pressure detection value is diastolic pressure of 80; the grandchild node under the hyperlipemia node has an inquiry experience: 26/3/2022 15: 00, carrying out one-time detection, wherein the blood fat detection value occupies the capacity of 6.2mmol/L, and the corresponding: 3/2022, 25/15: 00. 3/2022, 25/25 13: 00. the method comprises the following steps 26/3/2022 15: 00, combining a random number at the initial moment to form a dynamic value, and performing full encryption by using a hash function, wherein all the child nodes correspond to the same secret key.
The second child node key is generated by the following formula,
Figure 283101DEST_PATH_IMAGE016
wherein the content of the first and second substances,
Figure 336377DEST_PATH_IMAGE017
is the key of the second child node and,
Figure 534140DEST_PATH_IMAGE018
as the number of nodes of all the grandchild nodes,
Figure 81796DEST_PATH_IMAGE006
in order to address the initial moment of the next inquiry,
Figure 48615DEST_PATH_IMAGE004
in order to be a function of the hash function,
Figure 289103DEST_PATH_IMAGE019
is as follows
Figure 450527DEST_PATH_IMAGE020
The capacity is occupied by the grandchild node of each patient,
Figure 801874DEST_PATH_IMAGE007
is a first random number that is a random number,
Figure 623199DEST_PATH_IMAGE057
and occupying capacity for all the grandchild nodes.
The technical scheme provided by the invention provides another scene, and a plurality of cases are related in reality, for example, hyperglycemia, if the blood sugar is not well controlled, the metabolism is abnormal, cardiovascular diseases are likely to occur, and finally cardiovascular diseases are caused, for example, coronary heart disease is common; cataract: if the blood sugar of the diabetic is not well controlled, the crystal in the eye can be diseased, and the longer the disease is, the turbid crystal can be caused, so that the vision is finally reduced, and the blindness can be caused in serious cases; the difference from another scheme is that the present scheme can perform information sharing of multiple child nodes, even all of them, and a physician can view case data at a grandchild node under multiple child nodes according to his own will, because different from another scheme, in the present scheme, all child nodes generate a child node key, it can be understood that the keys of all child nodes are the same, for example: the node key for hyperglycemia is: 111, the node key for the fracture is: 111, the node key of hypertension is: 111, therefore, all case data of the patient are displayed, and the keys of all the child nodes are a key, so that the doctor can conveniently look up the case data.
And step S120, sending the case decision tree, the parent node key and the child node key which are preset at the patient side to the doctor side based on the sending request.
According to the technical scheme provided by the invention, a patient sends a sending request through a patient end, and a case decision tree, a parent node key and a child node key which are preset at the patient end are sent to a doctor end based on the sending request. The doctor end can receive the whole decision tree, but all the nodes are in a locked state, and meanwhile, the doctor end can conveniently operate the doctor end by a follow-up doctor to unlock the data displayed by the patient by receiving the parent node key and the child node key.
Step S130, the physician side decrypts the case decision tree for the first time based on the parent node key to obtain the patient attribute data corresponding to the parent node in the case decision tree, and decrypts the case decision tree for the second time based on the child node key to obtain the target case data corresponding to the child node in the case decision tree.
According to the technical scheme provided by the invention, a doctor receives a decision tree, a parent node key and a child node key of a patient end, decrypts the case decision tree once by using the parent node key, and obtains patient attribute data corresponding to the parent node in the case decision tree after decryption, such as: patient name, height, weight; and carrying out secondary decryption on the corresponding child nodes in the case decision tree through the child node keys to obtain case data after decryption, protecting the privacy of patients, realizing the sharing of personal case data and facilitating reference of doctors. It can be understood that the patient obtains hemorrhoids, but the department of inquiry is ophthalmology, and the corresponding patient only needs to show the data of the ophthalmology child node, and only transmits the key of the ophthalmology child node, so that the data sharing is realized, the individual privacy is protected, and the data security is improved.
Step S140, the physician side updates the target case data based on the case adding data added by the physician to obtain updated case data, updates the case decision tree according to the updated case data to obtain an updated case decision tree, and sends the updated case decision tree to the patient side.
According to the technical scheme provided by the invention, a doctor updates target case data under a child node based on newly-added case data added by the doctor, correspondingly updates to form a new grandchild node, adds the new grandchild node under the child node, correspondingly grows to form a new case decision tree, the doctor sends the updated case decision tree to a patient, replaces the case decision tree before updating in the patient with the new case decision tree, and the continuous updating of the decision tree at the patient realizes the growth of the decision tree, so that the case data of the patient can be more accurately reflected, meanwhile, the key of the corresponding child node also dynamically changes, and the safety of the case data of the patient is improved.
In a possible implementation manner, the technical solution provided by the present invention further includes, as shown in fig. 3, initializing a case decision tree by the following steps, specifically including:
step S210, a patient end receives historical case data input by a user, and sequentially extracts patient attribute data, case type data and case data corresponding to each case type data in the historical case data; according to the technical scheme provided by the invention, a patient end receives historical case data input by a user, wherein the case data comprises case types, inquiry time and case data contents, such as: obtaining the inquiry experience at the grandchild node: 3/2022, 25/15: 00, carrying out one-time detection, wherein the blood sugar detection value is 7.1mmol/L, the corresponding case type is hyperglycemia, the inquiry time is 3 months, 25 days, 15 days in 2022 years: 00, case data content 3, month, 25, day 15 in 2022: 00, carrying out one-time detection, wherein the blood sugar detection value is 7.1mmol/L, the memory occupied by the corresponding case data content is 480B, and dividing historical case data into patient attribute data, case category data and case data corresponding to each case category data to prepare for subsequently establishing a case decision tree.
And S220, constructing parent nodes of the case decision tree according to the patient attribute data. According to the technical scheme provided by the invention, a parent node of a decision tree is constructed according to information such as patient attribute data, for example, height data 175cm of a patient, age data 27 year of the patient, communication data (mobile phone number) 12345678912 of the patient and the like, the content corresponding to the parent node for constructing the decision tree is the height data of the patient, the age data of the patient, the communication data of the patient and the like, and the parent node is constructed to facilitate the generation of a case type child node corresponding to each person in subsequent corresponding generation.
And step S230, constructing a corresponding number of decision tree child nodes according to the case category data, wherein each decision tree child node is connected with the decision tree parent node. According to the technical scheme provided by the invention, according to the case type data of the patient, for example: the case type data of hypertension, hyperglycemia, hyperlipidemia, fracture and the like form case tree child nodes, it can be understood that 4 cases correspondingly form 4 child nodes, N cases correspondingly form N child nodes, the child nodes are connected with the parent nodes, and the rear of the child nodes is constructed so that the grandchild nodes under the child nodes can be constructed later.
Step S240, building decision tree grandchild nodes of a corresponding number according to the case data corresponding to each case category data, where each decision tree grandchild node is connected to a corresponding decision tree child node. According to the technical scheme provided by the invention, a corresponding number of decision tree grandchild nodes are constructed according to the case data corresponding to each case category data, for example, the case category data only has one hypertension, namely, the patient only has too high blood pressure, the corresponding visit record according to the hypertension case category data corresponds, for example, 2 visits are made, and the grandchild nodes under the hypertension node have secondary inquiry experience: 3/2022, 25/25 13:00, carrying out primary detection, wherein the blood pressure detection value is systolic pressure of 120, and the blood pressure detection value is diastolic pressure of 80; 3/2022, 26/13: 00, when the blood pressure detection value is 130 systolic pressure and 85 diastolic pressure, 2 grandchild nodes are generated correspondingly, and the two grandchild nodes are connected to the child node of the hypertension case type of the child node.
According to the technical scheme provided by the invention, a case decision tree is generated according to the past case data of the patient, a foundation is established for the growth of the follow-up case decision tree, and reference is also made for follow-up doctors to look up by establishing the past case decision tree.
In a possible embodiment, the technical solution provided by the present invention further includes:
the patient end determines a viewing sub-node that needs to be viewed by a physician based on the input data of the patient; according to the technical scheme provided by the invention, the patient determines the viewing sub-node to be viewed by the doctor based on the input data of the patient, for example, the patient inputs the type of the hypertension case, and the doctor wants to view the type of the hypertension case.
Traversing all child nodes in the case decision tree, and if the target child node corresponding to the viewing child node does not exist, generating a target child node corresponding to the viewing child node; according to the technical scheme provided by the invention, based on input data of a patient, for example, a doctor wants to check the type of a hypertension case, and traverses all child nodes in a case decision tree based on the type of the hypertension case, if the child nodes without the type of the hypertension case are found to indicate that the patient does not have past medical history of hypertension before, the patient directly generates child nodes of the type of the hypertension case, and corresponding to the fact that no grandchild node exists under the child nodes of the hypertension, the child nodes can be directly sent to the doctor to add grandchild nodes to the doctor.
Connecting the newly generated target child node as a new added child node with the parent node of the decision tree, and updating the case decision tree; according to the technical scheme provided by the invention, a newly generated target child node, such as the newly generated case type child node of hypertension, is connected with the parent node of the case data of the patient and becomes a new case decision tree after being newly added, and compared with the previous case type child node with hypertension, the new case decision tree has one more child node, is updated, and the whole decision tree is conveniently sent to a doctor end in the follow-up process.
The invention provides a technical scheme for configuring an initial key for a newly added child node, wherein the key of the newly added child node each time is the initial key, and the initial key can be: 111, 123, the number and the mode of setting the initial key are not limited, for example, a fixed key may be set by the patient as the initial key.
According to the technical scheme provided by the invention, a patient establishes a new case type sub-node by himself through a patient side; if there is no corresponding case type under the case decision tree, that is, there is no corresponding child node, the patient adds the child node of the case data decision tree through the patient end, it can be understood that the patient never has hypertension before, and the blood pressure is found to be high by measuring at home on a certain day, so the patient himself adds the child node in the hospital, so that the case decision tree grows dynamically instead of being fixed, the autonomy of the patient is increased, the patient can carry out follow-up physical examination observation by the newly built node and add data through the doctor.
In another possible implementation manner, the technical solution provided by the present invention further includes:
after receiving a case decision tree sent by a patient end, a doctor end generates a new child node of a corresponding case type based on case type data input by the doctor; according to the technical scheme provided by the invention, after the doctor end receives the case decision tree sent by the patient end, when the data of the doctor end is searched and increased by the doctor, for example, the doctor searches the child nodes of the hypertension case type and does not search the child nodes, the doctor directly inputs the child nodes of the hypertension case type to newly add the child nodes of the case decision tree.
Generating an additional grandchild node corresponding to the additional child node according to case data input by a doctor end to obtain an updated case decision tree of the doctor end; according to the technical scheme provided by the invention, according to case data input by a doctor end, for example: 3/2022, 25/25 13:00, carrying out primary detection, wherein the blood pressure detection value is systolic pressure of 120, the blood pressure detection value is diastolic pressure of 80, the content of the lower grandchild node of the child node corresponding to the type of the hypertension case is generated, the doctor directly adds the grandchild node to complete the updating of the decision tree, the doctor carries out the updating of the decision tree, and the corresponding case decision tree is more accurate.
After receiving the case decision tree updated by the doctor, the patient extracts case data corresponding to the new child node to generate a new child node key if judging that the new child node exists in the case decision tree. According to the technical scheme provided by the invention, after the patient end receives the case decision tree updated by the doctor end,
the technical scheme provided by the invention provides another scheme for adding the child nodes of the case by the doctor in another scene, if the case decision tree has no corresponding case type, namely has no corresponding child node, the doctor adds the child nodes of the case data decision tree through the doctor end, and can understand that the patient never has too high blood sugar before, but finds the high blood sugar after the hospital detects the high blood sugar, so that the doctor adds the child nodes of the case type, the case decision tree is in an increased state, and the doctor adds the child node data more accurately according to the diagnosis result.
In a possible embodiment, the technical solution provided by the present invention further includes:
initializing the pre-allocation capacity of each child node in the decision tree according to the initial number of the child nodes in the decision tree; according to the technical scheme provided by the invention, before memory allocation is carried out on a new child node, for example, a patient sees 2 different disease types before, such as: hypertension and fracture are 10 GB in total, and 5GB are respectively distributed to 2 nodes.
After judging the newly added child nodes, acquiring the pre-distribution capacity of the child nodes of each child node in the case decision tree and the current occupied capacity of each child node to obtain the residual capacity of each child node, and obtaining the residual total capacity according to the residual capacities of all the child nodes; according to the technical scheme provided by the invention, the capacity of the newly-added child node and the capacity of the newly-added child node can be conveniently distributed in the follow-up process by acquiring the residual total capacity. According to the technical scheme provided by the invention, after the new adding child node is judged to be generated, the child node pre-allocation capacity of each child node in the case decision tree is acquired, for example, 10 GB and 2 child nodes correspond to two points, the pre-allocation capacity is 5GB, the current occupied capacity of the first node for hypertension is assumed to be 2GB, the current occupied capacity of the second node for fracture is assumed to be 1GB, and the corresponding residual total capacity is 7 GB.
Acquiring case category data corresponding to each child node, and determining a first distribution weight corresponding to each child node according to the case category data; the technical scheme provided by the invention quantizes the case type data, respectively quantizes the weight of each case type, and judges the subsequent memory capacity needing to be increased according to the quantized weight, for example: since hypertension and the like need to be detected frequently, the data is updated frequently with a corresponding weight of 100, for example: the probability that a fracture due to a fall is an accidental recurrence is not high, and the corresponding weight may be 10.
Acquiring case data corresponding to all grandchild nodes of each child node, determining the last inquiry time closest to the current time in the case data, and determining the second distribution weight corresponding to each child node according to the last inquiry time; according to the technical scheme provided by the invention, the time closest to the current time in each case data is checked as the second weight, so that the occurrence duration of the case is measured. For example: today's visit times are 3 months, 27 days 1: 50, there are 2 cases under the corresponding sub-nodes of hypertension, which are 3 months, 22 days and 2 days: 50 and 3 months, 26 days 3: 50, 1 case data under the child nodes of the fracture is 1 month, 22 days and 2 days: 50, it will be appreciated that the fracture cases are separated by too long a period of time, and the correspondence also represents a case that is a small probability event or has recovered without further treatment, and the second weight ratio for hypertension is greater than the fracture, which may be, for example, 200 for hypertension and 10 for fracture.
Determining the newly-added capacity of a newly-added child node based on the first distribution weight, the second distribution weight, the node number of the grandchild node and the remaining total capacity;
in a possible implementation manner, in the step of determining the newly added capacity of the newly added child node based on the first allocation weight, the second allocation weight, the node number of the grandchild node, and the remaining total capacity, the technical solution provided by the present invention specifically includes:
the newly added capacity is calculated by the following formula,
Figure 283857DEST_PATH_IMAGE021
wherein the content of the first and second substances,
Figure 190633DEST_PATH_IMAGE022
in order to newly increase the capacity of the child node,
Figure 80091DEST_PATH_IMAGE023
the first assigned weight of the child node is newly added,
Figure 21502DEST_PATH_IMAGE024
a second assigned weight for the newly added child node,
Figure 88947DEST_PATH_IMAGE025
for an upper bound on the number of nodes of the case decision tree child node,
Figure 483019DEST_PATH_IMAGE026
is the number of nodes of the child node,
Figure 176168DEST_PATH_IMAGE027
is as follows
Figure 972086DEST_PATH_IMAGE028
The number of nodes of the grandchild node to which the child node is connected,
Figure 974546DEST_PATH_IMAGE023
is as follows
Figure 855914DEST_PATH_IMAGE028
A first assigned weight for a child node,
Figure 352755DEST_PATH_IMAGE024
is as follows
Figure 3179DEST_PATH_IMAGE028
The second assignment of weights to the child nodes,
Figure 927273DEST_PATH_IMAGE029
the number of nodes that are standard grandchild nodes,
Figure 43740DEST_PATH_IMAGE030
is as follows
Figure 78692DEST_PATH_IMAGE028
Sub-nodeThe sub-node of (a) pre-allocates capacity,
Figure 583623DEST_PATH_IMAGE031
is as follows
Figure 678618DEST_PATH_IMAGE028
The current occupied capacity of the child node(s),
Figure 783846DEST_PATH_IMAGE058
in order to have a remaining total capacity,
Figure 356910DEST_PATH_IMAGE033
in order to newly increase the capacity adjustment value,
Figure 981926DEST_PATH_IMAGE059
the proportion value of the new added child node to all child nodes is obtained, wherein,
Figure 247822DEST_PATH_IMAGE060
and
Figure 76232DEST_PATH_IMAGE022
in a direct proportion to the total weight of the composition,
Figure 718566DEST_PATH_IMAGE061
and
Figure 198089DEST_PATH_IMAGE022
inversely, it can be understood that the capacity to be allocated can be obtained by multiplying the weight ratio of the child node by the remaining capacity, and meanwhile, other child nodes can also be calculated by using the formula, for example: calculating the allocated capacity of the fracture sub-node, calculating the allocated capacity of the hypertensive sub-node, and so on, again without limitation, wherein the first allocated weight of the new sub-node is added
Figure 634886DEST_PATH_IMAGE023
Quantitatively determined according to case category data, and second distribution weight of new child node
Figure 449127DEST_PATH_IMAGE024
Can be artificially set and quantitatively determined according to the last inquiry time closest to the current time of historical case data
Figure 363994DEST_PATH_IMAGE028
First assigned weight of child node
Figure 963602DEST_PATH_IMAGE023
Is quantitatively determined according to case category data
Figure 571301DEST_PATH_IMAGE028
Second distribution weight of child nodes
Figure 377233DEST_PATH_IMAGE024
Is quantitatively determined according to the last interrogation moment closest to the current moment.
According to the technical scheme provided by the invention, the weighted values are obtained by quantifying the type and duration of the case, the capacities of all the child nodes are respectively distributed, and the capacity partition is very quick when the data is called correspondingly. It can be understood that the distribution volume corresponding to common cases such as hypertension, diabetes, cold, etc. is large, and the distribution volume corresponding to unusual diseases such as fracture, etc. is small.
The technical solution provided by the present invention, in a possible implementation manner, further includes
Generating an inquiry frequency of each child node according to the number of nodes of grandchild nodes connected with each child node of the case decision tree, the inquiry initiation time corresponding to the initial grandchild node connected with each child node and the inquiry initiation corresponding to the final grandchild node connected with each child node; according to the technical scheme provided by the invention, the node of the grandchild node, for example, the node of the grandchild node under the hypertension node is utilized, for example, the hypertension has 2 inquiries, which correspond to 3 months and 25 days in 2022 and 7 days in 25: 00, carrying out primary detection, wherein the blood pressure detection value is systolic pressure of 120, and the blood pressure detection value is diastolic pressure of 80; 3/2022, 25/25 13:00, carrying out primary detection, wherein the blood pressure detection value is systolic pressure of 120, and the blood pressure detection value is diastolic pressure of 85; the corresponding initial grandchild node is the earliest node sorted from morning to evening, the terminal grandchild node is the latest node sorted from morning to evening, the corresponding time period is 7 × 3600-13 × 3600=21600, and the time period is divided by the number of times to obtain the interval time of each inquiry, namely the inquiry frequency, wherein if only one grandchild node exists, the time period is set as a default value, and the default value can be 100 or 1000.
Generating a child node portrait according to the inquiry frequency of each child node and the number of child nodes of the case decision tree; according to the technical scheme provided by the invention, the sub-node portrait is generated through the inquiry frequency of each sub-node and the number of the sub-nodes, and the inquiry frequency and the number of the sub-nodes reflect the current health condition of a patient.
Generating a decision tree portrait according to the child node portrait and the frequency weight of the child node; the image value is adjusted by the frequency weight of the sub-node and combined with the decision tree image reference value, the condition of the case data of the patient is reflected, the health degree of the patient is correspondingly reflected, the decision tree image reference value is an average value of daily health case data, the frequency weight of the sub-node can be set manually and correspondingly and is mainly determined according to the inquiry frequency of a certain case of the patient, the higher the inquiry frequency is, the larger the frequency weight value of the corresponding sub-node is, the lower the inquiry frequency is, the smaller the frequency weight value of the corresponding sub-node is, for example: the data cases of the patient under the orthopedic sub-nodes are 4 times, the frequency of the orthopedic inquiry is obtained by dividing the application time by the times, and the frequency weight value of the sub-nodes is correspondingly set according to the frequency of the orthopedic inquiry.
Obtaining a first integrated inspection time period according to the decision tree image; according to the technical scheme provided by the invention, by adjusting the average degree of health, if the number of child nodes is too large, the disease is more, the corresponding constitution is worse, the corresponding user portrait is larger, the time interval is smaller, the too small value is adjusted through the adjustment value of the physical examination time period, for example, the value is smaller than 1, and a minimum standard physical examination time period is set, such as: one physical examination per week, or one physical examination every 3 days. Meanwhile, the inquiry frequency is that the inquiry time interval is larger, the corresponding image value is smaller, the inquiry time interval is larger, the good health state is realized without carrying out a plurality of physical examinations, and the corresponding first physical examination time period is larger.
The decision tree portrayal and the first volume examination time period are calculated by the following formula,
Figure 95790DEST_PATH_IMAGE034
wherein the content of the first and second substances,
Figure 549905DEST_PATH_IMAGE035
in order to make a decision tree representation,
Figure 328506DEST_PATH_IMAGE036
is as follows
Figure 117339DEST_PATH_IMAGE038
When the initial inquiry time corresponding to the initial grandchild node connected with the child node is up,
Figure 639587DEST_PATH_IMAGE037
is as follows
Figure 948209DEST_PATH_IMAGE038
When the initial inquiry moment of the current inquiry is corresponding to the terminal grandchild node connected with the child node,
Figure 897710DEST_PATH_IMAGE039
is as follows
Figure 409725DEST_PATH_IMAGE038
The number of nodes of the grandchild node to which the child node is connected,
Figure 735664DEST_PATH_IMAGE040
is as follows
Figure 240071DEST_PATH_IMAGE038
The frequency weight of the child node(s),
Figure 314468DEST_PATH_IMAGE041
portraying a decision treeThe value of the adjustment is adjusted,
Figure 546735DEST_PATH_IMAGE062
for the reference value of the decision tree image,
Figure 145207DEST_PATH_IMAGE043
is a first examination time period of the body examination,
Figure 897262DEST_PATH_IMAGE063
the value is adjusted for the physical examination period,
Figure 953947DEST_PATH_IMAGE045
for an upper bound on the number of nodes of the case decision tree child node,
Figure 893084DEST_PATH_IMAGE046
is the number of nodes of the child node, 365 is the time corresponding to one year,
Figure 544514DEST_PATH_IMAGE064
time intervals for the interrogation, for example: the hypertension sub-nodes are correspondingly provided with 2 grandchild nodes, the 1 st inquiry initial time is 7:00 at 1 month and 10 days 2022, the 2 nd inquiry initial time is 13:00 at 1 month and 10 days 2022, correspondingly
Figure 416655DEST_PATH_IMAGE037
Is 7 × 3600=25200, corresponding to
Figure 144440DEST_PATH_IMAGE036
Is 13 x 3600=46800,
Figure 321606DEST_PATH_IMAGE065
for the frequency of the interrogation of each sub-node,
Figure 793038DEST_PATH_IMAGE046
and
Figure 785265DEST_PATH_IMAGE035
in a direct proportion to the total weight of the composition,
Figure 418372DEST_PATH_IMAGE035
and
Figure 378106DEST_PATH_IMAGE043
in inverse proportion, it can be understood that the larger the number of child nodes, the shorter the time interval corresponding to physical examination,
Figure 387650DEST_PATH_IMAGE066
and
Figure 234384DEST_PATH_IMAGE035
in the inverse proportion,
Figure 303971DEST_PATH_IMAGE035
and
Figure 983958DEST_PATH_IMAGE043
inversely proportional, it can be understood that the larger the interval of inquiry, the larger the corresponding interval of physical examination, wherein the decision tree representation adjustment value
Figure 797193DEST_PATH_IMAGE041
And adjustment value of physical examination time period
Figure 764012DEST_PATH_IMAGE067
The adjustment value of the corresponding decision tree portrait and the adjustment value of the physical examination time period can be set manually and adjusted and determined according to the physical change process of the patient. Wherein the adjustment value of the physical examination time period
Figure 738921DEST_PATH_IMAGE068
The setting can be done by human, for example: because the patient physique is relatively poor, the actual output time interval is too short, the patient can not go to the physical examination according to the output frequency, and the adjustment value of the physical examination time period can be manually adjusted to meet the actual requirement.
According to the technical scheme provided by the invention, a physical examination time period is automatically generated according to the number of types of cases and the frequency of inquiry of the patient and is used for reminding the patient how often the patient should go to physical examination or inquiry, and the function of automatically reminding the physical examination is realized by processing case data.
The technical solution provided by the present invention, in a possible implementation manner, further includes
A second session is acquired during the actual physical examination of the patient. Generally, the physical examination of the patient is regular, the second physical examination time period may be the actual physical examination time period of the patient, the shorter the second physical examination time period is, the higher the actual physical examination frequency of the patient is proved, and the longer the second physical examination time period is, the lower the actual physical examination frequency of the patient is proved.
Obtaining a time adjustment trend according to the first volume inspection time period and the second volume inspection time period; the system obtains an adjustment trend value with time increasing or decreasing according to the actual physical examination time period (second physical examination time) of the patient and the first physical examination time period automatically output by the system.
Correcting the adjustment value of the physical examination time period according to the time adjustment trend to obtain a corrected adjustment value of the physical examination time period;
the corrected adjustment value of the physical examination time period is obtained by the following formula,
Figure 407669DEST_PATH_IMAGE047
wherein the content of the first and second substances,
Figure 759016DEST_PATH_IMAGE048
for the second examination period of time,
Figure 580341DEST_PATH_IMAGE049
the adjusted value is the adjusted value of the physical examination time period after the correction,
Figure 476884DEST_PATH_IMAGE050
the trend correction value is increased for the physical examination period,
Figure 649239DEST_PATH_IMAGE051
the trend correction value is reduced for the physical examination period,
Figure 804277DEST_PATH_IMAGE069
is a secondThe absolute value of the difference between the physical examination period and the first physical examination period,
Figure 214530DEST_PATH_IMAGE070
is the difference between the second volume detection time period and the first volume detection time period. The technical scheme provided by the invention is divided into 2 cases, namely, the actual physical examination time period is greater than the time period output by the system, the difference value of the corresponding physical examination time period is positive and the adjustment value of the physical examination time period is correspondingly increased; the other situation is that the actual physical examination time period is smaller than the time period output by the system, at the moment, if the difference value corresponding to the physical examination time period is a negative number, an absolute value is correspondingly taken, and the adjustment value of the physical examination time period is correspondingly reduced, wherein the increase trend correction value of the physical examination time period is
Figure 46089DEST_PATH_IMAGE050
Can be a range set by people according to
Figure DEST_PATH_IMAGE071
The error is adjusted by the difference value of the first physical examination time period, and the correction value of the increasing trend of the physical examination time period
Figure 909002DEST_PATH_IMAGE051
Can be a range set by people according to
Figure 602152DEST_PATH_IMAGE072
The absolute value of the difference between the second volume inspection time period and the first volume inspection time period is adjusted.
According to the technical scheme provided by the invention, the automatically generated physical examination time period is corrected according to the physical examination order value, the actual physical examination time period and the actual time period of the patient after the patient actually goes to physical examination, so that the actual condition of the patient is better met, and the data is more accurate. The invention has the advantages that the autonomous learning and adjusting process is realized, the system output is more in line with the actual situation, and the output result is more accurate and practical.
In order to implement the medical system case data processing method provided by the present invention, the present invention further provides a medical system case data processing system, wherein a patient transmits case data to a physician end of a physician based on a patient end, as shown in fig. 4, the method specifically includes:
the generation module is used for determining target case data of a target child node in a case decision tree by a patient based on input data of the patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient;
the transmitting module is used for transmitting a case decision tree, a parent node key and a child node key which are preset at a patient end to a doctor end based on a transmitting request;
the decryption module is used for decrypting the case decision tree for the first time by a doctor end based on the parent node key to obtain patient attribute data corresponding to the parent node in the case decision tree, and decrypting the case decision tree for the second time based on the child node key to obtain target case data corresponding to the child node in the case decision tree;
and the updating module is used for updating the target case data to obtain updated case data by the doctor end based on the case adding data added by the doctor, updating the case decision tree to obtain an updated case decision tree according to the updated case data, and sending the updated case decision tree to the patient end by the doctor end.
Referring to fig. 5, which is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention, the electronic device 50 includes: a processor 51, a memory 52 and computer programs; wherein
A memory 52 for storing the computer program, which may also be a flash memory (flash). The computer program is, for example, an application program, a functional module, or the like that implements the above method.
A processor 51 for executing the computer program stored in the memory to implement the steps performed by the apparatus in the above method. Reference may be made in particular to the description relating to the preceding method embodiment.
Alternatively, the memory 52 may be separate or integrated with the processor 51.
When the memory 52 is a device independent of the processor 51, the apparatus may further include:
a bus 53 for connecting the memory 52 and the processor 51.
The present invention also provides a readable storage medium, in which a computer program is stored, which, when being executed by a processor, is adapted to implement the methods provided by the various embodiments described above.
The present invention also provides a readable storage medium, in which a computer program is stored, which, when being executed by a processor, is adapted to implement the methods provided by the various embodiments described above.
The readable storage medium may be a computer storage medium or a communication medium. Communication media includes any medium that facilitates transfer of a computer program from one place to another. Computer storage media may be any available media that can be accessed by a general purpose or special purpose computer. For example, a readable storage medium is coupled to the processor such that the processor can read information from, and write information to, the readable storage medium. Of course, the readable storage medium may also be an integral part of the processor. The processor and the readable storage medium may reside in an Application Specific Integrated Circuits (ASIC). Additionally, the ASIC may reside in user equipment. Of course, the processor and the readable storage medium may also reside as discrete components in a communication device. The readable storage medium may be read-only memory (ROM), random-access memory (RAM), CD-ROMs, magnetic tapes, floppy disks, optical data storage devices, and the like.
The present invention also provides a program product comprising execution instructions stored in a readable storage medium. The at least one processor of the device may read the execution instructions from the readable storage medium, and the execution of the execution instructions by the at least one processor causes the device to implement the methods provided by the various embodiments described above.
In the above embodiments of the apparatus, it should be understood that the Processor may be a Central Processing Unit (CPU), other general purpose processors, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like. The steps of a method disclosed in connection with the present invention may be embodied directly in a hardware processor, or in a combination of the hardware and software modules within the processor.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A case data processing method for a medical system is characterized in that a patient transmits case data to a doctor end of a doctor based on a patient end, and the method specifically comprises the following steps:
the patient end determines target case data of a target child node in a case decision tree based on input data of a patient, generates a child node key of the target child node according to the target case data, and generates a parent node key of the case decision tree according to patient attribute data of the patient;
sending a case decision tree, a parent node key and a child node key which are preset at a patient end to a doctor end based on the sending request;
the physician side decrypts the case decision tree for the first time based on the parent node key to obtain the patient attribute data corresponding to the parent node in the case decision tree, and decrypts the case decision tree for the second time based on the child node key to obtain the target case data corresponding to the child node in the case decision tree;
and the physician end updates the target case data based on the case adding data added by the physician to obtain updated case data, updates the case decision tree according to the updated case data to obtain an updated case decision tree, and sends the updated case decision tree to the patient end.
2. The method of claim 1,
initializing a case decision tree by the following steps, specifically comprising:
the patient end receives historical case data input by a user, and sequentially extracts patient attribute data, case category data and case data corresponding to each case category data in the historical case data;
constructing parent nodes of a case decision tree according to the patient attribute data;
constructing a corresponding number of decision tree child nodes according to the case category data, wherein each decision tree child node is connected with the decision tree parent node;
and constructing decision tree grandchild nodes in a corresponding number according to the case data corresponding to each case category data, wherein each decision tree grandchild node is connected with a corresponding decision tree child node.
3. The method of claim 1,
the steps of determining target case data of a target child node in a case decision tree based on input data of a patient from a patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient specifically include:
generating a target child node by a patient based on input data of the patient, and acquiring target case data at a target grandchild node connected with the target child node in a case decision tree;
counting the number of the acquired target grandchild nodes, the occupied capacity of each target grandchild node, the current inquiry initial time and a first random number to generate a first character string, calculating the first character string based on a Hash algorithm to obtain a first Hash value, and taking the first Hash value as a first child node key, wherein the first child node key is the key of the target child node;
generating a second character string according to the patient age data, the patient height data, the patient communication data and a second random number, calculating the second character string based on a hash algorithm to obtain a second hash value, and taking the second hash value as a parent node key;
the first child node key and the parent node key are generated by the following formulas,
Figure 858746DEST_PATH_IMAGE001
wherein the content of the first and second substances,
Figure 838203DEST_PATH_IMAGE002
is a first sub-node key that is,
Figure 617940DEST_PATH_IMAGE003
is a key of the parent node and is,
Figure 576931DEST_PATH_IMAGE004
in order to be a function of the hash function,
Figure 391303DEST_PATH_IMAGE005
the number of nodes that are the target grandchild node,
Figure 213766DEST_PATH_IMAGE006
in order to address the initial moment of the next inquiry,
Figure 543116DEST_PATH_IMAGE007
is a first random number that is a random number,
Figure 742016DEST_PATH_IMAGE008
is as follows
Figure 473212DEST_PATH_IMAGE009
The capacity of the target grandchild node is occupied,
Figure 466576DEST_PATH_IMAGE010
is the data of the age of the patient,
Figure 220905DEST_PATH_IMAGE011
is the height data of the patient and is the height data of the patient,
Figure 20234DEST_PATH_IMAGE012
an upper limit value for the patient communication data,
Figure 543619DEST_PATH_IMAGE013
is as follows
Figure 534315DEST_PATH_IMAGE014
A value corresponding to the patient communication data,
Figure 775941DEST_PATH_IMAGE015
is a second random number.
4. The method of claim 1,
the steps of determining target case data of a target child node in a case decision tree based on input data of a patient from a patient, generating a child node key of the target child node according to the target case data, and generating a parent node key of the case decision tree according to patient attribute data of the patient specifically include:
determining all child nodes by the patient based on the input data of the patient, and acquiring all case data at all grandchild nodes connected with all child nodes in the case decision tree;
counting the number of nodes of all grandchild nodes corresponding to all acquired child nodes, wherein all grandchild nodes occupy the capacity, the initial time of next inquiry and the first random number to generate a third character string, calculating the third character string based on a hash algorithm to obtain a third hash value, and taking the third hash value as a second child node key, wherein the second child node key is the key of all child nodes;
the second child node key is generated by the following formula,
Figure 113381DEST_PATH_IMAGE016
wherein the content of the first and second substances,
Figure 756852DEST_PATH_IMAGE017
is the key of the second child node and,
Figure 154336DEST_PATH_IMAGE018
as the number of nodes of all the grandchild nodes,
Figure 883257DEST_PATH_IMAGE006
in order to address the initial moment of the next inquiry,
Figure 962072DEST_PATH_IMAGE004
in order to be a function of the hash function,
Figure 522366DEST_PATH_IMAGE007
is a first random number that is a random number,
Figure 294013DEST_PATH_IMAGE019
is as follows
Figure 808433DEST_PATH_IMAGE020
The grandchild node of each patient occupies capacity.
5. The method of claim 2, further comprising:
the patient end determines a viewing sub-node that needs to be viewed by a physician based on the input data of the patient;
traversing all child nodes in the case decision tree, and if the target child node corresponding to the viewing child node does not exist, generating a target child node corresponding to the viewing child node;
connecting the newly generated target child node as a new added child node with the parent node of the decision tree, and updating the case decision tree;
and configuring an initial key for the newly added child node.
6. The method of claim 2, further comprising:
after receiving a case decision tree sent by a patient end, a doctor end generates a new child node of a corresponding case type based on case type data input by the doctor;
generating an additional grandchild node corresponding to the additional child node according to case data input by a doctor end to obtain an updated case decision tree of the doctor end;
after receiving the case decision tree updated by the doctor, the patient extracts case data corresponding to the new child node to generate a new child node key if judging that the new child node exists in the case decision tree.
7. The method of claim 5 or 6, further comprising:
initializing the pre-distribution capacity of each sub-node in the decision tree according to the initial number of the sub-nodes in the decision tree;
after judging the newly added child nodes, acquiring the pre-distribution capacity of the child nodes of each child node in the case decision tree and the current occupied capacity of each child node to obtain the residual capacity of each child node, and obtaining the residual total capacity according to the residual capacities of all the child nodes;
acquiring case category data corresponding to each child node, and determining a first distribution weight corresponding to each child node according to the case category data;
acquiring case data corresponding to all grandchild nodes of each child node, determining the last inquiry time closest to the current time in the case data, and determining the second distribution weight corresponding to each child node according to the last inquiry time;
and determining the newly-added capacity of the newly-added child node based on the first distribution weight, the second distribution weight, the node number of the grandchild node and the remaining total capacity.
8. The method of claim 7,
in the step of determining the newly added capacity of the newly added child node based on the first allocation weight, the second allocation weight, the node number of the grandchild node, and the remaining total capacity, the method specifically includes:
the newly added capacity is calculated by the following formula,
Figure 690938DEST_PATH_IMAGE021
wherein the content of the first and second substances,
Figure 43422DEST_PATH_IMAGE022
in order to newly increase the capacity of the child node,
Figure 48288DEST_PATH_IMAGE023
the first assigned weight of the child node is newly added,
Figure 486222DEST_PATH_IMAGE024
weights are assigned to the second of the newly added child nodes,
Figure 969156DEST_PATH_IMAGE025
for an upper bound on the number of nodes of the case decision tree child node,
Figure 441726DEST_PATH_IMAGE026
is the number of nodes of the child node,
Figure 555175DEST_PATH_IMAGE027
is as follows
Figure 542723DEST_PATH_IMAGE028
The number of nodes of the grandchild node to which the child node is connected,
Figure 767031DEST_PATH_IMAGE023
is as follows
Figure 828528DEST_PATH_IMAGE028
A first assigned weight for a child node,
Figure 673730DEST_PATH_IMAGE024
is as follows
Figure 86257DEST_PATH_IMAGE028
The second assignment of weights to the child nodes,
Figure 176573DEST_PATH_IMAGE029
the number of nodes that are standard grandchild nodes,
Figure 358156DEST_PATH_IMAGE030
is as follows
Figure 547829DEST_PATH_IMAGE028
The child nodes of the child nodes pre-allocate capacity,
Figure 509968DEST_PATH_IMAGE031
is as follows
Figure 76079DEST_PATH_IMAGE028
The current occupied capacity of the child node(s),
Figure 377747DEST_PATH_IMAGE032
in order to have a remaining total capacity,
Figure 800638DEST_PATH_IMAGE033
the value is adjusted for the new increased capacity.
9. The method of claim 1,
generating an inquiry frequency of each child node according to the number of nodes of grandchild nodes connected with each child node of the case decision tree, the inquiry initiation time corresponding to the initial grandchild node connected with each child node and the inquiry initiation corresponding to the final grandchild node connected with each child node;
generating a child node portrait according to the inquiry frequency of each child node and the number of child nodes of the case decision tree;
generating a decision tree portrait according to the child node portrait and the frequency weight of the child node;
obtaining a first integrated inspection time period according to the decision tree image;
the decision tree portrayal and the first volume examination time period are calculated by the following formula,
Figure 187757DEST_PATH_IMAGE034
wherein the content of the first and second substances,
Figure 121341DEST_PATH_IMAGE035
in order to make a decision tree representation,
Figure 759739DEST_PATH_IMAGE036
is as follows
Figure 556794DEST_PATH_IMAGE037
When the initial inquiry time corresponding to the initial grandchild node connected with the child node is up,
Figure 227946DEST_PATH_IMAGE038
is as follows
Figure 401439DEST_PATH_IMAGE037
When the initial inquiry moment of the current inquiry is corresponding to the terminal grandchild node connected with the child node,
Figure 412120DEST_PATH_IMAGE039
is a first
Figure 442393DEST_PATH_IMAGE037
The number of nodes of the grandchild node to which the child node is connected,
Figure 538525DEST_PATH_IMAGE040
is as follows
Figure 312446DEST_PATH_IMAGE037
The frequency weight of the child node(s),
Figure 177634DEST_PATH_IMAGE041
for the decision tree image adjustment value,
Figure 316491DEST_PATH_IMAGE042
for the reference value of the decision tree image,
Figure 463701DEST_PATH_IMAGE043
is a first examination time period of the body examination,
Figure 713417DEST_PATH_IMAGE044
the value is adjusted for the physical examination period,
Figure 761007DEST_PATH_IMAGE045
for an upper bound on the number of nodes of the case decision tree child node,
Figure 70766DEST_PATH_IMAGE046
the number of nodes that are child nodes.
10. The method of claim 9,
acquiring a second physical examination time period when the patient actually performs physical examination;
obtaining a time adjustment trend according to the first volume inspection time period and the second volume inspection time period;
correcting the physical examination time period adjustment value according to the time adjustment trend to obtain a corrected physical examination time period adjustment value;
the corrected adjustment value of the physical examination time period is obtained by the following formula,
Figure 141490DEST_PATH_IMAGE047
wherein the content of the first and second substances,
Figure 257213DEST_PATH_IMAGE048
for the second examination period of time,
Figure 96993DEST_PATH_IMAGE049
the adjusted value is the adjusted value of the physical examination time period after the correction,
Figure 577653DEST_PATH_IMAGE050
the trend correction value is increased for the physical examination period,
Figure 932411DEST_PATH_IMAGE051
the trend correction value is reduced for the physical examination period.
CN202210376560.1A 2022-04-12 2022-04-12 Case data processing method for medical system Active CN114465828B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210376560.1A CN114465828B (en) 2022-04-12 2022-04-12 Case data processing method for medical system

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210376560.1A CN114465828B (en) 2022-04-12 2022-04-12 Case data processing method for medical system

Publications (2)

Publication Number Publication Date
CN114465828A true CN114465828A (en) 2022-05-10
CN114465828B CN114465828B (en) 2022-07-12

Family

ID=81417206

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210376560.1A Active CN114465828B (en) 2022-04-12 2022-04-12 Case data processing method for medical system

Country Status (1)

Country Link
CN (1) CN114465828B (en)

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150213570A1 (en) * 2012-08-15 2015-07-30 Hewlett-Packard Development Company, Lp Metadata tree of a patient with lockboxes
CN106131014A (en) * 2016-07-06 2016-11-16 杨炳 The security system accessed for the case information of medical treatment
CN110634544A (en) * 2019-09-19 2019-12-31 腾讯科技（深圳）有限公司 Medical record data processing method and device based on block chain, storage medium and equipment
CN110752024A (en) * 2019-10-17 2020-02-04 湖北工业大学 Online medical diagnosis service system based on privacy protection
US20210141921A1 (en) * 2018-01-31 2021-05-13 Myprivacy Gmbh Method for Generating Metadata-Free Trees
CN113517065A (en) * 2021-05-31 2021-10-19 湖北工业大学 Cloud-assisted decision tree model diagnosis system and method for protecting medical data privacy
CN113903480A (en) * 2021-11-19 2022-01-07 丁玉 AR-element universe decentralized medical community diagnosis system

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150213570A1 (en) * 2012-08-15 2015-07-30 Hewlett-Packard Development Company, Lp Metadata tree of a patient with lockboxes
CN106131014A (en) * 2016-07-06 2016-11-16 杨炳 The security system accessed for the case information of medical treatment
US20210141921A1 (en) * 2018-01-31 2021-05-13 Myprivacy Gmbh Method for Generating Metadata-Free Trees
CN110634544A (en) * 2019-09-19 2019-12-31 腾讯科技（深圳）有限公司 Medical record data processing method and device based on block chain, storage medium and equipment
CN110752024A (en) * 2019-10-17 2020-02-04 湖北工业大学 Online medical diagnosis service system based on privacy protection
CN113517065A (en) * 2021-05-31 2021-10-19 湖北工业大学 Cloud-assisted decision tree model diagnosis system and method for protecting medical data privacy
CN113903480A (en) * 2021-11-19 2022-01-07 丁玉 AR-element universe decentralized medical community diagnosis system

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
TIAN-FU LEE ET AL.: "《A blockchain-based medical data preservation scheme for telecare medical information systems》", 《INTERNATIONAL JOURNAL OF INFORMATION SECURITY》 *
薛腾飞等: "《基于区块链的医疗数据共享模型研究》", 《自动化学报》 *
陈俊杰等: "无线体域网中隐私保护的远程医疗诊断系统", 《长春师范大学学报》 *

Also Published As

Publication number Publication date
CN114465828B (en) 2022-07-12

Similar Documents

Publication Publication Date Title
Eurlings et al. Telemedicine in heart failure—more than nice to have?
WO2019244949A1 (en) Biological information processing method, biological information processing device, and biological information processing system
CN112863702A (en) Intelligent health service method and display equipment thereof
US20090132282A1 (en) Medical data management
CN113257383A (en) Matching information determination method, display method, device, equipment and storage medium
JP2002032488A (en) Medical information system, terminal device for patient and medium
CN116646046A (en) Electronic medical record processing method and system based on Internet diagnosis and treatment
JP2014203416A (en) Waiting time prediction system
CN114743625B (en) Electronic health record management method, system and computer storage medium
Latifi et al. Telemedicine consultation as an indicator of local telemedicine champions' contributions, health care system needs or both: tales from two continents
CN114465828B (en) Case data processing method for medical system
JP2004030128A (en) Health care information sharing system, health care information sharing method, and health care information sharing program
JP2022544030A (en) Patient-based meal plan recommendation system
CN111190931A (en) Method and device for processing prescription data, electronic equipment and storage medium
KR100474797B1 (en) A cyber hospital system and an operating method thereof
CN115620895A (en) Intelligent diagnosis method, device, equipment and medium
CN112786159A (en) Method, system and storage medium for decentralized management of medical image data based on block chain
JP6654274B1 (en) Examination notebook system, patient terminal, and control method
US8494869B1 (en) Method and system for presenting treatment options
CN113744852A (en) Medical data management system, method, server and client device
US20140172462A1 (en) Method and System for Providing Information to Physicians
CN114898877B (en) Processing method and system suitable for multi-dimensional health data
JP5585134B2 (en) Layout creation program, layout creation apparatus, layout creation system, and layout creation method
JP2001282922A (en) Method and terminal for exchanging patient medical information
JP7396637B2 (en) Oriental medical chart sharing device, oriental medical chart sharing method, program, recording medium

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant